Salesforce Tableau-CRM-and-Einstein-Discovery-Consultant Salesforce Tableau CRM Einstein Discovery Consultant (SU24) Exam Practice Test

The steps in the data exploration process in Salesforce Tableau CRM are as follows:

• View: Initially viewing the data to understand what is available and identify areas of interest.
• Explore: Digging deeper into the data to discover patterns, anomalies, and insights.
• Refine: Adjusting the exploration by filtering, sorting, or enhancing the data visualization for clearer insights.
• Save: Saving the state of your exploration to revisit or continue working on later.
• Share: Sharing your findings with others in the organization, either through direct dashboard access, downloadable reports, or URLs.

For a dashboard that needs to visualize Closed Won opportunities by different time groupings (month, quarter, year) using a single chart, the appropriate action is: D. Use selection binding to update the grouping in the chart. This method allows the chart to dynamically change the grouping based on user selection, effectively switching between month, quarter, or year views within the same visual representation. Reference: Salesforce Documentation on Dynamic Dashboards

In Salesforce Analytics Query Language (SAQL), the order of functions plays a crucial role in how data is processed. The 'filter' and 'order' functions can be used interchangeably depending on the specific needs of the query. However, 'offset' should always follow either 'filter' or 'order', and 'limit' must be specified after 'offset'. This order ensures that the query first narrows down the data set, then offsets into this subset, and finally limits the number of records returned.

Predictive insights are primarily beneficial for:

• C: Drilling down into the underlying reasons behind a prediction. This capability enables users to understand not just what might happen in the future but why those outcomes are likely, based on the data being analyzed. This understanding is crucial for strategic decision-making and for optimizing processes based on predictive analytics.

To address the need for department-specific dashboard access while utilizing common datasets with the same row-level security, the best approach is to:

• C: Create one application for each department, put common datasets in the shared app, and use roles to share applications. This setup ensures that each department has its own tailored application containing relevant dashboards while still accessing shared datasets from a central application. The use of roles facilitates controlled access based on user roles within the organization.

When you select one variable in an Einstein Discovery story and examine its insights, you are comparing the impact of that selected variable with all other data in the story. This involves analyzing how much the selected variable influences the outcome variable relative to other variables in the dataset. The process evaluates the contribution of the selected variable to the overall variance in the outcome, offering insights into its significance and relationship within the broader data context. Reference: Salesforce Einstein Discovery Story Insights

When Salesforce Analytics runs a dataflow that accesses Salesforce objects, it uses the Integration User. This is a system user configured to perform back-end operations that require interacting with Salesforce data, independent of any specific human user. This setup ensures that data operations such as dataflows are consistent and not tied to the permissions or roles of individual users, which could vary and affect the execution of such tasks. Reference: Salesforce Help - Dataflows and Integration User

In statistical terms, the standard deviation measures the amount of variation or dispersion in a set of values. A higher standard deviation indicates that the values are more spread out from the mean (average). Among the options provided, a standard deviation of 16 suggests the highest degree of spread away from the average compared to the other values.

To convert the "CloseDate_Month" numerical field into a month name in the dataset, the consultant should use:

• C: computeExpression. This Dataflow node allows the transformation of existing data fields using expressions, such as converting a month number into a month name. By applying a suitable expression, the 'CloseDate_Month' can be transformed into a string representing the month’s name, enhancing readability and usability in filters.

In Einstein Analytics (Tableau CRM), the security options include:

• Inherited Security: Utilizes the existing Salesforce security model to control access.
• Row-level Security with Security Predicates: Offers granular control by restricting data visibility at the row level based on specific criteria.
• App Level Security: Manages access at the application level within Einstein Analytics.

Field-level security is not directly configurable within Einstein Analytics; instead, it's managed through the broader Salesforce security settings, affecting data visibility before it's imported into Einstein Analytics datasets. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_security_overview.htm &type=5

For Benito to build and edit a dashboard using the Seed Bank dataset in the Einstein Analytics app, the admin needs to do the following two things:

• Grant Benito access to the specific app that contains the Seed Bank dataset. This ensures he can access the necessary dataset within the confines of the designated app.
• Assign him a permission set that includes the "Create and Edit Analytics Dashboards" permission. This permission is crucial for enabling Benito to not only view but also create and modify dashboards within the app.

This dual requirement ensures both data accessibility and the ability to perform required operations on the dashboard. Reference: Salesforce Help - Permission Sets

The correct set of statements that generate a monthly amount on a cumulative basis annually is Option B. This option likely includes cumulative sum functions applied within a grouped statement, typically done with a grouping by 'Month' and an annual reset. Such SQL-like syntax is commonly used in Tableau CRM for time-based cumulative calculations.

In Salesforce Analytics Query Language (SAQL), a keyword refers to specific commands or functions such as 'group', 'foreach', 'filter', and more. These keywords are essential for structuring queries and must be used in lowercase to be recognized by the system. Keywords control operations on data streams within the queries, facilitating complex data manipulations and analyses.

Each prebuilt Analytics app has its own PSL. When you set up permissions for those apps, select the right PSL. For Service Analytics, select "Service Analytics Apps." For Event Monitoring, select "Event Monitoring Analytics Apps," and so on.

The "Unexplained Bar" in the "Why it Happened" chart of Einstein Discovery represents the difference between the predicted outcome and the observed outcome. This metric is essential as it highlights the variance that the model's explanatory factors do not account for, providing insight into the limitations of the current model. This understanding aids in interpreting the accuracy and reliability of the model predictions.

For Universal Containers to automate the creation of datasets from their on-premise data warehouse, the best solution would be to utilize middleware with the Analytics External Data API. This approach enables the integration of on-premise data systems with Salesforce through a secure and scalable middleware, which can automate the data extraction and loading process into Tableau CRM, thus replacing the weekly manual process.

To address the requirement where some users need to create their own dashboards and others only need to use them, the best practice is to:

• A: Create two permission sets with different system permissions. One permission set would grant capabilities necessary for creating and managing dashboards, likely including permissions like "Create Dashboard," "Edit Dashboard," etc. The other permission set would grant only "View" permissions, restricting users to dashboard usage without modification capabilities.

When deploying models to an existing prediction in Tableau CRM, it is important to be aware of the limits imposed by the platform. Specifically, there is a limit of 10 models under a single Prediction Definition. This limit helps manage the complexity and ensures that the predictions remain manageable and efficient.

For a Tableau CRM dashboard that utilizes data sync for replication:

In Salesforce Analytics, the basic actions within a query that are fundamental to building meaningful visualizations and data summaries include:

• A. Aggregate by a measure: This involves summarizing data using statistical measures like sum, average, maximum, etc., which help in understanding the distribution and trends of data.
• C. Filter to narrow down your results: Filtering allows users to limit the data shown in the dashboard to only those records that meet certain criteria, thus focusing on more relevant subsets of data.
• D. Grouping by a dimension: Grouping involves categorizing data based on specific fields (dimensions), which helps in analyzing the data across different segments or categories.

To enable Analytics for Communities in Tableau CRM, you need specific prerequisites:

• License Requirements: A Customer Community Plus or Partner Community license is necessary, enabling more extensive CRM functionalities for community members.
• Permission Set License: Community members must be assigned the Analytics for Communities permission set license. This setup ensures that community users have the appropriate access to use and benefit from Analytics features within their community environments.

alesforce Tableau CRM uses two internal users—Integration User and Security User—to manage access to Salesforce data for analytics purposes. The Integration User is used to extract data from Salesforce objects and fields during dataflow jobs, ensuring that data movements are secure and compliant with the organization's access policies. The Security User's permissions are employed when querying datasets with row-level security based on the User object, allowing for secure and appropriate data access based on user roles and permissions.

In Salesforce Tableau CRM, with data sync enabled, the maximum number of dataflow definitions an organization can have is 25. This limit is designed to balance the need for comprehensive data operations with system performance and manageability.

The correct timeseries statement to fix the SAQL query for predicting sales in each sales region is:

• C: q = timeseries q generate 'sum_Sales' as 'Forecasted_Sales' with (dateCols=('Date_Year', 'Date_Quarter', "Y-Q"), partition='Region', seasonality=4). This statement correctly specifies the required parameters for a timeseries analysis, including the date columns, the partition by 'Region', and the seasonality factor, which is crucial for accurate forecasting in time series analysis.

The appropriate action when Einstein Discovery surfaces an unexpected pattern is to determine if the pattern is a data issue or a new insight. This approach involves verifying the integrity and accuracy of the data, and understanding whether the pattern represents a meaningful insight into the business operations or an anomaly caused by data issues.

To potentially improve an AUC score of .62 in a Tableau CRM story, consider the following strategies:

• A. Segment the training set: By segmenting the training set, you can ensure that the model trains on more homogeneously grouped data, which might help in improving the model’s predictive accuracy and, consequently, the AUC score.
• D. Add more rows of relevant data: Increasing the volume of relevant data can provide the model with more examples to learn from, which might enhance its ability to generalize and improve the AUC score.

In Tableau CRM, recipes allow users to create new datasets from various sources. The sources that can be utilized include:

• A. Existing datasets: Recipes can combine or transform data from datasets that have been previously created or imported into Tableau CRM.
• B. Records from a Salesforce object: Direct extraction of data from Salesforce objects into a new dataset via a recipe is supported, allowing users to leverage live Salesforce data for further analysis.

Creating an Einstein Discovery story begins with selecting a dataset in Tableau CRM. Users can navigate to the desired dataset and initiate the story creation process by selecting the "Create Story" option. This feature allows users to explore data automatically, uncovering patterns and predictive insights that help in making data-driven decisions. The process does not involve copying texts or uploading presentations but rather utilizes the existing datasets to generate meaningful and actionable insights. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_edd_create_story.htm &type=5

The strength of the model depicted in the chart cannot be determined conclusively from the metrics shown (GINI coefficient of 0.535, maximum accuracy of 0.7597, and threshold of 0.5163). These metrics indicate aspects of the model's performance, but they do not provide a complete picture of its effectiveness in real-world scenarios. For instance, the GINI coefficient suggests some degree of discriminative power, yet it is not high enough alone to declare the model very effective. Similarly, the maximum accuracy shows potential, but without context regarding the complexity of the prediction task or comparisons to baseline models, a definitive conclusion about the model's strength cannot be drawn.

* template-info.json manages all elements of your template, including metadata information about the template, the Analytics objects that define dashboards and lenses, and the other files that are part of the template.* ui.json manages the configuration wizard that drives app creation. It defines the number of wizard pages, the order of wizard questions, and any messages you want the user to see.* variables.json contains all template variables, including text for wizard questions and specifications for the answers. Variables also define conditional questions. For example, you may want some questions to appear in the wizard only if an org contains certain data. Or you might want to add more specific questions based on the answers to more general wizard questions.* template-to-app-rules.json defines the rules the template follows. For example, you could define a rule that specifies that if an org doesn’t use certain Salesforce objects, the app won’t refer to them in dashboards or include them in the dataflow. Rules also define how variables are handled. For example, if the wizard asks which fields to include in filters for accounts, template-to-app-rules.json determines how that choice is reflected in dashboards.

To build a table in a Sales Manager dashboard and include a summary as the first line in a compare table:

• A: Use the Show Summary option in the compare table. This feature automatically calculates and displays a summary row at the top of the compare table, providing an aggregated view of the data points included in the table.

The Service Analytics Overview dashboard in Tableau CRM is designed to provide comprehensive insights into various aspects of service operations. It includes:

• Key metrics such as open cases, average time to close, first contact resolution, and customer satisfaction, offering a quick overview of service efficiency.
• Drill-down capabilities into more detailed dashboards like agent performance, channel review, and telephony metrics, enabling deeper analysis.
• Availability on both desktop and mobile platforms, ensuring accessibility across different devices.
• It serves as an excellent starting point for service analytics, providing a holistic view of the service department’s performance.

This dashboard is a central tool for managing and analyzing service operations effectively, helping organizations to optimize their customer service strategies. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_service_analytics.htm &type=5

In Tableau CRM (formerly Einstein Analytics), you can restrict access to data at the row level using security predicates. Security predicates are filter expressions added to datasets that dynamically restrict data access based on user attributes or record specifics. This method allows administrators to enforce data security policies directly within the dataset, ensuring that users only see data relevant and permitted to them, without altering the underlying data structure like removing rows manually or adding flags. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_security_predicates.htm &type=5

Predicate is a name for a filter condition that defines row-level access to records in a dataset Example: "rowLevelSecurityFilter":"'AccountOwner' == \"$User.Name\""

The issue of the VP of Sales seeing "No results found" while others can view data in the dataset suggests a problem with access permissions. Two likely causes are: A) The VP’s Salesforce profile may lack read permissions on some crucial fields of the Opportunity__c custom object, which could prevent viewing data where those fields are essential. B) Security predicates at the dataset level might be set up in a way that restricts data visibility for the VP, perhaps due to specific conditions that exclude higher hierarchy levels.

In Salesforce Tableau CRM, the limit on the number of queries that a user can execute per day is set to 50,000. This is designed to ensure optimal performance across the platform while providing sufficient capacity for users to carry out extensive data analysis throughout the day.

The widget property that allows a consultant to restrict the view to a domain bounded by the values entered is "Add Custom Range." This property enables setting specific range limits on the data displayed in the widget, effectively controlling the scope of data visualization to ensure focus on relevant metrics.

For accessing Tableau CRM data from remote applications or websites, the following methods are recommended:

• B Lightning Out enables Salesforce components, including Tableau CRM dashboards, to be embedded into external web applications, maintaining their interactive capabilities and security.

Einstein Discovery in Salesforce functions like having a personal data scientist on staff. It utilizes advanced machine learning algorithms to analyze large volumes of data, identify patterns, and provide predictive insights and recommendations. This tool simplifies complex data analysis tasks and enables users to make informed decisions without the need for deep statistical knowledge or expertise in data science. Reference: Salesforce Einstein Discovery Overview

A merge key in a dataset recipe is used to join related data from different sources within Tableau CRM. By specifying merge keys, users define the common fields between datasets that serve as the basis for combining records. This process is critical for creating comprehensive datasets that pull in relevant data from disparate sources, ensuring data accuracy and consistency across the joined datasets. The use of merge keys facilitates the alignment of data based on shared attributes, crucial for accurate analysis and reporting. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_prep_recipes_join_data.htm &type=5

XMD stands for Extended Metadata. This component in Salesforce Tableau CRM allows users to customize datasets and dashboards further. With XMD, users can define label overrides, specify numeric formats, set up color coding, and more, enhancing the visualization and interpretation of data within the analytics environment.

In a scenario where Recipe3 depends on Dataset1 created by Recipe1, while Recipe1 and Recipe2 have no dependencies, the optimal scheduling strategy is to make Recipe1 and Recipe2 time-based and Recipe3 event-based, triggered by the successful completion of Recipe1. This ensures that Recipe3 only processes data after Dataset1 is updated and available, maintaining data integrity and workflow efficiency.

To enhance the performance of data synchronization in Tableau CRM, after removing unused objects and fields, the next logical step is to evaluate the connection mode for each connected object. This involves analyzing whether the current connection settings (e.g., full or incremental sync) are optimized based on the data volume and update frequency. Adjusting these settings can significantly improve sync performance by reducing unnecessary data transfer and processing.