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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 4	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 5	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 6	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 7	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 8	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 9	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 (Q135-Q140):

NEW QUESTION # 135
Which ONE of the following would be the LEAST effective input to an AI-based test optimization process?

A. Previously failing tests
B. Defect reports
C. Source control data
D. Test environment downtime

Answer: D

Explanation:
Test environment downtime would be the least effective input to an AI-based test optimization process. While downtime may affect the ability to run tests, it does not provide direct insight into the quality of the tests or the effectiveness of the AI model being tested. In contrast, previously failing tests, defect reports, and source control data are much more relevant as they provide information about areas where the system may have issues or require further optimization.

NEW QUESTION # 136
There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?

A. Use it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers
B. Use it to prioritize defects automatically based on the time expected for the fix to be made, the speed of the fix, and the likelihood of regressions
C. Use it to determine the root cause of each defect and develop a process improvement plan that can be implemented to remove the most common root causes
D. Use it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas

Answer: A

Explanation:
The syllabus explains that ML models can be used to analyze reported defects and suggest which developers are best suited to fix them based on historical data about defect assignment and resolution speed:
"Assignment: ML models can suggest which developers are best suited to fix particular defects, based on the defect content and previous developer assignments." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.2, page 78 of 99)

NEW QUESTION # 137
Before deployment of an AI-based system, a developer is expected to demonstrate in a test environment how decisions are made. Which of the following characteristics does decision making fall under?

A. Autonomy
B. Non-determinism
C. Explainability
D. Self-learning

Answer: C

Explanation:
The syllabus definesexplainabilityas the ability to understand how the AI-based system comes up with a particular result:
"Explainability is considered to be the ease with which users can determine how the AI-based system comes up with a particular result." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.7)

NEW QUESTION # 138
Which statement regarding pairwise testing in an AI-based automotive lane-keeping assist system is correct?

A. Pairwise testing only uses parameters directly influenced by the driver, otherwise the number of test cases becomes too large.
B. Pairwise testing reduces the test suite so much that it is typically feasible within the available time.
C. Pairwise testing is usually insufficient because most defects arise only from interactions of many parameters.
D. Pairwise testing can reduce testing efforts otherwise very high due to the large number of parameters.

Answer: D

Explanation:
The ISTQB CT-AI syllabus (Section4.3 - Test Design for AI-Based Systems) highlights pairwise testing as an effectivetest-case reduction techniquefor systems with many input parameters.
Lane-keeping assist systems typically include environmental, sensor, and vehicle-dynamic parameters, making exhaustive testing infeasible. Pairwise testing significantly reduces the number of test cases while still capturingall 2-way interactions, which are responsible for a large proportion of software defects.
Option B aligns with this syllabus description: pairwise testing reduces otherwise extremely large parameter combinations, making test effort manageable.

NEW QUESTION # 139
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION

A. Testing the data pipeline for any sources for algorithmic bias.
B. Testing the distribution shift in the training data for inappropriate bias.
C. Test the model during model evaluation for data bias.
D. Check the input test data for potential sample bias.

Answer: C

Explanation:
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 is B. Test the model during model evaluation for data bias.
Reference:
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.

NEW QUESTION # 140
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