Microsoft SC-200 Microsoft Security Operations Analyst Exam Practice Test

As outlined in Microsoft’s official Defender for Office 365 documentation, this service provides comprehensive protection against threats targeting Microsoft 365 collaboration tools—such as SharePoint Online, OneDrive for Business, and Microsoft Teams. The marketing team uses SharePoint Online for vendor collaboration and has experienced incidents in which vendors uploaded malicious files. Microsoft Defender for Office 365 specifically addresses this scenario through features like Safe Attachments and Safe Links, which automatically scan uploaded or shared files for malware and block access to harmful content.

When a vendor uploads a file to SharePoint Online, Defender for Office 365 inspects the file in real time within a virtual sandbox environment before allowing users to open or share it. If malware is detected, the system quarantines or removes the file and notifies administrators. These detection and remediation capabilities prevent infection propagation, protect sensitive marketing data, and maintain compliance with Contoso’s security posture.

By leveraging Defender for Office 365, Contoso’s marketing team can continue external collaboration safely, ensuring that all uploaded files are scanned and validated before internal access—thereby resolving their specific malware-related issue.

 Table: DeviceFileEvents

 Aggregation function: count()

In Microsoft Defender XDR advanced hunting, data tables such as DeviceFileEvents, DeviceProcessEvents, and CloudAppEvents are used to investigate various types of activities. Since this query aims to investigate an issue related to file activity—specifically identifying when files have been accessed, modified, or created repeatedly—the correct data source table is DeviceFileEvents. This table contains information about file-level activities recorded by Defender for Endpoint sensors, including file path, file name, action type, and user account involved.

The KQL structure shown in the image follows standard hunting query syntax:

DeviceFileEvents

| where Timestamp > ago(2d)

| summarize activityCount = count() by FolderPath, FileName, ActionType, AccountDisplayName

| where activityCount > 5

Here’s why:

The where Timestamp > ago(2d) clause filters results from the last 2 days, a typical timeframe for immediate investigations.

The summarize operator groups events by FolderPath, FileName, ActionType, and AccountDisplayName, then uses count() to determine how many times each file was acted upon.

Finally, where activityCount > 5 filters to show only unusually high-frequency activity, which might indicate suspicious or automated file manipulation.

Microsoft Defender XDR documentation highlights that DeviceFileEvents is the correct schema for file activity investigations, while DeviceProcessEvents focuses on process creation and execution, and CloudAppEvents targets cloud application usage.

Thus, the verified and documented correct completions are:

Table: DeviceFileEvents

Aggregation function: count()

To complete the KQL query against the BehaviorAnalytics table, you need to know the exact column name (for example, the Boolean field that flags a new or first-time country for the sign-in). Microsoft’s standard method to discover table schemas and column names is the Logs (Log Analytics) query window. In this pane, the left-hand Schema browser lists all connected tables and, when expanded, shows every column name and data type. Selecting a table (e.g., BehaviorAnalytics) reveals its fields, and the editor provides IntelliSense/autocomplete for columns as you type your KQL, making it straightforward to complete a clause like | where == true.

Security alerts in Azure Security Center (Defender for Cloud), the Azure Activity log, and Azure Advisor do not expose the per-table column schema needed to build KQL filters. Security Center surfaces alerts and recommendations; the Activity log records control-plane operations; and Advisor provides optimization guidance—none of these replace the Logs experience for exploring data schemas.

Therefore, to accurately identify and verify the column required in the where clause for failed sign-ins from a first-time country, you should use the Log Analytics workspace query window, consult the Schema pane for the BehaviorAnalytics table, and leverage the editor’s autocomplete to insert the correct column name.

 Internal threat: Modify the access policy settings for the key vault.

 External threat: Modify the Key Vault firewall settings.

For internal threats involving a potential compromise of Fabrikam’s own Azure AD applications, the most direct and least disruptive remediation is to modify the Key Vault access policies (or RBAC assignments, if RBAC for Key Vault data-plane is in use) to immediately remove or reduce the compromised service principal’s permissions (Get/List/Decrypt/Sign/Wrap). Microsoft guidance for Key Vault access emphasizes least privilege and promptly revoking credentials or app permissions when compromise is suspected. Access policies (or data-plane RBAC) govern which identities can access secrets, keys, and certificates; adjusting these stops further data-plane actions by the compromised app. “Resource locks” protect against deletion or configuration changes at the management plane, but they do not remove a compromised identity’s ability to read or use vault objects, so they are not an appropriate first response for this scenario.

For external threats, Microsoft recommends hardening Key Vault firewall and networking: restrict public network access, allow only required IPs, use virtual network rules, and prefer private endpoints. Key Vault includes a built-in firewall for IP and VNet ACLs; tuning these controls reduces exposure to the public internet and blocks unauthorized traffic. NSGs apply to IaaS subnets/nics and don’t directly secure the public Key Vault endpoint. Azure Firewall can add perimeter control, but it is not necessary for remediating a specific Key Vault Defender alert; the most effective and immediate remediation is tightening the Key Vault firewall settings to limit external access pathways.

To remediate active attacks automatically once alerts or incidents are detected, Microsoft Sentinel uses playbooks, which are workflows built on Azure Logic Apps. These playbooks can execute remediation actions—such as isolating a machine, blocking an account, or triggering other security control changes—without manual intervention. Microsoft’s documentation clearly states that “playbooks in Microsoft Sentinel are based on workflows built in Azure Logic Apps” and that they can “automate and orchestrate your threat response by using playbooks … run a playbook on-demand or automatically in response to specific alerts or incidents.”

When an analytics rule in Sentinel triggers an alert or incident, you can attach an automation rule which in turn invokes a playbook (i.e. a Logic Apps workflow) to perform the remediation steps. The automation rule defines the trigger conditions and calls the playbook action as part of its response actions.

Let us evaluate other options:

Azure Automation runbooks (Option A) are powerful for scripting in Azure (e.g., PowerShell or Python) and can perform remediation tasks, but they are not the native mechanism within Sentinel for orchestrated, alert-driven response workflows.

Azure Functions (Option C) are serverless compute for custom code, but you would have to build and integrate orchestration logic manually; they are not the out-of-box SOAR component in Sentinel.

Azure Sentinel livestreams (Option D) is not a recognized remediation automation component—it is irrelevant in this context.

Therefore, the correct solution to remediate active attacks (triggering automated actions in response to alerts/incidents with minimal manual effort) is to use Azure Logic Apps (via Sentinel playbooks) as the orchestration engine. Logic Apps are the documented foundation of Sentinel’s automation response capabilities.

According to Microsoft Security Operations documentation, Microsoft Defender for Endpoint is designed to protect endpoint devices—including Windows, macOS, Android, and iOS—against cyberattacks through advanced behavioral analysis, threat intelligence, and automated investigation and remediation. In the given case study, the sales team exclusively uses iOS devices and has previously experienced attacks while exchanging files using third-party applications. These unmanaged file-sharing methods exposed the team to malware, phishing, and data leakage threats.

By implementing Microsoft Defender for Endpoint on iOS, Contoso can apply unified endpoint protection across all mobile devices. Defender for Endpoint’s mobile threat defense (MTD) capabilities detect malicious apps, risky network connections, jailbroken devices, and phishing attempts. It also integrates with Microsoft Intune for compliance enforcement and conditional access—ensuring only secure, compliant devices can access corporate resources. This directly mitigates the security challenges faced by the sales team while minimizing manual investigation effort through automated response.

Therefore, the issue affecting the sales team (mobile device attacks and unsafe file transfers) can be effectively resolved using Microsoft Defender for Endpoint.

In Microsoft Sentinel (built on Azure Monitor Logs), analytics and hunting queries are executed within a Log Analytics workspace. To run Sentinel queries for Fabrikam, the tenant must have at least one workspace (with Sentinel enabled) in its subscription to host rules, incidents, hunting queries, and workbooks. Sentinel’s cross-workspace/tenant capability is provided by cross-resource queries in Kusto Query Language (KQL). The key construct for reaching outside the current workspace is the workspace() function, which lets you reference another Log Analytics workspace by name or resource ID—even across subscriptions or tenants when proper permissions (often via Azure Lighthouse or guest access) are in place.

Typical correlation looks like:

union workspace('Fabrikam-WS').SecurityEvent, workspace('Contoso-WS').SecurityEvent | …

Here, workspace() is the required element to bring together data sets from multiple tenants; operators like extend and project only shape columns and do not establish cross-tenant scope. Therefore, to meet the requirements with minimal overhead: Fabrikam needs one workspace to host its Sentinel content, and you use workspace() in your KQL to correlate Contoso and Fabrikam data.

The requirement is to enable Microsoft Defender for Servers Plan 2 on all Azure VMs while excluding Server2 from agentless scanning. Defender for Cloud provides a built-in mechanism to exclude specific machines from agentless scanning based on resource tags. The process is: assign a distinct tag name:value to the VM you want to exclude (Server2), and then, in Defender for Cloud’s Agentless scanning for machines settings, specify that tag pair under exclusions. Defender’s continuous discovery honors these exclusions and skips any VM that matches the configured tag. This approach aligns with the business requirement of least privilege and minimal administrative effort: it avoids broad configuration changes, requires no extensions on the VM, and is reversible by simply removing or changing the tag. The Microsoft Antimalware extension and Automanage configuration are unrelated to agentless scanning behavior, and a resource lock would only prevent modifications/deletions, not scanning. Therefore, to meet the Defender for Cloud requirement precisely, configure an Azure resource tag on Server2 and reference that tag in the agentless scanning exclusion settings.

For a near-real-time (NRT) analytics rule that detects sign-ins by a designated break-glass account, the most direct and performant pattern is to filter SigninLogs by joining to a Microsoft Sentinel watchlist that contains the protected account(s). Sentinel exposes watchlists to KQL through the helper function _GetWatchlist('<watchlist-name>'), which returns a table with standard columns (including SearchKey) plus any custom columns you imported. Using join kind=inner ensures the result set includes only those SigninLogs rows whose UserPrincipalName matches an entry in the watchlist—ideal for alerting on a high-value account without post-filtering.

The completed query is:

SigninLogs | join kind=inner (_GetWatchlist('breakglass_account')) on $left.UserPrincipalName == $right.SearchKey

This approach satisfies the requirement to implement an NRT rule for the break-glass account because:

NRT rules support KQL with joins and watchlists and are optimized for rapid evaluation over fresh data.

Using a watchlist lets SecOps adjust monitored accounts without editing the rule—minimizing administrative effort and aligning with least-privilege operations (no extra permissions beyond watchlist management).

The inner join pattern reduces noise by returning only matched events, which are then turned into alerts/incidents by the NRT rule.

Thus, select join and GetWatchlist, and join UserPrincipalName to the watchlist’s SearchKey.

To monitor Windows Security events from Server1 using the Windows Security Events via AMA connector, the first object you must create is a Data Collection Rule (DCR). With the Azure Monitor Agent (AMA) model used by Microsoft Sentinel, data flow is controlled by DCRs, not by the legacy MMA/OMS workspace settings. A DCR defines what to collect (e.g., the Security event log, specific event IDs or XPath queries), from where (the target machines or machine groups), and where to send it (the Sentinel/Log Analytics workspace). After creating the DCR, you associate it with Server1 (Arc-enabled), and the connector will begin streaming Security events to your Sentinel workspace. Creating a Sentinel scheduled rule or an automation rule does not enable collection; those features act after data is already ingested. Event Grid topics are unrelated to Windows event collection. Therefore, the correct first step for meeting the Sentinel requirement to monitor Server1’s Security log via AMA is to create a DCR, then assign it to Server1 and the Sentinel workspace.

The case study requires:

“Ensure that the Group1 members can create and edit playbooks.”

In Microsoft Sentinel, the ability to create, edit, and assign playbooks is granted by the Microsoft Sentinel Automation Contributor role.

This role allows users to:

Create and manage automation rules,

Create and edit playbooks (Logic Apps) in the connected subscription,

Associate playbooks with Sentinel incidents or alerts.

By contrast:

Logic App Contributor allows Logic App creation but doesn’t include Sentinel-level integration permissions.

Automation Operator can run playbooks but not edit or create them.

Sentinel Playbook Operator can execute playbooks but cannot modify or assign them.

✅ Answer for Question 11: A. Microsoft Sentinel Automation Contributor

The Sentinel requirement states:

“Ensure that multiple alerts generated by rulequery1 in response to a single user launching Azure Cloud Shell multiple times are consolidated as a single incident.”

This behavior is controlled by the event grouping setting in the analytics rule configuration.

Microsoft Sentinel documentation explains:

“Event grouping determines how alerts generated from the same rule are grouped into incidents. Selecting ‘Group all alerts triggered by this rule into a single incident’ allows all related alerts to be combined.”

Hence, configuring event grouping ensures that all alerts from rulequery1 related to the same user are consolidated into one incident, satisfying the requirement.

✅ Answer for Question 10: C. event grouping

Microsoft Sentinel playbooks can be triggered by different types of events—alerts, incidents, or entities. Since the requirement specifies “incidents generated by rulequery1” and asks for automatic processing of those incidents, the correct approach is to use a playbook with an incident trigger.

According to Microsoft Sentinel automation documentation:

“When you want automation to start after an incident is created or updated, use the incident trigger. This allows you to automate workflows such as closing incidents, adding comments, or sending notifications.”

This trigger type works with automation rules that specify when and how to execute playbooks based on incident state or severity. Using a playbook with an incident trigger meets the need for post-incident automation (e.g., auto-closing incidents if they match certain conditions).

✅ Answer for Question 8: A. a playbook with an incident trigger

To have a Microsoft Sentinel workbook pull live data from an external web service, you use the workbook’s built-in JSON data source. Workbooks can query REST endpoints that return JSON and render results dynamically in visuals. Because the Azure portal (where the workbook runs) is a different origin than your on-prem/public Webapp1, browsers will block these cross-origin requests unless the endpoint explicitly allows them. Therefore, the external service must enable CORS to permit the portal’s origin to fetch the JSON. This aligns with Microsoft guidance that Workbooks can query HTTP/HTTPS JSON endpoints and that external endpoints must allow cross-origin requests for client-side calls from the Azure portal. Enforcing CORS on Webapp1 satisfies the security model while enabling the workbook to retrieve data at view time—meeting the requirement to “dynamically retrieve data from Webapp1.” Options like “custom endpoint” or “custom resource provider” aren’t needed here, and enabling SOP or only enforcing TLS 1.2 wouldn’t address the browser’s cross-origin policy blocking the call.

In Microsoft Sentinel’s Advanced Security Information Model (ASIM), DNS queries are normalized through the Im_Dns parser, which unifies DNS telemetry from multiple sources (Infoblox, Windows DNS, Azure Firewall DNS proxy, etc.). Microsoft guidance states that when you need broad compatibility and want to “use built-in ASIM parsers whenever possible,” you should call the generic Im_Dns() parser. To minimize overhead, ASIM provides a pack parameter that restricts the parser to a specific content pack (vendor/source) so it won’t iterate through all available source parsers under the hood. For Infoblox NIOS, you pass the Infoblox pack via the pack parameter, which limits parsing to the Infoblox implementation and reduces query cost/latency while keeping the query portable across environments.

Putting it together, the recommended pattern is:

Im_Dns(pack="InfobloxNIOS")

| where DnsResponseCodeName == "NXDOMAIN"

| summarize count()

This approach satisfies all requirements:

Uses built-in ASIM (Im_Dns).

Minimizes query overhead (uses pack to limit parsing to Infoblox).

Targets NXDOMAIN responses for counting DNS request failures from Infoblox1.

The Defender for Cloud requirement states:

“The members of Group1 must be able to enable Defender for Cloud plans and apply regulatory compliance initiatives.”

According to Microsoft Defender for Cloud’s RBAC documentation, the Security Assessment Contributor role provides permissions to:

Enable and configure Defender plans,

Manage security policies and initiatives,

View and edit recommendations and compliance results.

This role gives enough permissions to perform Defender configuration tasks but follows the principle of least privilege, unlike broader roles like Owner or Contributor, which grant excessive rights.

✅ Answer for Question 9: C. Security Assessment Contributor

To monitor and detect higher-than-normal volumes of password resets, you need to gather password reset event data both from Azure Active Directory (cloud identities) and from on-premises Active Directory (domain accounts). Microsoft’s official Defender XDR and Sentinel integration guidance describes that:

Azure AD Password Protection enforces and monitors password policies in both cloud and hybrid environments. It can detect weak, commonly used, or compromised passwords and logs related password change/reset activities. Deploying Azure AD Password Protection extends password reset visibility to on-premises domain controllers through the Password Protection proxy and DC agent. This makes it the correct choice for implementing monitoring at the identity environment level.

In Microsoft Sentinel, to ingest and analyze password reset activities from on-premises servers (e.g., domain controllers), you must use the Windows Security Events via AMA connector. This connector collects Event ID 4723 (password change), 4724 (password reset), and related security logs directly from Windows Servers into the Sentinel Log Analytics workspace through the Azure Monitor Agent (AMA). Once the events are available in Sentinel, they can be correlated with other identity or behavioral analytics to detect abnormal reset volumes or potential compromise attempts.

The other options are not suitable:

Microsoft Defender for Identity focuses on identity compromise detection, not specifically on password reset volume monitoring.

Smart lockout protects against brute-force sign-in attempts but doesn’t generate detailed reset event telemetry.

Microsoft security rule and UEBA are higher-level analytic configurations, not data ingestion mechanisms.

Therefore, to meet the Sentinel requirements for monitoring password reset anomalies:

✅ Implement in the identity environment: Azure AD Password Protection

✅ Configure in Microsoft Sentinel: The Windows Security Events via AMA connector

In Microsoft Sentinel, Hunting queries are used to proactively search for threats and anomalies across collected security data. The requirement states that HuntingQuery1 must run automatically when the Hunting page of Microsoft Sentinel is accessed. According to Microsoft’s official Sentinel documentation, this behavior is achieved by adding the hunting query to “Favorites.”

When a query is marked as a favorite in the Sentinel Hunting blade, Sentinel automatically runs it every time the Hunting page is opened. This provides updated results without the need for manual execution, ensuring analysts always see the most current data for that query. This configuration also adheres to the organization’s requirement to minimize administrative effort, since it eliminates the need for scheduling, automation, or manual refresh actions.

Other options do not meet this functional requirement:

A. Add to a livestream is used to monitor near-real-time data streams, not to auto-run queries upon accessing the Hunting page.

B. Create a watchlist is used for referencing static external data sets (like IPs, users, or devices) inside KQL queries, not for automating hunting query execution.

C. Create an automation rule applies to incident management workflows, not hunting query execution.

Therefore, as per Microsoft Sentinel’s hunting documentation and best practices, the correct and verified answer is:

D. Add HuntingQuery1 to favorites.

According to Microsoft Defender for Cloud (formerly Azure Security Center) documentation, when integrating servers and virtual machines for security monitoring, the Defender for Cloud data is stored and analyzed in a Log Analytics workspace. Microsoft best practices recommend using an existing workspace when one is already deployed for centralized log collection—especially if it already gathers telemetry from Azure and on-premises resources.

In this case, the existing workspace LA1 already “contains logs and metrics collected from all Azure resources and on-premises servers.” This satisfies the business requirement of “all servers must send logs to the same Log Analytics workspace.” Creating a new workspace would violate the cost-minimization and centralization requirements. Therefore, LA1 is the correct workspace to use.

For the Windows security events collection setting, Defender for Cloud offers three levels:

Minimal – collects essential security events only (insufficient for comprehensive monitoring).

Common – collects a balanced set of security-related events (such as account logon, privilege use, and object access), recommended by Microsoft for most environments.

All Events – collects every possible security event, which increases data ingestion cost and is typically used only for high-security or regulated environments.

Because Litware Inc. must minimize cost while ensuring a full audit trail of user activities, the Common level provides the optimal balance of visibility and cost efficiency.

Therefore, based on Microsoft Defender for Cloud configuration guidelines:

✅ Log Analytics workspace to use: LA1

✅ Windows security events to collect: Common

To show labeled files from Windows 10 endpoints in the Azure Information Protection – Data discovery dashboard, you must first enable the built-in integration between Microsoft Defender for Endpoint and Azure Information Protection (AIP). This is turned on in the Microsoft Defender Security Center under Settings → Advanced features. When enabled, Defender for Endpoint inventories sensitivity labels seen on files across managed Windows devices and streams that telemetry to the AIP Data discovery experience, providing visibility into where labeled data resides on endpoints. Scanner clusters and content scan jobs in AIP are intended for on-premises repositories (file shares/SharePoint servers), not for endpoint discovery. Device health/compliance reports do not surface or forward label inventory to AIP. Therefore, the first configuration step is enabling the AIP integration advanced feature in Defender for Endpoint so labeled files on Windows clients appear in the AIP Data discovery dashboard.

The problem described in the case study states that “Cloud App Security frequently generates false positive alerts when users connect to both offices simultaneously.” This behavior is commonly associated with the “Impossible travel” anomaly detection policy in Microsoft Defender for Cloud Apps.

According to Microsoft documentation, the “Impossible travel” policy detects when a user signs in from two locations that are geographically distant within an unrealistic timeframe. However, in multi-office environments (such as Boston and Seattle) or when VPN connections are used, this policy can frequently trigger false positives, since it may misinterpret legitimate connections as anomalous.

Microsoft recommends adjusting the impossible travel detection policy to account for trusted IP ranges, known locations, or VPN endpoints to reduce false alerts. Specifically, administrators can modify the policy’s sensitivity, include the organization’s office IP addresses as trusted, and exclude known network ranges.

This approach directly aligns with the case study’s scenario and satisfies the Defender for Cloud Apps requirement to reduce false positives while maintaining user anomaly detection accuracy.

✅ Final Answer for Question 3: D. Impossible travel

In Microsoft Sentinel, entity mapping is a critical configuration that ensures detected events and alerts are correctly represented in the investigation graph, incidents, and hunting experiences. The Sentinel requirements in the case study specify:

“Add notes to events that represent data access from a specific IP address to provide the ability to reference the IP address when navigating through an investigation graph while hunting.”

To meet this requirement, the analytic rule must be configured to map entities such as IP address, user, hostname, or URL in the Set rule logic section. This mapping allows the incident and its related alerts to visually associate with those entities, enabling analysts to pivot and investigate in the Sentinel investigation graph.

According to Microsoft Sentinel documentation:

“Entity mapping in analytic rules helps correlate alerts and incidents to specific entities such as accounts, IPs, or hosts, enabling richer investigation experiences and faster triage.”

Therefore, configuring entity mapping directly under Set rule logic ensures that incidents are enriched with contextual information (for example, the specific IP address), meeting both the functional and investigative requirements.

✅ Final Answer for Question 2: C. From Set rule logic, map the entities.

The test analytics rule must generate alerts for inbound Office 365 access by several test users and group those alerts into separate incidents—one per user. In Azure Sentinel, incident grouping by entity depends on the rule’s Entity mapping. When you create a scheduled analytics rule, under Set rule logic you map columns from your query to entities like Account, IP, or Host. Once mapped, you can configure Event grouping so alerts with the same entity value (e.g., the same Account) are automatically grouped into a single incident. Turning suppression on/off or changing severity/tactics doesn’t influence entity-based incident grouping. Therefore, to ensure “one incident per test user account,” you must map the Account entity (and any other relevant entities) in Set rule logic, then enable grouping by that entity—fulfilling the Sentinel requirement.QUESTION NO: 3 HOTSPOT

You need to create the analytics rule to meet the Azure Sentinel requirements.

What should you do? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Answer:

According to Microsoft Security Operations (SecOps) and Azure Sentinel documentation, when you need to create an analytics rule that executes a custom KQL query and automatically initiates a playbook, the correct configuration is to create a Scheduled rule and ensure the playbook includes a trigger.

Here’s why:

A Scheduled analytics rule in Microsoft Sentinel (Microsoft Defender XDR portal) is designed for running custom KQL queries at defined intervals (for example, every hour or every few minutes) to detect specific patterns of suspicious activity. When the rule’s conditions are met, Sentinel generates alerts that can automatically trigger a playbook for response and automation.

A playbook in Sentinel is an Azure Logic App that automates responses to incidents or alerts. To connect a playbook to an analytics rule, it must include a trigger—specifically, the “Microsoft Sentinel Alert” or “Incident trigger.” This allows the rule to start the playbook automatically when the defined condition is met.

The other options are incorrect because:

Fusion rules are built-in and use Microsoft’s machine learning to correlate signals automatically; they can’t be used for custom queries.

Microsoft incident creation rules are also built-in and handle alert-to-incident grouping logic, not custom query execution.

A service principal would be needed for permissions (e.g., admin1 configuring playbooks), but not inside the playbook itself.

Diagnostics settings apply to log collection and retention, not rule automation.

Therefore, based on Microsoft Sentinel best practices and documentation:

✅ Create the rule of type: Scheduled

✅ Configure the playbook to include: A trigger

The requirement states that Cloud App Security (Defender for Cloud Apps) must determine whether a user’s connection is anomalous based on tenant-level patterns, and the current false positives occur when users connect through two office egress points at the same time. These symptoms align with the Impossible travel anomaly detection policy, which learns normal sign-in geolocation patterns and flags sign-ins from distant locations within an unrealistically short time window. To meet the requirement and reduce false positives, you modify the Impossible travel policy settings—such as excluding trusted corporate IP ranges/VPN egress points and tuning sensitivity—so detections better reflect tenant-wide behavior rather than isolated user hops via different office exits. Policies like Activity from anonymous/suspicious IP addresses rely on threat-intel lists of anonymizers or known-bad sources and don’t address the “two-office” scenario. Risky sign-in is part of Azure AD Identity Protection, not the MCAS anomaly policy to tune here. Thus, the policy to modify is Impossible travel.

According to Microsoft’s official Defender for Identity deployment documentation, setting up Microsoft Defender for Identity (MDI) on an on-premises domain controller (DC) such as DC1 requires a specific sequence of steps. MDI monitors and protects Active Directory from advanced threats by using sensors installed directly on domain controllers.

The correct sequence is as follows:

1️⃣ Provide global administrator credentials to the Azure AD tenant.

Microsoft Defender for Identity is created and managed in the Microsoft 365 Defender portal or Microsoft Security portal, which requires global administrator permissions to provision the MDI instance and connect it to the Azure AD tenant.

2️⃣ Create an instance of Microsoft Defender for Identity.

You must create an MDI instance in the portal before deploying any sensors. This instance defines your Defender for Identity workspace and generates the configuration package that sensors will later use to connect to the cloud service.

3️⃣ Provide domain administrator credentials to the on-premises Active Directory domain.

Domain admin credentials are required for the sensor installation because the sensor must access the domain controller’s security logs, event logs, and directory services to monitor authentication traffic and suspicious activities.

4️⃣ Install the sensor on DC1.

Since DC1 is a domain controller connected directly to the internet, the standard sensor (not the standalone sensor) is installed directly on it. The standalone sensor is used only when you do not want to install the sensor directly on a DC. The sensor automatically connects to the created MDI instance and begins collecting telemetry for detection.

This sequence aligns with Microsoft Defender for Identity deployment guidance, ensuring secure onboarding of DC1 while maintaining the principle of least privilege and meeting the business requirement to protect all domain controllers.

✅ Final Order:

Provide global administrator credentials →

Create instance of Microsoft Defender for Identity →

Provide domain administrator credentials →

Install the sensor on DC1.

To integrate Microsoft Defender for Cloud Apps (MCAS) with Microsoft Sentinel, Microsoft’s official SecOps and Sentinel documentation specifies a two-step configuration process.

In the Defender for Cloud Apps portal – You add a security extension to enable integration with external SIEM platforms. This action allows MCAS to forward its alerts, activities, and discovered app telemetry to other Microsoft or third-party security platforms. By adding the security extension, Defender for Cloud Apps is authorized to send data streams and alerts to Microsoft Sentinel through a supported API connection.

In Microsoft Sentinel (Azure portal) – You then add a data connector. Data connectors in Sentinel are predefined integration pipelines that bring in telemetry from Microsoft or external security solutions. The Microsoft Defender for Cloud Apps connector specifically ingests MCAS alerts and audit logs into Sentinel, where they can be correlated with other Microsoft Defender XDR signals, enabling unified detection and investigation across identity, endpoint, and cloud layers.

This integration approach adheres to Microsoft’s principle of minimizing administrative effort by using native connectors rather than custom ingestion or log collector configurations. Once connected, Sentinel automatically normalizes MCAS alerts into its SecurityAlert and CloudAppEvents tables for rule creation, playbook automation, and incident correlation.

Therefore, the verified correct configuration is:

Defender for Cloud Apps: Add a security extension

Sentinel: Add a data connector

To integrate Microsoft Defender for Cloud Apps (MCAS) with Microsoft Sentinel, Microsoft’s official SecOps and Sentinel documentation specifies a two-step configuration process.

In the Defender for Cloud Apps portal – You add a security extension to enable integration with external SIEM platforms. This action allows MCAS to forward its alerts, activities, and discovered app telemetry to other Microsoft or third-party security platforms. By adding the security extension, Defender for Cloud Apps is authorized to send data streams and alerts to Microsoft Sentinel through a supported API connection.

In Microsoft Sentinel (Azure portal) – You then add a data connector. Data connectors in Sentinel are predefined integration pipelines that bring in telemetry from Microsoft or external security solutions. The Microsoft Defender for Cloud Apps connector specifically ingests MCAS alerts and audit logs into Sentinel, where they can be correlated with other Microsoft Defender XDR signals, enabling unified detection and investigation across identity, endpoint, and cloud layers.

This integration approach adheres to Microsoft’s principle of minimizing administrative effort by using native connectors rather than custom ingestion or log collector configurations. Once connected, Sentinel automatically normalizes MCAS alerts into its SecurityAlert and CloudAppEvents tables for rule creation, playbook automation, and incident correlation.

Therefore, the verified correct configuration is:

Defender for Cloud Apps: Add a security extension

Sentinel: Add a data connector

Microsoft Sentinel incidents that originate from the Microsoft 365 Defender data connector are synchronized automatically with the Microsoft 365 Defender portal. To add or link an additional alert (for example, from Defender for Cloud Apps) to an existing incident, you must do it from the Microsoft 365 Defender portal’s Alerts page, not directly in Sentinel.

Once updated in Microsoft 365 Defender, the changes sync back to Sentinel, maintaining incident parity between both platforms.

✅ Correct Answer: D. the Alerts page in the Microsoft 365 Defender portal

In Microsoft 365 Defender, Advanced Hunting is the dedicated feature for creating and managing custom Kusto Query Language (KQL) queries used to proactively detect threats, investigate suspicious activity, and track ongoing risks across Microsoft 365 services (including Defender for Endpoint, Defender for Identity, Defender for Cloud Apps, and Defender for Office 365).

A custom tracked query is a saved hunting query that continuously assesses your environment’s threat status over time. According to Microsoft documentation, you create, edit, and manage these queries under the “Advanced Hunting” page in the Microsoft 365 Defender portal. The tracked queries appear under the “Custom detection rules” or “Tracked queries” tabs within this section, depending on their purpose.

Here’s how it works:

Navigate to Microsoft 365 Defender portal → Hunting → Advanced Hunting.

Use KQL to define your custom query that identifies specific threat patterns or anomalies.

Save it as a tracked query, allowing Microsoft 365 Defender to run it regularly and surface ongoing results in the hunting dashboard.

Other options are not correct:

Policies & rules – used for configuration of alerting and security policies, not ad-hoc or proactive hunting.

Explorer – provides real-time investigation of emails and threats but doesn’t support custom KQL queries.

Threat analytics – offers intelligence-driven insights and reports but doesn’t allow creating custom queries.

✅ Correct Answer: D. Advanced Hunting

 Select Take action.

 Configure the Trigger automated response settings.

 Filter by alert title.

In Microsoft Defender for Cloud, automatic responses to alerts are implemented through Take action ➜ Trigger automated response, which creates or binds a workflow automation to a Logic App. For an alert such as “Suspicious process executed”, the least-effort approach is to start from the alert experience and attach the prebuilt Logic App that uses the “When a Defender for Cloud alert is created or triggered” trigger (your LogicApp2). The documented flow is: open the alert and choose Take action; within that blade, select Trigger automated response to connect a Logic App; then scope the automation by setting conditions/filters, including Alert title, so it only runs when the specific alert (“Suspicious process executed”) is generated. This maps exactly to the three steps above.

Other panes under Take action—Mitigate the threat and Prevent future attacks—provide manual guidance or recommend hardening steps and are not used to bind a Logic App. Similarly, Suppress similar alerts is for tuning noise, not for launching automations. Because you already have LogicApp2 with the Defender for Cloud alert trigger, selecting Trigger automated response and filtering by alert title ensures the playbook runs every time that specific alert fires, with minimal administration and without creating additional custom logic.

 Policy template type: Access policy

 Filter based on: IP address tag

In Microsoft Defender for Cloud Apps (formerly Microsoft Cloud App Security), policies are used to detect, alert, and control access or activity patterns across cloud applications.

To detect connections originating from a botnet network, you need a policy that evaluates real-time access conditions such as the user’s IP address, device, or location at the time of the connection attempt. This is achieved through an Access policy, which controls and monitors session access to cloud apps using Conditional Access App Control.

Microsoft documentation specifies that Access policies can filter based on IP address ranges, tags, or risk levels. The “IP address tag” is particularly used to classify addresses into categories like “Risky,” “Anonymous proxy,” “Botnet,” etc. Microsoft’s built-in IP address tagging capability recognizes malicious or suspicious sources, including known botnet IPs.

Activity policies monitor in-app user actions such as file downloads, sharing, or admin operations—not the connection origin.

Anomaly detection policies rely on behavioral analytics and machine learning, not static IP classifications, and cannot explicitly target botnet IPs.

Therefore, to meet the requirement of detecting connections to Microsoft 365 apps from botnet networks, you must configure an Access policy that filters based on the IP address tag set to “Botnet.”

According to Microsoft Defender for Cloud documentation, continuous export allows you to stream security alerts and recommendations to other monitoring or SIEM systems. When exporting alerts in real time, Azure Event Hubs is the supported mechanism. Event Hubs provides a scalable data-streaming platform that can feed events into third-party SIEM tools such as Splunk, IBM QRadar, or ArcSight. Microsoft states: “To enable real-time streaming of security alerts or recommendations, configure Continuous Export to Azure Event Hubs. From there, your external SIEM or event processing solution can consume the data.”

The other options serve different purposes:

Azure Cosmos DB is a NoSQL database, not used for alert streaming.

Azure Event Grid is for reactive event-driven automation but not intended for continuous log export.

Azure Data Lake is for large-scale storage and analytics, not streaming.

Thus, exporting Defender alerts to Azure Event Hubs is the correct and supported configuration for continuous export to third-party SIEM solutions.

According to Microsoft Defender for Cloud Apps (formerly Microsoft Cloud App Security) documentation, when an application discovered by Cloud Discovery is deemed risky or non-compliant, the recommended remediation process is to unsanction it and enforce blocking through your network security devices or firewalls.

The process follows these four exact steps:

Select the app – In the Discovered apps tab, security analysts must first locate and select the risky application (in this case, Launchpad).Microsoft documentation states: “Select the discovered app that you want to remediate to open available actions.”

Tag the app as Unsanctioned – Marking an app as Unsanctioned flags it as not allowed for organizational use. This designation helps track and automatically block or monitor it through connected security controls.Reference guidance: “Unsanction apps that you want to block across your network to prevent user access.”

Generate a block script – Once an app is tagged as Unsanctioned, you can generate a block script compatible with your firewall or proxy device (such as Palo Alto, Cisco ASA, or Zscaler).Microsoft documentation explains: “From the unsanctioned app menu, select Generate block script to create a script for your appliance type.”

Run the script on the source appliance – Finally, to enforce blocking, the generated script must be executed on the network appliance or proxy device that is integrated with Cloud Discovery.Official description: “Run the generated script on your network appliance to block unsanctioned apps.”

This sequence ensures compliance and reduces data exposure by automatically restricting high-risk apps, aligning with Microsoft SecOps best practices of proactive risk mitigation and least privilege.

✅ Final Correct Order:

1. Select the app → 2. Tag the app as Unsanctioned → 3. Generate a block script → 4. Run the script on the source appliance

To investigate threats using data from the unified audit log in Microsoft Defender for Cloud Apps, you must first bring that audit log data into the scope of Cloud Apps. The unified audit log is a Microsoft 365 / Purview (formerly Office 365) audit log service, which records user and administrator activities across Microsoft 365 services. Microsoft Defender for Cloud Apps can ingest those audit records via a Microsoft 365 / Office 365 connector.

Before logs can flow, you need to ensure that auditing is turned on in Microsoft Purview / Microsoft 365, and then connect Microsoft 365 to Defender for Cloud Apps via the “App connectors → Microsoft 365” option under Cloud Apps settings. Once the Microsoft 365 connector (also known as the Office 365 connector) is configured, Defender for Cloud Apps begins ingesting unified audit log events and making them available for investigation, policy enforcement, anomaly detection, etc.

Here’s why the other options are not sufficient:

The Azure connector is used to bring Azure service logs into Cloud Apps, not Microsoft 365 audit logs.

User enrichment settings add metadata about users (e.g. departments, manager) to Cloud Apps context but do not by themselves bring audit log events.

Automatic log upload settings (for example, for firewall or proxy logs) support Cloud Discovery or network log ingestion, not the unified audit log from Microsoft 365.

Therefore, to use the unified audit log data for threat investigations in Defender for Cloud Apps, you must first configure the Microsoft 365 connector.

To have your query1 run every hour in a Microsoft Sentinel environment, you should configure it as an analytics rule—specifically a scheduled query (custom detection) rule. Microsoft’s official documentation for Sentinel describes “scheduled analytics rules” as queries that you configure to run on a recurring schedule, with a defined lookback period, and trigger alerts if the query results meet the rule criteria. Those scheduled rules are the mechanism by which KQL queries are periodically executed automatically.

When you create an analytics rule in Sentinel, you supply the query (i.e. query1) and you specify the recurrence (for example, every hour). Once configured as such, Sentinel takes care of executing it on schedule with minimal ongoing administrative intervention. This aligns exactly with “minimize administrative effort.”

Among the answer choices:

An automation rule is used to act upon alerts (for example, route, suppress, or apply playbooks) but does not itself schedule periodic queries.

Automated Investigation and Response (AIR) is part of Defender for Endpoint’s automated remediation workflow and is used for responding to alerts on endpoints—not for scheduling general KQL hunting queries.

A watchlist is a static data reference (list of values) you can use within queries or rules but does not itself execute queries on a schedule.

A custom detection (analytics) rule is exactly what you would use to wrap query1 into a scheduled operation.

Thus, converting query1 into a custom detection rule (i.e. a scheduled analytics rule) is the correct choice.

From Microsoft SecOps and Sentinel rule documentation: scheduled rules are based on Kusto queries configured to run at regular intervals over a lookback period, and if results cross a threshold, an alert is triggered. The rule’s scheduling frequency (such as hourly) is part of rule configuration. This model ensures your query1 gets executed every hour automatically with minimal manual work.

To automatically send a Microsoft Teams message when an incident (like a sign-in risk) is activated, you must use Logic Apps playbooks in Microsoft Sentinel.

Steps based on Microsoft documentation:

Add (create) a playbook (Option D) — a Logic App workflow that defines the automation (e.g., post a message to a Teams channel).

Associate the playbook to the analytics rule (Option B) that generates the incident — this ensures the playbook triggers automatically whenever that rule fires.

Other options do not accomplish the goal:

Entity behavior analytics (A) provides user and entity context but does not automate actions.

Fusion rule (C) correlates multi-stage attacks automatically but isn’t used to trigger notifications.

Workbooks (E) are for visualization, not automation.

✅ Final Answers:

Question 80: D. Assign the incident

Question 83: B and D

Microsoft Defender for Cloud collects security-related event data from virtual machines (VMs) by connecting them to a Log Analytics workspace. To minimize both administrative overhead and cost, the most efficient configuration involves automatic provisioning and setting an appropriate data collection level.

Automatic Provisioning (Option E):According to Microsoft Defender for Cloud documentation, enabling automatic provisioning ensures that the Log Analytics agent (also known as the Defender for Cloud agent) is automatically installed and configured on all existing and new Azure virtual machines. This eliminates the need for manual agent deployment, significantly reducing administrative effort.

Data Collection Level – Common (Option A):Defender for Cloud allows configuration of event collection levels — None, Common, and All Events.

Common collects essential security events needed for threat detection, such as logon failures, account lockouts, and other critical activities, thereby reducing storage and processing costs.

All Events collects all Windows security logs, increasing data volume and cost without necessarily improving detection for most environments.

By combining automatic provisioning with the “Common” event level, you ensure coverage for Defender for Cloud’s analytics with minimal manual configuration and optimized costs.

Therefore, the correct and verified answer is:

✅ A. Set data collection level to Common

✅ E. Enable automatic provisioning for the virtual machines

Roles in Microsoft Sentinel are designed for least privilege. The Azure Sentinel Responder role allows analysts to view, assign, update, and dismiss incidents, change status/severity, and add comments—exactly the actions a Tier-1/Tier-2 analyst needs for day-to-day triage. Azure Sentinel Reader is view-only and cannot change incident state. Azure Sentinel Contributor is broader than needed (it includes creating/editing analytics rules and other configuration). Logic App Contributor pertains to playbook authoring, not incident handling. Therefore, to let a new analyst assign and dismiss incidents while honoring least privilege, assign Azure Sentinel Responder.

When configuring the Syslog via Azure Monitor Agent (AMA) connector in Microsoft Sentinel, the Data Collection Rule (DCR) determines which Linux system logs are ingested. Syslog messages are filtered based on facility (the subsystem that generated the message) and severity level (priority).

According to Microsoft’s “Collect Syslog data sources using Azure Monitor Agent” documentation:

“Syslog facilities define which type of process generated the log (e.g., authentication, cron, daemon), and log levels define the severity (0=emerg, 1=alert, 2=crit, 3=err, etc.). To minimize data ingestion and cost, select only the facility and the highest severity level required for your investigation.”

In this scenario:

You need to collect logs related to App1, a background process (non-kernel, non-authentication, non-cron). Typically, such custom or application-level processes send logs to LOG_AUTH or LOG_DAEMON, depending on configuration. If App1 is an application that uses authentication or security features, the LOG_AUTH facility is appropriate.

You must collect only logs with a critical priority, which corresponds to Syslog severity 2 (critical). In Syslog terminology, that maps to the LOG_EMERG or “emergency” level and above.

However, to ensure minimum data collection while still capturing critical events, Sentinel’s best practice is to configure the Syslog DCR to collect only the specific facility associated with the application and the exact required severity level. For “critical only,” LOG_EMERG is the correct choice because it ensures that only the most urgent (critical/emergency) events are ingested, reducing data volume and cost.

✅ Final Configuration:

Setting

Correct Option

Purpose

Facility

LOG_AUTH

Collects security/authentication-related logs for App1

Log level

LOG_EMERG

Collects only critical/emergency events (priority ≤ 0) to minimize ingestion volume

In summary:

Selecting LOG_AUTH and LOG_EMERG satisfies both requirements — capturing only the highest-severity logs relevant to App1 while keeping the ingestion volume minimal, in line with Microsoft Sentinel Syslog DCR configuration guidance.

To minimize administrative effort in responding to incidents and remediating security threats in Microsoft Sentinel, you should use the platform’s built-in automation and orchestration capabilities — specifically Automation Rules and Playbooks.

Microsoft Sentinel Automation Rules (Option C):

Automation rules in Sentinel allow you to automate incident management tasks such as assigning owners, changing severity, tagging, or automatically running playbooks when an incident or alert is created.

They help standardize responses across similar alerts, significantly reducing manual intervention.

Microsoft documentation states:

“Automation rules simplify the management of playbook triggers and incident handling by allowing you to define actions that automatically occur when incidents are created or updated.”

Microsoft Sentinel Playbooks (Option D):

Playbooks are Logic App–based workflows that automate responses to security alerts or incidents.

They can perform remediation actions such as disabling compromised accounts, isolating infected devices, blocking IP addresses, or sending notifications to SOC teams.

You can link playbooks directly to analytics rules or call them through automation rules for end-to-end automation.

Incorrect Options:

A. Bookmarks are used for hunting investigations, not for automation or remediation.

B. Azure Automation runbooks can be used for specific administrative scripts but require more manual setup and integration — not the most efficient choice for Sentinel’s automated workflows.

E. Azure Functions apps are custom code execution environments; while powerful, they are not the primary tool for Sentinel’s automated response.

In Microsoft 365 Defender’s Advanced Hunting (Kusto Query Language - KQL), investigators use the DeviceLogonEvents table to search for authentication attempts and filter failed logons. To detect failed sign-ins from specific devices, you begin by applying a where clause that filters only the relevant logon failures and the specific device names you want to investigate.

The correct syntax and logical order—based on Microsoft Security Operations (SecOps) and Microsoft 365 Defender hunting guide—is to first filter (where) the ActionType to “LogonFailed,” then narrow down the dataset to the target devices (DeviceName in ("CFOLaptop", "CEOLaptop", "COOLaptop")). This ensures that only failed authentication attempts from those three machines are included.

After filtering, the summarize operator is used to group the data by DeviceName and LogonType, counting how many failures occurred per device and logon type. This aggregation step follows Microsoft’s recommended practice for incident hunting, allowing analysts to quickly assess which devices are generating unusual authentication failure volumes.

Finally, the project statement selects only the LogonFailures output, simplifying the results for reporting or alerting purposes.

This query structure aligns exactly with Microsoft 365 Defender Advanced Hunting documentation for detecting failed logins on specific endpoints while maintaining query efficiency and clarity, minimizing administrative overhead during investigations

In Microsoft Defender for Cloud (formerly Azure Security Center), workflow automation is used to run Logic Apps in response to recommendations or alerts. Because your goal is to automatically remediate security risks (which are surfaced as recommendations—secure score items like missing configurations or hardening tasks), the Logic App should be wired to the Recommendation trigger. The workflow automation rule monitors recommendation events and calls your Logic App with the recommendation payload, enabling parameterized remediation. To test the Logic App from within Defender for Cloud, you use the Recommendations blade: open any applicable recommendation and choose the action to run the associated Logic App. This directly invokes LA1 with the correct context (affected resource, subscription, control ID), allowing you to validate inputs, connections, and remediation steps on demand. Options like Automation rules (alerts) or Workflow automation page “Run” aren’t suited for validating recommendation-driven remediation scenarios because they either act on alerts or don’t pass the full recommendation context needed for remediation. Therefore, select the Recommendation trigger and test execution from Recommendations.

In Microsoft Sentinel, the Advanced Security Information Model (ASIM) provides a standardized schema and parser layer for common telemetry types (DNS, Authentication, NetworkSession, etc.). To investigate DNS-related activity, you should use the ASIM-normalized table ASim_Dns, which unifies data from multiple DNS sources (Microsoft Defender for Endpoint, Azure Firewall, DNS servers, etc.) under a consistent schema.

The ASim_Dns parser standardizes key fields such as:

TimeGenerated → timestamp of the DNS event

ResponseCodeName → name of the DNS response code (e.g., NXDOMAIN, NOERROR, etc.)

QueryName → domain name queried

SrcIpAddr and DstIpAddr → IP addresses involved

To investigate recent DNS failures (e.g., non-existent domains), the query filters for events where:

TimeGenerated > ago(7d) — limits to the last 7 days of DNS logs.

ResponseCodeName == "NXDOMAIN" — identifies DNS lookups that returned “Non-Existent Domain,” a common indicator of suspicious or misconfigured activity.

Therefore, the correct and compliant ASIM-based query syntax is:

ASim_Dns

| where TimeGenerated > ago(7d)

| where ResponseCodeName == "NXDOMAIN"

This approach ensures your investigation leverages ASIM normalization and aligns with Microsoft Sentinel best practices for DNS event analysis.

Dropdown/Step

Value to Select

where ResponseStatusCode in (...)

("204")

split([dropdown], "/")[-3]

RequestUri

To detect Microsoft Graph operations that change role membership, you target POST requests to the directoryRoles … /members/$ref endpoint. Adding a member to a role via Microsoft Graph is performed with POST /directoryRoles/{role-id}/members/$ref and, on success, Graph returns HTTP 204 No Content. Therefore, filtering ResponseStatusCode to 204 isolates successful role-assignment events while excluding errors like 401/403 and non-mutating redirects such as 302.

The RequestUri contains the role identifier in the path. For URIs like:

https://graph.microsoft.com/v1.0/directoryRoles/{role-id}/members/$ref

splitting on “/” yields: [https:, , graph.microsoft.com, v1.0, directoryRoles, {role-id}, members, $ref]. The element at index -3 is the {role-id}. Hence, extend Role = tostring(split(RequestUri, "/")[-3]) correctly extracts the role GUID for reporting.

Putting it together, a concise query is:

MicrosoftGraphActivityLogs

| where RequestUri has_all ("https://graph.microsoft.com/ ", "/directoryRoles", "members/$ref")

| where RequestMethod == "POST"

| where ResponseStatusCode in ("204")

| extend Role = tostring(split(RequestUri, "/")[-3])

| project TimeGenerated, IPAddress, ResponseStatusCode, Role

This returns the timestamp, source IP, success code, and the affected role ID for each successful role-membership addition in your tenant.

In Microsoft Defender for Identity (MDI), data collected from Active Directory Domain Services (AD DS) is stored in Microsoft 365 Defender advanced hunting tables under the Identity schema.

When investigating LDAP or authentication activities related to domain controllers, the correct table is IdentityLogonEvents.

The IdentityLogonEvents table contains information about all logons detected by Defender for Identity sensors on domain controllers — including LDAP binds, Kerberos, NTLM, and other authentication types.

You can identify LDAP simple binds using a query such as:

IdentityLogonEvents

| where Protocol == "LDAP"

| where AuthenticationPackage == "SimpleBind"

Other options explained:

AADServicePrincipalRiskEvents – Contains Entra (Azure AD) service principal risk detections, not AD DS activity.

AADDomainServicesAccountLogon – Used for Azure AD Domain Services (AAD DS), not traditional on-prem AD DS.

SigninLogs – Contains Entra (Azure AD) sign-in data, not LDAP or on-prem authentication.

✅ Correct table: IdentityLogonEvents

In Microsoft Sentinel (and Azure Monitor Logs), when you create a custom parser, it must output a consistent schema from a KQL query that only transforms or reshapes data. The query cannot include commands that control presentation, sorting, or time filtering logic such as sort by, take, or explicit where TimeGenerated > ago(). These constructs are valid in ad-hoc queries and hunting scenarios but not supported inside parser definitions because parsers are designed to provide reusable, schema-consistent structured data for analytics, detection rules, and normalization.

In the provided example, line 5 uses:

sort by TimeGenerated desc nulls last

Sorting is purely a presentation operation and is not allowed within a parser definition. A parser should only manipulate and project columns (for example, project, extend, parse, etc.) but should not include sort, limit, or take clauses.

Other lines serve valid data filtering and shaping purposes:

Line 2 filters the time range (TimeGenerated > ago(7d)), which can remain for a specific scenario setup.

Line 3 applies a valid operation filter (where Operation contains "delete").

Line 4 projects relevant fields.

Therefore, to prepare this query for inclusion in a custom parser, you must remove line 5, the sort by command, ensuring the parser complies with Sentinel’s parser syntax and execution engine requirements.

Hence, the correct answer is C. Remove line 5.

To collect event logs from Linux virtual machines in Microsoft Sentinel, the data ingestion path must be correctly configured through Azure Monitor (Log Analytics) and Sentinel connectors. The correct order of actions is as follows:

1️⃣ Add Microsoft Sentinel to a workspace:

Sentinel requires a Log Analytics workspace as its data foundation. You must first enable Microsoft Sentinel on a workspace by selecting Microsoft Sentinel → Add → Select workspace. This step prepares the workspace to receive and analyze security data.

2️⃣ Add a Syslog connector to the workspace:

Linux systems send event data through Syslog. You must enable the Syslog connector within the Sentinel workspace. The connector defines which Syslog facilities and severities should be collected and ingested into Sentinel. It acts as the integration bridge between the Linux hosts and Sentinel’s analytics engine.

3️⃣ Install the Log Analytics agent for Linux on the virtual machines:

To forward the logs, each Linux virtual machine needs the Log Analytics agent (OMS agent). This agent collects the configured Syslog and performance data and sends it to the connected Log Analytics workspace.

This sequence ensures proper setup for Linux log ingestion: Sentinel is first activated, then the Syslog data source is configured, and finally, agents are deployed to gather and transmit the logs.

When performing a content search for Microsoft Teams chats in the Microsoft Purview Compliance portal, Teams messages are not stored directly in Teams. Instead, they are archived in the users’ Exchange Online mailboxes. This applies to both 1:1 and group chat messages. Channel messages, on the other hand, are stored in the Exchange mailbox associated with the Microsoft 365 Group that owns the Team.

Therefore, to search Teams chat messages, you must target the Exchange mailboxes — not SharePoint sites or public folders. Selecting Exchange mailboxes ensures you’re searching the storage location where chat message copies reside.

To further minimize the scope of your search and focus only on Teams chat content, use the keyword property filter Kind:im. The “Kind” property specifies the type of message, and Kind:im limits results to instant messages, which include Teams chat content.

Here’s how the query is typically configured:

Location: Exchange mailboxes (for user and Teams group chat data).

Keyword: Kind:im (to return only instant message content).

Other options explained:

Exchange public folders – store shared emails, not Teams content.

SharePoint sites – store Teams files, not chat messages.

Category and ItemClass – can refine searches but don’t specifically filter by chat type.

✅ Final Answer:

Locations: Exchange mailboxes

Keywords: Kind

In Microsoft Sentinel, a custom analytics (scheduled) rule can fail intermittently for transient reasons. According to Microsoft’s guidance on troubleshooting analytics rules:

A transient failure is one that occurs due to a temporary condition and does not require human intervention to resolve. Examples include: • The rule query takes too long to run and times out. • Connectivity issues between the data sources and Log Analytics, or between Log Analytics and Microsoft Sentinel.Microsoft Sentinel will attempt to run the rule again after a delay, up to predetermined retries.

Therefore, option A (rule query taking too long) and option D (connectivity issues) are correct causes of intermittent failures.

On the other hand, permanent failures or configuration-level issues can lead to rules being “auto-disabled,” but those are not intermittent failures. The documentation lists permanent failure causes like the target workspace being deleted or permissions to data sources being changed. Microsoft Learn

For example:

Option B (the target workspace was deleted) is a permanent cause, not an intermittent one. Once the workspace is gone, the rule can never run.

Option C (permissions to the data sources of the rule query were modified) is also a permanent failure scenario, because once permissions change, the rule loses access until corrected, which is not something that happens intermittently unless someone is repeatedly toggling permissions.

In Microsoft Defender for Endpoint, the Live Response feature enables security analysts to remotely connect to devices (including servers) via a secure shell session directly from the Microsoft 365 Defender portal. This feature allows real-time investigation, evidence collection, and remediation commands without requiring direct network access to the device.

To enable this capability for Windows servers, you must first enable the “Live Response for Servers” advanced feature within Defender for Endpoint settings. Microsoft’s documentation explicitly states that this setting allows remote shell access to onboarded Windows Server devices, which is disabled by default for security reasons.

Once the advanced feature is enabled, Live Response permissions and functionality are managed at the device group level. Device groups in Defender for Endpoint are typically configured using device tags, which classify and organize endpoints (e.g., by department, OS type, or role). Tag-based grouping allows administrators to apply policies or features (like Live Response) efficiently to specific sets of devices, such as only production servers.

Alternative options such as “Automation level” or “device value” are unrelated — automation level controls auto-remediation, while device value assigns importance for alert prioritization.

Thus, the correct configuration steps are:

Enable Live Response for Servers under advanced features.

Apply the configuration to the target device group identified by a device tag.

✅ Final Answer:

Advanced feature: Live Response for Servers

For the device group: A device tag

Microsoft Sentinel can automatically disable scheduled analytics rules and mark them “AUTO DISABLED” when their executions repeatedly fail. A common cause is query performance issues, such as long-running queries that exceed execution time limits or hit throttling, especially with large lookback windows or inefficient joins. When a rule’s query persistently times out, Sentinel halts it to protect service health and prevent unnecessary load. Other options listed don’t align as primary triggers in Sentinel’s auto-disable behavior: exceeding 10,000 alerts in two minutes is not the documented threshold used to auto-disable rules; generic “connectivity issues” are not specific to a single rule; permissions changes can cause failures but the well-known, tested cause that leads to the AUTO DISABLED prefix in practice is repeated timeout/failure of the query itself.

In Microsoft Defender XDR, the attack surface map visually prioritizes assets that are tagged as critical to your organization’s operations. According to Microsoft’s documentation:

“Critical asset rules help ensure high-value or sensitive resources are highlighted on the attack surface map for monitoring and correlation in incidents.”

To have DB1 (a database server) appear on the attack surface map, it must be designated as a critical asset. When you configure a critical asset rule, Defender XDR automatically classifies and surfaces that system on the map, enabling enhanced monitoring, prioritization, and contextual correlation in security incidents.

Other options do not achieve this:

Asset rule: general classification, not used for attack surface prioritization.

Honeytoken entity tag: marks decoy assets for threat detection.

Sensitive entity tag: used for sensitivity context in data or user-based scenarios.

✅ Correct answer: A. a critical asset rule