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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
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	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 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	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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
Which of the following is a technique used in machine learning?

A. Decision tables
B. Decision trees
C. Equivalence partitioning
D. Boundary value analysis

Answer: B

Explanation:
Decision trees are a foundational algorithm used in supervised machine learning. The syllabus describes:
"A decision tree is a tree-like ML model whose nodes represent decisions and whose branches represent possible outcomes." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.4)

NEW QUESTION # 21
A team of software testers is attempting to create an AI algorithm to assist in software testing. This particular team has gone through over 40 iterations of testing and cannot afford to spend as much time as it takes to run the full regression test suite. They are hoping to have the algorithm reduce the amount of testing required, thus reducing the time needed for each testing cycle.
How can an AI-based tool be expected to assist in this reduction?

A. By performing optimization of the data from past iterations to see where the most common defects occurred and select the corresponding test cases
B. By performing Bayesian analysis to estimate the types of human interactions that are expected to be seen in the system and then selecting those test cases
C. By using A/B testing to compare the last update with the newest change and compare metrics between the two
D. By using a clustering method to quantify the relationships between test cases and then assigning each test case to a category

Answer: A

Explanation:
The syllabus mentions that AI can help optimize regression test suites:
"An AI-based tool can perform optimization of the regression test suite by analyzing... the information from previous test results, associated defects, and the latest changes that have been made, such as features which are broken more frequently and which tests exercise code impacted by recent changes." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.4, page 79 of 99)

NEW QUESTION # 22
You have been developing test automation for an e-commerce system. One of the problems you are seeing is that object recognition in the GUI is having frequent failures. You have determined this is because the developers are changing the identifiers when they make code updates.
How could AI help make the automation more reliable?

A. It could identify the objects multiple ways and then determine the most commonly used and stable identification for each object.
B. It could modify the automation code to ignore unrecognizable objects to avoid failures.
C. It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly.
D. It could dynamically name the objects, altering the source code, so the object names will match the object names used in the automation.

Answer: A

NEW QUESTION # 23
A team of software testers is attempting to create an AI algorithm to assist in software testing. This particular team has gone through over 40 iterations of testing and cannot afford to spend as much time as it takes to run the full regression test suite. They are hoping to have the algorithm reduce the amount of testing required thus reducing the time needed for each testing cycle.
How can an AI-based tool be expected to assist in this reduction?

A. By performing optimization of the data from past iterations to see where the most common defects occurred and select the corresponding test cases
B. By performing bayesian analysis to estimate the types of human interactions that are expected to be seen in the system and then selecting those test cases
C. By using A/B testing to compare the last update with the newest change and compare metrics between the two
D. By using a clustering method to quantify the relationships between test cases and then assigning each test case to a category

Answer: A

Explanation:
AI-based tools can significantly optimize regression test suites by analyzing historical data, past test results, associated defects, and changes made to the software. These tools prioritize and select the most relevant test cases based on previous defect patterns and frequently failing features, which helps in reducing the test execution time while maintaining effectiveness.
The optimization process involves:
* Prioritizing test cases:AI-based tools rank test cases based on past defect detection trends, ensuring that the most relevant tests are executed first.
* Reducing redundant test cases:The tool can eliminate test cases that do not contribute significantly to defect detection, reducing overall test execution time.
* Augmenting test cases:The AI can also suggest new test cases if certain features are more prone to defects.
This approach has been proven to reduce regression test suite sizes by up to 50% while maintaining fault detection capabilities.
* Section 11.4 - Using AI for the Optimization of Regression Test Suitesstates that AI-based tools can optimize regression test suites by analyzing past test data and defect occurrences, leading to significant reductions in test execution time.
Reference from ISTQB Certified Tester AI Testing Study Guide:

NEW QUESTION # 24
Which of the following aspects is a challenge when handling test data for an AI-based system?

A. Output data or intermediate data
B. Personal data or confidential data
C. Data frameworks or machine learning frameworks
D. Video frame speed or aspect ratio

Answer: B

Explanation:
The syllabus explicitly mentions challenges of handling personal data and ensuring privacy when testing AI- based systems:
"The management of personal data and sensitive data is often a concern during testing, as testing typically requires realistic data and it is difficult to fully anonymize data." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.3, page 52 of 99)

NEW QUESTION # 25
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CT-AI New Test Materials - Latest CT-AI Test Prep

ISTQB CT-AI Exam Syllabus Topics:

Latest CT-AI Test Prep - Simulated CT-AI Test

ISTQB Certified Tester AI Testing Exam Sample Questions (Q20-Q25):

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