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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	systems from those required for conventional systems.
Topic 3	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 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	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 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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q38-Q43):

NEW QUESTION # 38
Max. Score: 2
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I - V).
I . Autonomy
II . Maintainability
III . Safety
IV . Transparency
V . Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?
SELECT ONE OPTION

A. Aspects I, II, and III
B. Aspects I, IV, and V
C. Aspects II, III and IV
D. Aspects III, IV, and V

Answer: D

Explanation:
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects. Here's why:
Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.
Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.
Why Not Other Options:
Autonomy and Maintainability (Aspects I and II): While important, they are secondary to the immediate concerns of safety, transparency, and managing side effects in life-critical processes.

NEW QUESTION # 39
A data scientist is performing unsupervised learning on a set of financial records relating to previous loan applications, and trying to predict defaults on future loans. They are reporting poor functional performance because of data issues. Which ONE of the below is LEAST likely to be a contributory factor?

A. Inconsistent pre-processing of some records
B. Irrelevant data included in the account records
C. Missing records for some accounts
D. Missing data relating to whether loans were previously granted and repaid

Answer: D

Explanation:
The correct answer is B . In unsupervised learning, the ML model is created from unlabeled data . The algorithm infers patterns in the input data and assigns inputs to groups or classes based on commonalities, rather than learning from known output labels. The CT-AI syllabus identifies clustering and association as the main unsupervised learning problem types.
Missing information about whether previous loans were granted and repaid is essentially missing outcome label information. That would be highly relevant for supervised learning, where the model learns from input data and corresponding labels, but it is least directly relevant to an unsupervised approach. By contrast, options A, C, and D are classic dataset quality issues. Missing account records indicate incomplete data; inconsistent pre-processing can create data-format and comparability defects; and irrelevant account information may adversely influence results or waste modelling resources. The syllabus explicitly lists incomplete data, data not pre-processed, and irrelevant data as typical dataset quality issues affecting ML models.
References/topics: CT-AI Syllabus Chapter 3, Section 3.1.2 "Unsupervised Learning"; Chapter 4, Section 4.3 "Dataset Quality Issues"; Chapter 4, Section 4.4 "Data Quality and its Effect on the ML Model."
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NEW QUESTION # 40
Which statement describes factors related to test data that make testing AI-based systems difficult?

A. Artificially generated data requires legal approval and must be sanitized and encrypted
B. Creating and managing large amounts of test data can be difficult, especially when it needs to be representative
C. Using the same implementation for data acquisition by data scientists and testers prevents defect masking
D. The input data must always be the same over time, especially in real-world systems

Answer: B

Explanation:
Section2.2 - Data Preparationand4.1 - Challenges in Testing AI-Based Systemsdescribe difficulties in obtaining and managing large, representative datasets. AI-based systems requirerealistic, diverse, and representativedata reflecting real-world variations. The syllabus emphasizes that assembling such datasets is time-consuming, resource-intensive, and often constrained by availability, privacy, or domain complexity. Option B directly corresponds to these documented challenges.

NEW QUESTION # 41
Which ONE of the following statements BEST describes a testing challenge that specifically applied to a self-learning system?

A. External data sources might be required to ensure that the system is unbiased
B. In can be difficult to explain the link between test inputs and outputs
C. When systems change themselves the results of previously passing tests may change
D. It is necessary to test whether the system will relinquish control to a human at the right time

Answer: C

Explanation:
A key challenge in testing self-learning systems is that, as the system learns and adapts over time, the results of previously passing tests may change. This is because the system's behavior evolves as it learns from new data, potentially altering how it responds to test inputs. This dynamic nature of self-learning systems makes it challenging to maintain consistent and reliable test results.

NEW QUESTION # 42
Which ONE of the following approaches to labelling requires the least time and effort?
SELECT ONE OPTION

A. Al-Assisted
B. Pre-labeled dataset
C. Internal
D. Outsourced

Answer: B

Explanation:
* Labelling Approaches: Among the options provided, pre-labeled datasets require the least time and effort because the data has already been labeled, eliminating the need for further manual or automated labeling efforts.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 4.5 Data Labelling for Supervised Learning, which discusses various approaches to data labeling, including pre-labeled datasets, and their associated time and effort requirements.

NEW QUESTION # 43
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