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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
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	Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
Topic 5	systems from those required for conventional systems.
Topic 6	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 7	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 8	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 9	ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
Topic 10	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 (Q103-Q108):

NEW QUESTION # 103
Which of the following is a problem with AI-generated test cases that are generated from the requirements?

A. They are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures.
B. They are slow and will usually not be able to execute in the time allowed.
C. They are defect prone because they are unable to detect nuances in the requirements.
D. They make debugging more complicated because the number of steps is usually high in order to induce the target failure.

Answer: A

Explanation:
AI-generated test cases are often created using machine learning (ML) models or heuristic algorithms. While these can be effective in generating large numbers of test cases quickly, they often suffer from the "test oracle problem." Test Oracle Problem: A test oracle is the mechanism used to determine the expected output of a test case. AI-generated test cases often lack expected results because AI-based tools do not inherently understand what the correct output should be.
Difficulty in Verification: Without expected results, verifying test cases becomes challenging.
Testers must rely on heuristics, anomaly detection, or significant failures, rather than traditional pass/fail conditions.

NEW QUESTION # 104
Which ONE of the following statements is MOST true about black box adversarial testing?

A. The training data is manipulated
B. Combinatorial testing techniques are used
C. The approach relies on the transferability of the attacks
D. The algorithm used for the model is known to the tester

Answer: C

Explanation:
The correct answer is B . The CT-AI syllabus explains that black-box adversarial attacks involve exploring the model to determine its functionality and then building a duplicate model with similar functionality. The attacker then uses a white-box approach on the duplicate model to identify adversarial examples. Because adversarial examples are generally transferable, those examples will normally also work on the original model.
Option A describes white-box adversarial testing, where the algorithm, model settings, and parameters are known. Option C describes data poisoning , where training data is manipulated to induce incorrect future predictions or create vulnerabilities. Option D describes pairwise or broader combinatorial testing, which is used where many parameters and values make exhaustive testing impractical. Black-box adversarial testing is instead based on limited knowledge of the target model and the empirical transferability of adversarial examples. If a duplicate model cannot be built, the syllabus also notes that high-volume automated testing may be used to discover adversarial examples and observe results.
References/topics: CT-AI Syllabus Chapter 9, Section 9.1.1 "Adversarial Attacks"; Section 9.1.2 "Data Poisoning."
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NEW QUESTION # 105
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?

A. Run the test several times to generate a statistically valid test result to ensure that an appropriate number of answers are accurate
B. Run the test several times to ensure that the AI always returns the same correct test result
C. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting a sufficient volume of input data
D. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting precise and accurate input data

Answer: A

Explanation:
The syllabus states:
"When testing probabilistic and non-deterministic systems, the same input may produce different outputs.
Tests need to be run several times to produce statistically valid test results, ensuring that an appropriate number of answers are accurate." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.4, page 58 of 99)

NEW QUESTION # 106
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. Check the input test data for potential sample bias.
B. Testing the distribution shift in the training data for inappropriate bias.
C. Test the model during model evaluation for data bias.
D. Testing the data pipeline for any sources for algorithmic 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 # 107
An ML engineer performing supervised learning needs to label images of football games based on the location of the football in the image. Which ONE of the below labeling approaches can be used?

A. Annotation
B. Benchmarking
C. Augmentation
D. Internal

Answer: A

Explanation:
Annotation is the correct labeling approach for supervised learning, as it involves manually labeling the images with the correct information, such as marking the location of the football in the image. This labeled data can then be used to train a machine learning model.

NEW QUESTION # 108
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