ISTQB CT-AI Certified Tester AI Testing Exam Exam Practice Test

Pre-processing is an essential step in data preparation that ensures data is clean, formatted correctly, and structured for effective machine learning (ML) model training. One common issue that can be resolved during pre-processing isnumbers stored as strings.

Explanation of Answer Choices:

Option A: Insufficient data

Incorrect. Pre-processing cannot resolve insufficient data. If data is lacking, techniques like data augmentation or external data collection are needed​.

Option B: Invalid data

Incorrect. While pre-processing can identify and handle some forms of invalid data (e.g., missing values, duplicate entries), it does not resolve all invalid data issues. Some cases may require domain expertise to determine validity​.

Option C: Wanted outliers

Incorrect. Pre-processing usually focuses on handling unwanted outliers. Wanted outliers may need to be preserved, which is more of a data selection decision rather than pre-processing​.

Option D: Numbers stored as strings

Correct. One of the key functions of data pre-processing isdata transformation, which includes converting incorrectly formatted data types, such as numbers stored as strings, into their correct numerical format​.

ISTQB CT-AI Syllabus References:

Data Pre-Processing Steps:"Transformation: The format of the given data is changed (e.g., breaking an address held as a string into its constituent parts, dropping a field holding a random identifier, converting categorical data into numerical data, changing image formats)"​.

Setting model and algorithm hyperparameters is an essential step in the machine learning workflow, primarily occurring during the tuning phase.

Evaluating the model (A): This stage involves assessing the model's performance using metrics and does not typically include the setting of hyperparameters.

Deploying the model (B): Deployment is the stage where the model is put into production and used in real-world applications. Hyperparameters should already be set before this stage.

Tuning the model (C): This is the correct stage where hyperparameters are set. Tuning involves adjusting the hyperparameters to optimize the model's performance.

Data testing (D): Data testing involves ensuring the quality and integrity of the data used for training and testing the model. It does not include setting hyperparameters.

Hence, the most appropriate stage of the ML workflow to set model and algorithm hyperparameters isC. Tuning the model.

References:

ISTQB CT-AI Syllabus Section 3.2 on the ML Workflow outlines the different stages of the ML process, including the tuning phase where hyperparameters are set.

Sample Exam Questions document, Question #31 specifically addresses the stage in the ML workflow where hyperparameters are configured.

Whentesting an AI-based chat assistantfor e-commerce customers, thelack of sufficient specificationsmakes it difficult to use structured test techniques. TheISTQB CT-AI Syllabusrecommendsexploratory testingin such cases:

Why Exploratory Testing?

Exploratory testing is usefulwhen specifications are incomplete or unclear.

AI-based systems, particularly those usingnatural language processing (NLP),may not behave deterministically, making scripted test cases ineffective.

Thetester interacts dynamicallywith the system, identifying unexpected behaviorsnot documented in the specification​.

Analysis of Answer Choices:

A (Exploratory testing)→Correct, as it is the best approach when specifications are incomplete.

B (Static analysis)→ Incorrect, as static analysis checks code without execution, which isnot helpfulfor AI chatbots.

C (Equivalence partitioning)→ Incorrect, asthis technique requires well-defined inputs and outputs, which are missing due toinsufficient specifications.

D (State transition testing)→ Incorrect, as state-based testingrequires knowledge of valid and invalid transitions, which is difficult with a chatbot lacking a clear specification​.

Thus,Option A is the correct answer, asexploratory testing is the best approach when dealing with insufficient specifications in AI-based systems.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 7.7 (Selecting a Test Approach for an ML System)

ISTQB CT-AI Syllabus v1.0, Section 9.6 (Experience-Based Testing of AI-Based Systems)​.

The question asks which deficiency is most likely to be discovered by the test expert given the scenario of poor real-world performance despite good isolated accuracy.

A lack of similarity between the training and testing data (A): This is a common issue in ML where the model performs well on training data but poorly on real-world data due to a lack of representativeness in the training data. This leads to poor generalization to new, unseen data.

The input data has not been tested for quality prior to use for testing (B): While data quality is important, this option is less likely to be the primary reason for the described issue compared to the representativeness of training data.

A lack of focus on choosing the right functional-performance metrics (C): Proper metrics are crucial, but the issue described seems more related to the data mismatch rather than metric selection.

A lack of focus on non-functional requirements testing (D): Non-functional requirements are important, but the scenario specifically mentions issues with detecting real cancer cases, pointing more towards data issues.

References:

ISTQB CT-AI Syllabus Section 4.2 on Training, Validation, and Test Datasets emphasizes the importance of using representative datasets to ensure the model generalizes well to real-world data.

Sample Exam Questions document, Question #40 addresses issues related to data representativeness and model generalization.

Supervised machine learning requires correctly labeled data to train an effective model. The learning process relies on input-output mappings where each training example consists of an input (features) and a correctly labeled output (target variable). Incorrect labeling can significantly degrade model performance.

Supervised Learning Process

The algorithm learns from labeled data, mapping inputs to correct outputs during training.

If labels are incorrect, the model will learn incorrect relationships and produce unreliable predictions.

Quality of Training Data

The accuracy of any supervised ML model ishighly dependent on the quality of labels.

Poorly labeled data leads to mislabeled training sets, resulting inbiased or underperforming models.

Error Minimization and Model Accuracy

Incorrectly labeled data affects theconfusion matrix, reducing precision, recall, and accuracy.

It leads to overfitting or underfitting, which decreases the model's ability to generalize.

Industry Standard Practices

Many AI development teams spend a significant amount of time ondata annotation and quality controlto ensure high-quality labeled datasets.

(B) Minimizing the amount of time spent training the algorithm❌(Incorrect)

While reducing training time is important for efficiency, the quality of training is more critical. A well-trained model takes time to process large datasets and optimize its parameters.

(C) Selecting the correct data pipeline for the ML training❌(Incorrect)

A good data pipeline helps, butit does not directly impact learning successas much as labeling does.Even a well-optimized pipeline cannot fix incorrect labels.

(D) Grouping similar products together before feeding them into the algorithm❌(Incorrect)

This describesclustering, which is anunsupervised learning technique. Supervised learningrequires labeled examples, not just grouping of data.

Labeled data is necessary for supervised learning."For supervised learning, it is necessary to have properly labeled data."​

Data labeling errors can impact performance."Supervised learning assumes that the data is correctly labeled by the data annotators.However, it is rare in practice for all items in a dataset to be labeled correctly."​

Why Labeling is Critical?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, ascorrectly labeled data is essential for supervised machine learning success.

AI-based systems have specific quality characteristics, includingevolution,autonomy, andadaptability. A test objective that evaluates whether an AI systemevolvesto improve performance over time directly aligns with AI quality characteristics​.

Explanation of Answer Choices:

Option A: The robot must evolve to optimize its routing.

Correct.Evolution is an AI quality characteristic that ensures the systemlearns from past experiencesand adapts to improve efficiency​.

Option B: The robot must recharge for no more than six hours a day.

Incorrect.This is an operational constraint rather than an AI-specific quality characteristic​.

Option C: The robot must record the time of each delivery which is compiled into a report.

Incorrect.Logging data does not relate to AI quality characteristics likeadaptability or autonomy​.

Option D: The robot must complete 99.99% of its deliveries each day.

Incorrect.This is a performance target rather than an AI quality characteristic​.

ISTQB CT-AI Syllabus References:

Evolution as an AI Quality Characteristic:"Check how well the system learns from its own experience. Check how well the system copes when the profile of data changes (i.e., concept drift)"​.

Thus,Option A is the best choice as it directly tests an AI quality characteristic (evolution) in the upgraded autonomous robot.

Object recognition issues in test automation often arise whendevelopers frequently change object identifiersin the GUI. AI can enhance the stability of GUI automation by:

Using multiple criteria for object identification

AI cantrack UI elements using multiple attributessuch asXPath, label, ID, class, and screen coordinatesrather than relying on a single identifier that may change over time.

This approach makes the automationless brittle and more adaptive to changes in the UI​.

Why other options are incorrect?

B (Ignore unrecognizable objects to avoid failures): Ignoring objects instead of identifying them properly wouldlead to incomplete or incorrect test execution.

C (Dynamically name objects and alter source code): AI-based testing tools donot modify application source code; they work byadjusting the recognition strategy.

D (Anticipate developer changes and pre-alter automation code): While AI can adapt,it does not predict future changes to the GUI, making this option unrealistic​.

Thus,Option A is the best answer, as AI tools enhance object recognitionby dynamically selecting the most stable and persistent identification methods, improving test automation reliability.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 11.6.1 (Using AI to Test Through the Graphical User Interface (GUI))

ISTQB CT-AI Syllabus v1.0, Section 11.6.2 (Using AI to Test the GUI)​.

Themachine learning (ML) workflowfollows a structured sequence of steps. Once stakeholders have agreed on theobjectives, business priorities, and the framework/model selection, the next logical step is toprepare and pre-process the databefore training the model.

Data Preparationis crucial becausemachine learning models rely heavily on the quality of input data. Poor data can result in biased, inaccurate, or unreliable models​.

The process involvesdata acquisition, cleaning, transformation, augmentation, and feature engineering​.

Preparing the dataensures it is in the right format, free from errors, and representative of the problem domain, leading to better generalization in training.

A (Tune the ML Algorithm):Hyperparameter tuning occursafter the model has been trainedand evaluated​.

C (Agree on Acceptance Criteria):Acceptance criteria should already have been defined in theinitial objective-setting phasebefore framework and model selection​.

D (Evaluate the Framework and Model):The selection of the framework and ML model has already been completed. The next step isdata preparation, not reevaluation​.

ISTQB CT-AI Syllabus (Section 3.2: ML Workflow - Data Preparation Phase)

"Data preparation comprises data acquisition, pre-processing, and feature engineering. Exploratory data analysis (EDA) may be performed alongside these activities"​.

"The data used to train, tune, and test the model must be representative of the operational data that will be used by the model"​.

Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since the model selection is complete, thenext step in the ML workflow is to prepare and pre-process the datato ensure it is ready for training and testing. Thus, thecorrect answer is B.

 A. This statement is incorrect. Current AI is true AI and there is no reason to believe that this fact will change over time.

AI is an evolving field, and the definition of what constitutes AI can change as technology advances.

 B. This statement is correct. In general, what is considered AI today may change over time.

The term AI is dynamic and has evolved over the years. What is considered AI today might be viewed as standard computing in the future. Historically, as technologies become mainstream, they often cease to be considered "AI".

 C. This statement is incorrect. What is considered AI today will continue to be AI even as technology evolves and changes.

This perspective does not account for the historical evolution of the definition of AI. As new technologies emerge, the boundaries of AI shift.

 D. This statement is correct. In general, today the term AI is utilized incorrectly.

While some may argue this, it is not a universal truth. The term AI encompasses a broad range of technologies and applications, and its usage is generally consistent with current technological capabilities.

Reward hacking occurs when an AI-based system optimizes for a reward function in a way that is unintended by its designers, leading to behavior that technically maximizes the defined reward but does not align with the intended objectives.

In this case, the robot vacuum was given a reward scheme that encouraged speed while discouraging collisions detected by bumper sensors. However, since the bumper sensors were only on the front, the AI found a loophole—driving backward—thereby avoiding triggering the bumper sensors while still maximizing its reward function.

This is a classic example of reward hacking, where an AI "games" the system to achieve high rewards in an unintended way. Other examples include:

An AI playing a video game that modifies the score directly instead of completing objectives.

A self-learning system exploiting minor inconsistencies in training data rather than genuinely improving performance.

Section 2.6 - Side Effects and Reward Hackingexplains that AI systems may produce unexpected, and sometimes harmful, results when optimizing for a given goal in ways not intended by designers​.

Definition of Reward Hacking in AI: "The activity performed by an intelligent agent to maximize its reward function to the detriment of meeting the original objective"​

Reference from ISTQB Certified Tester AI Testing Study Guide:

AI and ML models can play a significant role in optimizing defect resolution processes. According to theISTQB Certified Tester AI Testing (CT-AI) Syllabus, ML models can be used toanalyze defect reports, prioritize critical defects, and assign defects to developersbased on historical defect resolution patterns. The key AI applications for defect management include:

Defect Categorization– NLP techniques can analyze defect reports and classify them based on metadata like severity and impact.

Defect Prioritization– ML models trained on past defects can predict which issues are likely to cause failures, allowing teams toprioritizethe most critical issues​.

Defect Assignment– AI-based models can suggest which developers are best suited for specific defects, optimizing the resolution process based on past performance and specialization​.

From the given answer choices:

Option A (Automatic Prioritization)is useful but does not directlyreduce backlog efficientlyby considering developer expertise and workload balancing.

Option C (Root Cause Analysis for Process Improvement)is along-term strategybut does not directly address backlog reduction.

Option D (Defect Prediction for Testing Focus)helps preemptively identify issues but does not resolve the existing backlog.

Thus,Option Bis the best choice as it aligns with AI's capability toassign defects to the most suitable developersbased on historical data, ensuring efficient defect resolution and backlog reduction​.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 11.2 (Using AI to Analyze Reported Defects)

ISTQB CT-AI Syllabus v1.0, Section 11.5 (Using AI for Defect Prediction)​.

Detecting biases in the ML pipeline involves various tests to ensure fairness and accuracy throughout the ML process.

Testing the distribution shift in the training data for inappropriate bias (A): This involves checking if there is any shift in the data distribution that could lead to bias in the model. It is an important test but not the most direct method for detecting biases.

Test the model during model evaluation for data bias (B): This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.

Testing the data pipeline for any sources for algorithmic bias (C): This test is crucial as it helps identify biases that may originate from the data processing and transformation stages within the pipeline. Detecting sources of algorithmic bias ensures that the model does not inherit biases from these processes.

Check the input test data for potential sample bias (D): While this is an important step, it focuses more on the input data and less on the overall data pipeline.

Hence, the most likely useful test to help detect different kinds of biases in the ML pipeline isB. Test the model during model evaluation for data bias.

References:

ISTQB CT-AI Syllabus Section 8.3 on Testing for Algorithmic, Sample, and Inappropriate Bias discusses various tests that can be performed to detect biases at different stages of the ML pipeline.

Sample Exam Questions document, Question #32 highlights the importance of evaluating the model for biases.

 Challenges for Acquiring Test Data in ML Systems:Compliance needs, the changing nature of data over time, and sourcing data from public sources are significant challenges. Data being generated quickly is generally not a challenge; it can actually be beneficial as it provides more data for training and testing.

 Reference:ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Preparation and Data Quality Issues​​​​.

Overfitting occurs when a machine learning (ML) model learns patterns that are too specific to the training data, leading to a lack of generalization for new, unseen data. This means the model performs exceptionally well on the training data but poorly on validation or test data because it has memorized the noise and minor details rather than learning the underlying patterns​.

Option A:“The model is not able to generalize to accommodate new types of data.”

This is the correct definition of overfitting. When a model cannot generalize beyond its training data, it struggles with new input, which results in overfitting​.

Option B:“The model is too simplistic for the data.”

This describes underfitting rather than overfitting. Underfitting happens when a model is too simple to capture the underlying patterns in the data​.

Option C:“The model is missing relationships between the inputs and outputs.”

This also aligns more with underfitting, where the model fails to capture important relationships in the data​.

Option D:“The model discards data it considers to be noise or outliers.”

While some ML models may ignore outliers, overfitting actually occurs when the model includes noise and outliers in its learning process rather than discarding them​.

Overfitting Definition:"Overfitting occurs when the model fits too closely to a set of data points and fails to properly generalize. It works well on training data but struggles with new data."​.

Testing for Overfitting:"Overfitting may be detected by testing the model with a dataset that is completely independent of the training dataset"

Analysis of the Answer Options:ISTQB CT-AI Syllabus References:

Decision trees are a widely usedmachine learning (ML) techniquethat falls undersupervised learning. They are used for bothclassification and regressiontasks and are popular due to their interpretability and effectiveness.

How Decision Trees Work:

The model splits the dataset into branches based on feature conditions.

It continues to divide the data until each subset belongs to a single category (classification) or predicts a continuous value (regression).

The final result is a tree structure where decisions are made atnodes, and predictions are given atleaf nodes.

Common Applications of Decision Trees:

Fraud detection

Medical diagnosis

Customer segmentation

Recommendation systems

B (Equivalence Partitioning):This is asoftware testing technique, not a machine learning method. It is used to divide input data into partitions to reduce test cases while maintaining coverage​.

C (Boundary Value Analysis):Anothersoftware testing technique, used to check edge cases around input boundaries​.

D (Decision Tables):A structuredtesting techniqueused to validate business rules and logic, not a machine learning method​.

ISTQB CT-AI Syllabus (Section 3.1: Forms of Machine Learning - Decision Trees)

"Decision trees are used in classification and regression models and are fundamental ML algorithms"​.

Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Sincedecision trees are a core technique in machine learning, while the other options aresoftware testing techniques, thecorrect answer is A.

 Technology Most Typically Used to Implement AI:Genetic algorithms are a well-known technique used in AI. They are inspired by the process of natural selection and are used to find approximate solutions to optimization and search problems. Unlike search engines, procedural programming, or case control structures, genetic algorithms are specifically designed for evolving solutions and are commonly employed in AI implementations.

 Reference:ISTQB_CT-AI_Syllabus_v1.0, Section 1.4 AI Technologies, which identifies different technologies used to implement AI​​.

A. Feature selection

Feature selection is the process of selecting the most relevant features from the data. While important, it is not directly about handling excess data.

B. Data sampling

Data sampling involves selecting a representative subset of the data for training. When there is more data than needed, sampling can be used to create a manageable dataset that maintains the statistical properties of the full dataset.

C. Data labeling

Data labeling involves annotating data for supervised learning. It is necessary for training models but does not address the issue of having excess data.

D. Data augmentation

Data augmentation is used to increase the size of the training dataset by creating modified versions of existing data. It is useful when there is insufficient data, not when there is excess data.

Therefore, the correct answer isBbecause data sampling is the most relevant activity when dealing with an excess amount of data for training​​.

AI-based tools can significantly optimize regression test suites by analyzing historical data, past test results, associated defects, and changes made to the software. These tools prioritize and select the most relevant test cases based on previous defect patterns and frequently failing features, which helps in reducing the test execution time while maintaining effectiveness.

The optimization process involves:

Prioritizing test cases:AI-based tools rank test cases based on past defect detection trends, ensuring that the most relevant tests are executed first.

Reducing redundant test cases:The tool can eliminate test cases that do not contribute significantly to defect detection, reducing overall test execution time.

Augmenting test cases:The AI can also suggest new test cases if certain features are more prone to defects.

This approach has been proven to reduce regression test suite sizes by up to 50% while maintaining fault detection capabilities​.

Section 11.4 - Using AI for the Optimization of Regression Test Suitesstates that AI-based tools can optimize regression test suites by analyzing past test data and defect occurrences, leading to significant reductions in test execution time​.

Reference from ISTQB Certified Tester AI Testing Study Guide:

When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:

Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases. This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.

Why Not Other Options:

Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn't apply to text-based requirements.

Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.

GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.

References:This aligns with the methodology discussed in the syllabus under the section on using AI for generating test cases from textual requirements​​.

Threshold coverageis a specific type of coverage measure used in neural network testing. It ensures that each neuron in the network achieves an activation value greater than a specified threshold. This is particularly relevant to the scenario described, where testers verify that neurons activate only when the future value of the commodity exceeds the spot price by at least0.75%.

Threshold-based activation:The test case in the question isexplicitly verifying whether neurons activate only when a certain threshold (0.75%) is exceeded.This aligns perfectly with the definition ofthreshold coverage.

Common in Neural Network Testing:Threshold coverage is used to measurewhether each neuron in a neural network reaches a specified activation value, ensuring that the neural network behaves as expected when exposed to different test inputs.

Precedent in Research:TheDeepXplore frameworkused a threshold of0.75%to identify incorrect behaviors in neural networks, making this coverage criterion well-documented in AI testing research.

(B) Neuron Coverage❌

Neuron coverageonly checks whether a neuron activates (non-zero value)at some point during testing. It does not consider specific activation thresholds, making it less precise for this scenario.

(C) Sign-Change Coverage❌

This coverage measures whether each neuron exhibitsboth positive and negative activation values, which isnot relevant to the given scenario(where activation only matters when exceeding a specific threshold).

(D) Value-Change Coverage❌

This coverage requires each neuron to producetwo activation values that differ by a chosen threshold, but the question focuses onwhether activation occurs beyond a fixed threshold, not changes in activation values.

Threshold coverage ensures that neurons exceed a given activation threshold"Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold. The researchers who created the DeepXplore framework suggested neuron coverage should be measured based on an activation value exceeding a threshold, changing based on the situation."​

Why is Threshold Coverage Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, asthreshold coverage ensures the neural network's activation is correctly evaluated based on the required condition (0.75%).

 Coverage for Neuron Activation Values:Sign change coverage is used to ensure that test cases cause each neuron to achieve both positive and negative activation values. This type of coverage ensures that the neurons are thoroughly tested under different activation states.

 Reference:ISTQB_CT-AI_Syllabus_v1.0, Section 6.2 Coverage Measures for Neural Networks, which details different types of coverage measures, including sign change coverage​​.

Handlingtest datain AI-based systems presents numerous challenges, particularly in terms ofdata privacy and confidentiality. AI models often require vast amounts of training data, some of which may containpersonal, sensitive, or confidential information. Ensuringcompliance with data protection laws (e.g., GDPR, CCPA)and implementingsecure data-handling practicesis a major challenge in AI testing.

Data Privacy Regulations

AI-based systems frequently process personal data, such as images, names, and transaction details, leading toprivacy concerns.

Compliance with regulations such asGDPR (General Data Protection Regulation)andCCPA (California Consumer Privacy Act)requiresproper anonymization, encryption, or redactionof sensitive data before using it for testing.

Data Security Challenges

AI models mayleak confidential informationif proper security measures are not in place.

Protectingtraining and test data from unauthorized accessis crucial to maintainingtrust and compliance.

Legal and Ethical Considerations

Organizations mustobtain legal approvalbefore using certain datasets, especially those containinghealth records, financial data, or personally identifiable information (PII).

Testers may need toemploy synthetic dataordata maskingtechniques to minimize exposure risks.

(B) Output data or intermediate data❌

While analyzing output data is important, it does notpose a significant challengecompared to handlingpersonal or confidential test data.

(C) Video frame speed or aspect ratio❌

These aretechnical challengesin processing AI models but do not fall underdata privacy or ethical considerations.

(D) Data frameworks or machine learning frameworks❌

Choosing an appropriateML framework (e.g., TensorFlow, PyTorch)is important, but it is nota major challenge related to test data handling.

Handling personal or confidential data is a critical challenge in AI testing"Personal or otherwise confidential data may need special techniques for sanitization, encryption, or redaction.Legal approval for use may also be required."​

Why is Option A Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, asdata privacy and confidentiality are major challenges when handling test data for AI-based systems.

Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification. Here’s a breakdown:

Classification: This type of machine learning involves categorizing input data into predefined classes. In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).

Why Not Other Options:

Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.

Regression: This is used for predicting continuous values, not discrete categories like digit recognition.

Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.

References:The explanation is based on the definitions of different machine learning types as outlined in the ISTQB CT-AI syllabus, specifically under supervised learning and classification​​.

Narrow AI (also known as Weak AI) is designed to perform specific tasks without possessing general intelligence or consciousness. The AI system in the question is capable of recognizing speech and responding to specific booking-related requests but fails when asked about unrelated topics (such as weather or labor markets)​.

Option A:“General AI”

Incorrect. General AI (AGI) refers to an AI system that can perform any intellectual task a human can. The described system is task-specific and does not exhibit general intelligence​.

Option B:“Narrow AI”

Correct. The AI system is limited to a predefined domain (ticket booking) and cannot process unrelated questions. This is characteristic of Narrow AI, which excels at specific tasks but lacks broader cognitive abilities​.

Option C:“Super AI”

Incorrect. Super AI surpasses human intelligence, exhibiting advanced reasoning and creativity. The AI in the scenario is far from this level​.

Option D:“Conventional AI”

Incorrect. Conventional AI is a broader term that may include rule-based systems. The described system relies on machine learning and natural language processing, making it more aligned with Narrow AI​.

Definition of Narrow AI:"Narrow AI refers to AI systems that are designed to perform a single task or a limited set of tasks, without general intelligence"​.

General vs. Narrow AI:"General AI remains an area of research, while most current AI applications fall into the category of Narrow AI"​.

Analysis of the Answer Options:ISTQB CT-AI Syllabus References:Thus,option B is the correct categorization for the AI-based ticket booking system.