Important iSQI CT-AI Exam Questions

Answer: B

Explanation:

From the confusion matrix:

True Positives (TP) = 15

False Positives (FP) = 5

False Negatives (FN) = 15

True Negatives (TN) = 65

Accuracy= (TP + TN) / Total

= (15 + 65) / 100

=80%

Precision= TP / (TP + FP)

= 15 / (15 + 5)

= 15 / 20

=75%

Section3.2 -- Functional Performance Criteriain the syllabus explains accuracy and precision exactly these ways when evaluating ML classification performance.

Option B is therefore the only correct pair of values.

Answer: C

Explanation:

The ISTQB CT-AI syllabus describes theML data preparation workflowin Section2.2 -- Data Preparation. Data preparation consists ofdata gathering,cleaning,transformation, andsampling. The syllabus emphasizes that one significant challenge duringdata gatheringis combining data frommultiple heterogeneous sources, which often differ in structure, quality, and format. Ensuring the resulting dataset is accurate, complete, and representative can be complex, making this a critical challenge in the ML workflow. This aligns directly with OptionC.

Option A is incorrect because erroneous data correction is part ofcleaning, not transformation. Option B contradicts the syllabus: while automation can help,not all steps should be automateddue to the need for expert oversight, especially in detecting subtle data quality issues. Option D is incorrect because sampling continues to involve risk---particularly around representativeness---and the syllabus emphasizes caution, not complacency.

Thus, OptionCis the only statement that accurately reflects the syllabus.

Answer: D

Explanation:

The syllabus explains:

'Where the internal structure of an AI-based system is too complex for humans to understand, the system can only be tested as a black box. Even when the internal structure is visible, this provides no additional useful information to help with testing.'

This confirms that black-box testing is chosen because the tester does not need to understand the system's internal structure.

(Reference: ISTQB CT-AI Syllabus v1.0, Section 8.5, page 61 of 99)

Answer: B

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 isB. Test the model during model evaluation for data bias.

:

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.

Answer: C

Explanation:

Section4.2 -- Test Coverage Criteria for AI Modelsof the ISTQB CT-AI syllabus describes neural network--specific coverage methods. Among the techniques,threshold coverageis explicitly noted asadaptable, meaning testers may choose different thresholds to determine whether neuron activation is considered ''covered.'' This flexibility makes threshold coverage adjustable to the model architecture, problem domain, and required test thoroughness.

Options A and B (Sign--Sign and Sign--Change coverage) are more rigid structural criteria and are not described as adaptable within the syllabus. They focus on sign patterns of neuron activations and do not allow altering thresholds. Option D, neuron coverage, measures the proportion of neurons activated at least once. Although simple, it is not defined as an adaptable criterion. Its limitations are documented: it provides shallow insight and too easily achieves high coverage.

Onlythreshold coverageallows testers to adjust activation thresholds for more refined coverage measurement, makingOption Cthe correct choice.