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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	systems from those required for conventional systems.
Topic 2	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 3	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 4	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.
Topic 5	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 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	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 8	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 9	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 10	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 (Q126-Q131):

NEW QUESTION # 126
When verifying that an autonomous AI-based system is acting appropriately, which of the following are MOST important to include?

A. Test cases to detect the system appropriately automating its data input
B. Test cases to detect the system prompting for unnecessary human intervention
C. Test cases to verify that the system automatically suppresses invalid output data
D. Test cases to verify that the system automatically confirms the correct classification of training data

Answer: B

Explanation:
When verifyingautonomous AI-based systems, a critical aspect is ensuring that they maintain an appropriate level of autonomy whileonly requesting human intervention when necessary. If an AI system unnecessarily asks for human input, it defeats the purpose of autonomy and can:
* Slow down operations.
* Reduce trust in the system.
* Indicate improper confidence thresholds in decision-making.
This is particularly crucial inautonomous vehicles, AI-driven financial trading, and robotic process automation, where excessive human intervention would hinder performance.
* A. Test cases to verify that the system automatically confirms the correct classification of training data# This is relevant for verifying training consistency but not for autonomy validation.
* B. Test cases to detect the system appropriately automating its data input# While relevant, data automation does not directly address the verification of autonomy.
* D. Test cases to verify that the system automatically suppresses invalid output data# This focuses on output filtering rather than decision-making autonomy.
Why are the other options incorrect?Thus, the mostcritical test casefor verifyingautonomous AI-based systemsis ensuring that itdoes not unnecessarily request human intervention.
* Section 8.2 - Testing Autonomous AI-Based Systemsstates that it is crucial to testwhether the system requests human intervention only when necessaryand does not disrupt autonomy.
Reference from ISTQB Certified Tester AI Testing Study Guide:

NEW QUESTION # 127
An airline has created an ML model to project fuel requirements for future flights. The model imports weather data such as wind speeds and temperatures, calculates flight routes based on historical routings from air traffic control, and estimates loads from average passenger and baggage weights. The model performed within an acceptable standard for the airline throughout the summer but as winter set in, the load weights became less accurate. After some exploratory data analysis, it became apparent that luggage weights were higher in the winter than in summer.
Which of the following statements BEST describes the problem and how it could have been prevented?

A. The model suffers from corruption and therefore should be reloaded into the computer system being used, preferably with a method of version control to prevent further changes
B. The model suffers from drift and therefore should be regularly tested to ensure that any occurrences of drift are detected soon enough for the problem to be mitigated
C. The model suffers from drift and therefore the performance standard should be eased until a new model with more transparency can be developed
D. The model suffers from a lack of transparency and therefore should be regularly tested to ensure that any progressive errors are detected soon enough for the problem to be mitigated

Answer: B

Explanation:
The syllabus states:
"Concept drift occurs when the operational environment changes without the trained model changing correspondingly. The outputs of the model become less accurate and less useful. Therefore, the operational model should be regularly evaluated against its acceptance criteria." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6, Page 54 of 99)

NEW QUESTION # 128
A neural network has been designed and created to assist day-traders improve efficiency when buying and selling commodities in a rapidly changing market. Suppose the test team executes a test on the neural network where each neuron is examined. For this network, the shortest path indicates a "buy" and it will only occur when the one-day predicted value of the commodity is greater than the spot price by 0.75%. The neurons are stimulated by entering commodity prices and testers verify that they activate only when the future value exceeds the spot price by at least 0.75%.
Which of the following statements BEST explains the type of coverage being tested on the neural network?

A. Neuron coverage
B. Sign-change coverage
C. Value-change coverage
D. Threshold coverage

Answer: D

Explanation:
The syllabus details that threshold coverage requires each neuron to achieve an activation value greater than a specified threshold:
"Threshold coverage: Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold." (Reference: ISTQB CT-AI Syllabus v1.0, Section 6.2, page 48 of 99)

NEW QUESTION # 129
You are developing a "flower" ML model... Which of the following describes an objection that you can NEGLECT in your risk assessment?
Choose ONE option (1 out of 4)

A. The possible inputs for the 'leaf' and 'flower' ML models are so different that reuse has few advantages over new development.
B. The possible outputs of the "leaf" and "flower" ML models are so different that reuse has few advantages over new development.
C. The classification behavior of the "flower" ML model is more difficult to understand when it is reused compared to when it is developed from scratch.
D. The probability of misclassification of the ML model "flower" is higher when it is reused than when it is developed from scratch.

Answer: B

Explanation:
The ISTQB CT-AI syllabus explains that reusing pre-trained models is strongly related tosimilarity between the original task and the new task. Section1.8 - Pre-trained Models and Transfer Learningstates that reuse is effective when the new task is similar to the original one, such as adapting a cat-classifier to classify dog breeds. The syllabus warns about risks related toinput differences,data preparation inconsistencies, inherited shortcomings, andexplainability issues. These are legitimate objections (matching options A, B, and C) because large differences in image inputs or patterns can undermine transfer learning; misclassification risk can increase; and explainability often decreases when reusing pre-trained models .
However,output differences are NOT a valid concernhere. Both the leaf-based and flower-based ML models classifythe same plant species, meaning theiroutputs are identical. The syllabus does not identify output mismatch as a transfer-learning risk. Real risks concerninputs,bias inheritance,model transparency, andtraining differences-not output labels. Therefore, OptionDdescribes an objection that can be safely neglected, because output classes are the same and do not hinder reuse.

NEW QUESTION # 130
Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION

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

Answer: B

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.
References:
* 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 # 131
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