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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 2	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.
Topic 3	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 4	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 5	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 6	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 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	systems from those required for conventional systems.
Topic 9	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q93-Q98):

NEW QUESTION # 93
Which of the following problems would best be solved using the supervised learning category of regression?

A. Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store.
B. Determining if an animal is a pig or a cow based on image recognition.
C. Recognizing a knife in carry on luggage at a security checkpoint in an airport scanner.
D. Determining the optimal age for a chicken's egg laying production using input data of the chicken's age and average daily egg production for one million chickens.

Answer: D

Explanation:
Understanding Supervised Learning - RegressionSupervised learning is a category of machine learning where the model is trained on labeled data. Within this category,regressionis used when the goal is to predict a continuous numeric value.
* Regressiondeals with problems where the output variable is continuous in nature, meaning it can take any numerical value within a range.
* Common examples include predicting prices, estimating demand, and analyzing production trends.
* (A) Determining the optimal age for a chicken's egg-laying production using input data of the chicken's age and average daily egg production for one million chickens.#(Correct)
* This is a classicregression problembecause it involves predicting a continuous variable:daily egg productionbased on the input variablechicken's age.
* The goal is to find a numerical relationship between age and egg production, which makesregression the appropriate supervised learning method.
* (B) Recognizing a knife in carry-on luggage at a security checkpoint in an airport scanner.#(Incorrect)
* This is animage recognition task, which falls underclassification, not regression.
* Classification problems involve assigning inputs to discrete categories (e.g., "knife detected" or
"no knife detected").
* (C) Determining if an animal is a pig or a cow based on image recognition.#(Incorrect)
* This is anotherclassification problemwhere the goal is to categorize an image into one of two labels (pig or cow).
* (D) Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store.#(Incorrect)
* This problem could involve a mix ofclassificationandassociation rule learning, but it does not explicitly predict a continuous variable in the way regression does.
* Regression is used when predicting a numeric output."Predicting the age of a person based on input data about their habits or predicting the future prices of stocks are examples of problems that use regression."
* Supervised learning problems are divided into classification and regression."If the output is numeric and continuous in nature, it may be regression."
* Regression is commonly used for predicting numerical trends over time."Regression models result in a numerical or continuous output value for a given input." Analysis of Answer ChoicesReferences from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, as it aligns with the principles of regression-based supervised learning.

NEW QUESTION # 94
Which of the following are the three activities in the data acquisition activities for data preparation?

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

Answer: D

Explanation:
The syllabus defines data acquisition as consisting of three steps:
"Data acquisition: The activity of acquiring data relevant to the business problem to be solved by an ML model, typically involving the activities of identifying, gathering and labelling data." (Reference: ISTQB CT-AI Syllabus v1.0, Section 4.1, page 33 of 99)

NEW QUESTION # 95
Which of the following statements about reinforcement learning is correct?

A. The agent creates a model of the environment from labeled data during training
B. From experience, the agent learns theoptimal reward function
C. The agent's training is based on a reward function that rewards successful attempts
D. The approach is suitable when the application doesnotrequire interaction with the environment

Answer: C

Explanation:
Section1.6.3 - Reinforcement Learningof the ISTQB CT-AI syllabus states that reinforcement learning (RL) is based on anagent interacting with an environment, performing actions, and receivingrewards or penalties. The core concept is thereward function, which guides the agent's learning process. The syllabus emphasizes that training in RL isdriven by rewards, and the agent aims to maximize cumulative reward over time. Therefore, Option C directly reflects the correct description: the agent learns by being rewarded for successful actions .

NEW QUESTION # 96
Which of the following statements regarding experience-based testing for AI-based systems is correct?

A. Exploratory testing is often used for AI-based systems because there are often insufficient specifications or problems with the test oracle for AI-based systems.
B. Tour refers to intuitive test case design for AI-based systems based on multiple, sequential test cases using systematically biased training data.
C. In checklist-based testing of AI-based systems, the existing test cases are dynamically adapted, for example based on metamorphic testing.
D. Intuitive test case design for AI-based systems involves interactive, hypothesis-driven examination of data for correlations or developmental trends.

Answer: A

Explanation:
The ISTQB CT-AI syllabus explains inSection 4.4 - Experience-Based Testing for AI Systemsthat AI-based systems frequently suffer frominsufficient specifications, unpredictable model behavior, andtest oracle problems, especially when outputs depend on probabilistic or learned patterns.
The syllabus explicitly states thatexploratory testingis especially valuable in such contexts because it allows testers to investigate the system interactively, observe unexpected behavior, and evaluate system responses that cannot be fully predicted beforehand. Thus, Option C accurately reflects the role and justification of exploratory testing for AI systems.

NEW QUESTION # 97
Which statement regarding data preparation in the ML workflow is correct?
Choose ONE option (1 out of 4)

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

Answer: A

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 # 98
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