PASS GUARANTEED ISTQB - AUTHORITATIVE CT-AI - CERTIFIED TESTER AI TESTING EXAM VCE EXAM SIMULATOR

Pass Guaranteed ISTQB - Authoritative CT-AI - Certified Tester AI Testing Exam VCE Exam Simulator

Pass Guaranteed ISTQB - Authoritative CT-AI - Certified Tester AI Testing Exam VCE Exam Simulator

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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 3
  • 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 4
  • systems from those required for conventional systems.
Topic 5
  • 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 6
  • 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 7
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 8
  • 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.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q44-Q49):

NEW QUESTION # 44
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 dynamically name the objects, altering the source code, so the object names will match the object names used in the automation
  • B. It could identify the objects multiple ways and then determine the most commonly used and stable identification for each object
  • C. It could modify the automation code to ignore unrecognizable objects to avoid failures
  • D. It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly

Answer: B

Explanation:
The syllabus discusses using AI-based tools to reduce GUI test brittleness:
"AI can be used to reduce the brittleness of this approach, by employing AI-based tools to identify the correct objects using various criteria (e.g., XPath, label, id, class, X/Y coordinates), and to choose the historically most stable identification criteria." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.6.1)


NEW QUESTION # 45
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?

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

Answer: B

Explanation:
Probabilistic and non-deterministic AI-based systemsdo not always produce the same output for identical inputs. This makes traditional testing approaches ineffective. Instead, the best approach is torun tests multiple times and analyze results statistically.
* Statistical Validity:Running tests multiple times ensures that observed results are statistically significant. Instead of relying on a single test run,analyzing multiple iterations helps determine trends, probabilities, and outliers.
* Expected Result Tolerance:AI-based systems may produce different results within an acceptable range. Defining acceptable tolerances (e.g., "result must be within 2% of the optimal value") improves test effectiveness.
* A (Run Several Times for the Same Correct Result):AI systems are ofteninherently non- deterministicand may not return the exact same result every time. Expecting identical outputs contradicts the nature of these systems.
* B & C (Decomposing Tests into Data Ingestion Tests):While data ingestion quality is important, it does notdirectlysolve the issue of probabilistic test results. Statistical analysis is the key approach.
* ISTQB CT-AI Syllabus (Section 8.4: Challenges Testing Probabilistic and Non-Deterministic AI- Based Systems)
* "For probabilistic systems, running a test multiple times may be necessary to obtain a statistically valid test result.".
* "Where a single definitive output is not possible, results should be analyzed statistically rather than relying on individual test cases.".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Sinceprobabilistic AI systems do not always return the same result, the best approach is torun multiple test iterations and validate results statistically. Hence, thecorrect answer is D.


NEW QUESTION # 46
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 using a clustering method to quantify the relationships between test cases and then assigning each test case to a category
  • C. By using A/B testing to compare the last update with the newest change and compare metrics between the two
  • D. 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

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 # 47
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer). A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I . Pairwise testing of combinations
II . Testing each individual model for accuracy
III . A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION

  • A. Only II
  • B. Only III
  • C. I and II
  • D. I and III

Answer: C

Explanation:
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
Reference:
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.


NEW QUESTION # 48
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?

  • A. Classifying muffin purchases based on the perceived attractiveness of their packaging
  • B. Grouping individual fish together based on their types of fins
  • C. Estimating the expected purchase of cat food after a particularly successful ad campaign
  • D. Associating shoppers with their shopping tendencies

Answer: D

Explanation:
The syllabus defines clustering as:
"Clustering: This is when the problem requires the identification of similarities in input data points that allows them to be grouped based on common characteristics or attributes. For example, clustering is used to categorize different types of customers for the purpose of marketing." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.2, page 26 of 99)


NEW QUESTION # 49
......

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