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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • systems from those required for conventional systems.
Topic 3
  • 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 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
  • 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 6
  • 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 (Q15-Q20):

NEW QUESTION # 15
Which of the following is THE LEAST appropriate tests to be performed for testing a feature related to autonomy?
SELECT ONE OPTION

Answer: C

Explanation:
* Testing Autonomy: Testing for human handover when it should not be relinquishing control is the least appropriate because it contradicts the very definition of autonomous systems. The other tests are relevant to ensuring smooth operation and transitions between human and AI control.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Testing Autonomous AI-Based Systems and Testing for Human-AI Interaction.


NEW QUESTION # 16
Which ONE of the following statements BEST describes how system complexity can cause challenges when testing an AI based system?

Answer: B

Explanation:
The correct answer is D . The syllabus explains that AI-based systems are often used for tasks too complex for humans to perform, and this can create a test oracle problem because testers may not be able to determine expected results in the usual way. It also states that when the internal structure of an AI-based system is generated by software, the structure may be too complex for humans to understand, leading to the situation where the system can only be tested as a black box.
Option A concerns test data acquisition, which is a general AI testing challenge but not the central complexity mechanism identified in this question. Option B describes an approach used in explainability or bias-related investigation, especially model-agnostic reasoning about input-output sensitivity. Option C is more characteristic of self-learning systems, where the system may change its own behaviour over time.
Complexity primarily causes difficulty because expected behaviour, internal logic, component interactions, and test oracles become hard to understand or define.
References/topics: CT-AI Syllabus Chapter 8, Section 8.5 "Challenges Testing Complex AI-Based Systems"; Section 8.7 "Test Oracles for AI-Based Systems."
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NEW QUESTION # 17
You are evaluating the use of a highly accurate pre trained model that is used widely in industry tor a similar use case. There is an intention to apply transfer learning techniques to further customise the model. Which ONE of the following is the LEAST likely to be a significant risk with this approach?

Answer: C

Explanation:
The correct answer is D because the scenario explicitly reduces that risk: the model is described as highly accurate , widely used in industry, and applied to a similar use case. The CT-AI syllabus explains that using a pre-trained model can save training costs and largely eliminate the risk of it not working when an existing suitable model is reused. It also states that transfer learning effectiveness depends heavily on similarity between the original model's function and the new required function.
Options A, B, and C remain significant risks under the syllabus. A is credible because shortcomings such as inherited biases may pass from the pre-trained model to the reused system. B is credible because models created through transfer learning are highly likely to remain sensitive to vulnerabilities of the pre-trained model, including adversarial attacks. C is credible because differences in data preparation steps between original model development and later use may reduce functional performance. Therefore, the least likely significant risk is unexpectedly low functional performance of the already highly accurate, widely used, similar-purpose pre-trained model.
References/topics: CT-AI Syllabus Chapter 1, Sections 1.8.1-1.8.3 "Pre-Trained Models," "Transfer Learning," and "Risks of using Pre-Trained Models and Transfer Learning."
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NEW QUESTION # 18
A bank wants to use an algorithm to determine which applicants should be given a loan. The bank hires a data scientist to construct a logistic regression model to predict whether the applicant will repay the loan or not.
The bank has enough data on past customers to randomly split the data into a training dataset and a test
/validation dataset. A logistic regression model is constructed on the training dataset using the following independent variables:
* Gender
* Marital status
* Number of dependents
* Education
* Income
* Loan amount
* Loan term
* Credit score
The model reveals that those with higher credit scores and larger total incomes are more likely to repay their loans. The data scientist has suggested that there might be bias present in the model based on previous models created for other banks.
Given this information, what is the best test approach to check for potential bias in the model?

Answer: A

Explanation:
The syllabus mentions that experience-based testing and EDA are effective for detecting biases:
"Experience-based testing can be used to verify that the training dataset is operationally relevant and identify potential sources of bias. EDA is also useful for exploring the data and understanding any relationships that might lead to bias in the model." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.3, page 58 of 99)


NEW QUESTION # 19
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer).
A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I.Pairwise testing of combinations
II.Testing each individual model for accuracy
III.A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION

Answer: B

Explanation:
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
* Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
* Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
* A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
:
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.


NEW QUESTION # 20
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