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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
Topic 2	Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
Topic 3	ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
Topic 4	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 5	Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
Topic 6	Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 7	Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
Topic 8	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 9	Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q30-Q35):

NEW QUESTION # 30
Which of the following are the three activities in the data acquisition activities for data preparation?

A. Identifying, gathering, labelling
B. Cleaning, transforming, augmenting
C. Feature selecting, feature growing, feature augmenting
D. Building, approving, deploying

Answer: A

Explanation:
According to the ISTQB Certified Tester AI Testing (CT-AI) syllabus, data acquisition, a critical step in data preparation for machine learning (ML) workflows, consists of three key activities:
* Identification:This step involves determining the types of data required for training and prediction. For example, in a self-driving car application, data types such as radar, video, laser imaging, and LiDAR (Light Detection and Ranging) data may be identified as necessary sources.
* Gathering:After identifying the required data types, the sources from which the data will be collected are determined, along with the appropriate collection methods. An example could be gathering financial data from the International Monetary Fund (IMF) and integrating it into an AI-based system.
* Labeling:This process involves annotating or tagging the collected data to make it meaningful for supervised learning models. Labeling is an essential activity that helps machine learning algorithms differentiate between categories and make accurate predictions.
These activities ensure that the data is suitable for training and testing machine learning models, forming the foundation of data preparation.

NEW QUESTION # 31
Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?

A. Data testing
B. Tuning the model
C. Deploying the model
D. Evaluating the model

Answer: B

Explanation:
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 is C. Tuning the model.

NEW QUESTION # 32
Which challenge to testing self-learning systems puts you at risk of a data attack?
Choose ONE option (1 out of 4)

A. Complex test environment
B. Unexpected changes
C. Inadequate specification of the operating environment
D. Insufficient testing time

Answer: B

Explanation:
The ISTQB CT-AI syllabus describes thatself-learning systems continuously adjust their behaviorduring operation as new data arrives. Section4.1 - Challenges of Testing AI-Based Systemshighlights that such systems are vulnerable todata attacks, particularly through adversarial inputs, poisoning, or malicious drift.
The risk arises because unexpected changes in the input distribution may alter the learned model in harmful ways. OptionD - Unexpected changescorresponds directly to this syllabus-defined risk.
Option A refers to system specification issues but does not relate to data attacks. Option B discusses environment complexity, which makes testing difficult but is not tied to adversarial threats. Option C (insufficient testing time) affects quality but does not specifically increase vulnerability to malicious data manipulation.
Unexpected changes-including data drift, poisoned samples, or maliciously constructed training data-pose the greatest risk. When a self-learning system adapts to altered data patterns, it may unknowingly learn incorrect associations, causing model degradation or manipulation. Therefore,Option Dcorrectly identifies the challenge that increases exposure to data attacks.

NEW QUESTION # 33
Which statement regarding data preparation in the ML workflow is correct?
Choose ONE option (1 out of 4)

A. Since data preparation is time-consuming, all steps should be automated.
B. One challenge of data gathering is obtaining high-quality data from multiple sources.
C. A key challenge in data transformation is the removal or correction of erroneous data.
D. Sampling is so well researched that it is no longer considered risky.

Answer: B

Explanation:
The ISTQB CT-AI syllabus describes theML data preparation workflowin Section2.2 - Data Preparation.
Data preparation consists ofdata gathering,cleaning,transformation, andsampling. The syllabus emphasizes that one significant challenge duringdata gatheringis combining data frommultiple heterogeneous sources, which often differ in structure, quality, and format. Ensuring the resulting dataset is accurate, complete, and representative can be complex, making this a critical challenge in the ML workflow. This aligns directly with OptionC.
Option A is incorrect because erroneous data correction is part ofcleaning, not transformation. Option B contradicts the syllabus: while automation can help,not all steps should be automateddue to the need for expert oversight, especially in detecting subtle data quality issues. Option D is incorrect because sampling continues to involve risk-particularly around representativeness-and the syllabus emphasizes caution, not complacency.
Thus, OptionCis the only statement that accurately reflects the syllabus.

NEW QUESTION # 34
Which statement about testing levels for AI-based systems is correct?

A. If AI is offered as a service, system testing includes API tests of the service
B. Input data testing checks whether the inputs from the data pipeline are received by the model correctly and exchanged with all system components
C. Acceptance testing checks non-functional requirements such as explainability
D. ML model testing ensures that the relevant ML functional performance criteria are met

Answer: D

Explanation:
Section4.3 - Test Levels for AI Systemsclearly defines ML model testing as the level at which testers evaluate whether an ML model fulfills itsfunctional performance criteria, including accuracy, precision, recall, F1, robustness, stability, and fairness. Therefore, Option C is the correct and syllabus-aligned statement.

NEW QUESTION # 35
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