Exam CT-AI Overviews & CT-AI Exam Tutorials

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ISTQB CT-AI Exam Syllabus Topics:

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

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q149-Q154):

NEW QUESTION # 149
Which statement about AI-based test case generation is correct?
Choose ONE option (1 out of 4)

A. An AI-based system under test must not be used as a functional test oracle.
B. A different test oracle is usually required for each AI-generated functional test case.
C. AI-generated functional test cases typically result in poor requirements coverage.
D. Expected results may not be available for AI-generated functional test cases.

Answer: D

Explanation:
The ISTQB CT-AI syllabus indicates inSection 5.2 - AI for Testingthat AI-generated test cases may not come withpredefined expected results. This is because test-case generation methods-such as evolutionary algorithms, reinforcement learning, or clustering-based sampling-produceinputs, but the tester must still determine the correct outputs. Therefore, OptionCis correct: expected results may not be available, especially when AI produces novel or previously unseen input combinations.
Option A is incorrect: AI-based test generation can improve coverage by exploring large input spaces. Option B is incorrect because the need for oracles does not depend on whether the test case is AI-generated. Option D is incorrect because the syllabus allows using a model as an oracle inback-to-back testing, provided limitations are understood.
Therefore,Option Cis the correct syllabus-aligned statement.

NEW QUESTION # 150
Which ONE of the below is NOT likely to cause a data quality issue affecting a single ML model?

A. Incorrect weights
B. Security issues
C. Faulty sensors
D. Hardware issues

Answer: A

Explanation:
Incorrect weights refer to parameters within a trained model and are not typically considered a data quality issue. Data quality issues usually arise from the input data itself, such as security issues, hardware issues, or faulty sensors, which can lead to incorrect or missing data. Incorrect weights are a problem during the model training or fine-tuning phase, not directly related to data quality.

NEW QUESTION # 151
Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?

A. Compliance needs require proper care to be taken of input personal data.
B. Data for the use case is being generated at a fast pace.
C. Test data being sourced from public sources.
D. Nature of data constantly changes with lime.

Answer: B

Explanation:
Challenges for Acquiring Test Data in ML Systems: Compliance needs, the changing nature of data over time, and sourcing data from public sources are significant challenges. Data being generated quickly is generally not a challenge; it can actually be beneficial as it provides more data for training and testing.

NEW QUESTION # 152
Which statement regarding the use of training, validation, and test data sets is correct?

A. If only limited data is available, validation and test data sets can be combined in multiple ways during training.
B. If limited data is available, it may be better to work without a separate test data set.
C. Optimally, the data should be distributed equally between the training, validation, and test data sets.
D. The data in the test data set must be equivalent to the data in the training data sets and to the data in the validation data sets.

Answer: D

Explanation:
The ISTQB CT-AI syllabus (Section3.2 - Model Evaluation) specifies the correct usage oftraining, validation, andtestdatasets. It emphasizes that the test dataset must be representative of the real operational dataand must beequivalent in distribution to the training and validation sets, ensuring a fair and unbiased evaluation. Option D precisely matches this requirement.

NEW QUESTION # 153
Which statement regarding testing transparency, explainability, or interpretability is MOST correct?

A. Tests for explainability and transparency are comparable to exploratory testing and can be performed with little information about development
B. Dynamic testing is one way to quantify explainability; however, each method is specific to a particular model type
C. Since different users have different backgrounds, interpretability testing depends on the comprehensibility of the ML algorithm
D. LIME can precisely state the decisive reason for a change in the output

Answer: C

Explanation:
The ISTQB CT-AI syllabus states in Section2.10 - Explainability, Transparency, and Interpretability that interpretability isuser-dependent, meaning different users understand explanations differently. This is because interpretability depends not only on the ML algorithm but also on the user's domain knowledge, experience, and expectations. Option B directly reflects this syllabus principle: interpretability testing must consider user background, and explanations must be comprehensible to the intended user group.

NEW QUESTION # 154
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