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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	systems from those required for conventional systems.
Topic 5	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
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.
Topic 10	Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q83-Q88):

NEW QUESTION # 83
Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION

A. Testing the speed of the training of the model.
B. Testing the speed of the prediction by the model.
C. Testing the accuracy of the classification model.
D. Testing the API of the service powered by the ML model.

Answer: A

Explanation:
The question asks which test is least likely to be performed during the ML model testing phase. Let's consider each option:
Testing the accuracy of the classification model (A): Accuracy testing is a fundamental part of the ML model testing phase. It ensures that the model correctly classifies the data as intended and meets the required performance metrics.
Testing the API of the service powered by the ML model (B): Testing the API is crucial, especially if the ML model is deployed as part of a service. This ensures that the service integrates well with other systems and that the API performs as expected.
Testing the speed of the training of the model (C): This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model's performance and behavior rather than how quickly it was trained.
Testing the speed of the prediction by the model (D): Testing the speed of prediction is important to ensure that the model meets performance requirements in a production environment, especially for real-time applications.
ISTQB CT-AI Syllabus Section 3.2 on ML Workflow and Section 5 on ML Functional Performance Metrics discuss the focus of testing during the model testing phase, which includes accuracy and prediction speed but not the training speed.

NEW QUESTION # 84
In which ONE of the following situations would an ML model be MOST effective at determining the criticality of new defects?

A. An old application where defect records are linked to failed tests and production incidents
B. A new application which is in the early stages of the first test cycle
C. An old application with lots of defect records but a brand new development and test team
D. An old application with few critical defect records and many non-critical defect records

Answer: A

Explanation:
The correct answer is C . The CT-AI syllabus states that ML models trained on the features of the most critical defects can be used to identify defects most likely to cause system failures that account for a large proportion of reported defects. This requires meaningful historical defect data and reliable links between defect features and real outcomes.
Option C provides the strongest training basis because defect records are linked to failed tests and production incidents. These links create useful labels and outcome evidence for learning criticality. Option A lacks historical data because the application is new and early in its first test cycle. Option B has many records, but the brand-new team reduces the relevance of people- and assignment-related patterns, and the option does not indicate that records are connected to critical outcomes. Option D is weakened by class imbalance: few critical defects and many non-critical defects make it harder for the model to learn the minority critical class.
The most effective ML setting is therefore an old application with rich, outcome-linked historical data.
References/topics: CT-AI Syllabus Chapter 11, Section 11.2 "Using AI to Analyze Reported Defects."
=========

NEW QUESTION # 85
Which of the following is correct regarding the layers of a deep neural network?

A. The output layer is not connected with the other layers to maintain integrity
B. There is only an input and output layer
C. There must be a minimum of five total layers to be considered deep
D. There is at least one internal hidden layer

Answer: D

Explanation:
The syllabus clearly explains the structure of a deep neural network (DNN):
"A deep neural network comprises three types of layers. The input layer receives inputs... Between the input and output layers are hidden layers made up of artificial neurons, which are also known as nodes." (Reference: ISTQB CT-AI Syllabus v1.0, Section 6.1, page 45 of 99)

NEW QUESTION # 86
Which supervised-learning classification/regression statement is correct?
Choose ONE option (1 out of 4)

A. Predicting that diesel prices will increase by ~10% is a classification problem
B. Recognizing a dog from many different images is a regression problem
C. In classification, objects are always assigned to exactly two classes
D. Deciding whether an object is a bicycle or a motorcycle is a classification problem

Answer: D

Explanation:
The ISTQB CT-AI syllabus explains supervised learning under Section1.6 - Machine Learning Approaches
. It definesclassificationas predictingcategorical labels, whereasregressionpredictscontinuous numerical values. OptionB-deciding whether an object is a bicycle or a motorcycle-fits the definition of classification precisely because the model chooses between discrete categories. The syllabus also uses similar examples to illustrate classification tasks, reinforcing that this is the correct interpretation .
Option A is incorrect because image recognition of a dog is aclassificationtask, not regression. Option C is incorrect because predicting a 10% price rise involves forecasting anumerical value, which is aregression problem. Option D is incorrect because classification can involveany number of classes, not only two.
Multiclass classification is explicitly mentioned in the syllabus.
Therefore, OptionBis the only answer aligned with the syllabus' definitions.

NEW QUESTION # 87
In which ONE of the following situations would an ML model be MOST effective at determining the criticality of new defects?

A. An old application where defect records are linked to failed tests and production incidents
B. A new application which is in the early stages of the first test cycle
C. An old application with lots of defect records but a brand new development and test team
D. An old application with few critical defect records and many non-critical defect records

Answer: A

Explanation:
An old application where defect records are linked to failed tests and production incidents would provide the most valuable data for an ML model to determine the criticality of new defects. By using historical data of defects that are linked to actual issues in production or testing failures, the model can learn patterns and correlations between defects and their criticality, making it highly effective in predicting the criticality of new defects. This type of historical data provides the necessary context for accurate predictions.

NEW QUESTION # 88
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