CT-AI Valid Test Experience, Questions CT-AI Exam

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ISTQB CT-AI Exam Syllabus Topics:

Section	Objectives
AI System Lifecycle and Operations	- Continuous learning systems 1. Retraining strategies 2. Model drift detection - Deployment and monitoring 1. Post-deployment monitoring 2. Model deployment strategies
Data Quality and Bias	- Data quality assurance 1. Data labeling quality 2. Data completeness and consistency - Bias and fairness 1. Fairness testing approaches 2. Types of bias in AI systems
Testing AI-Based Systems	- Test design techniques 1. Data-driven test design 2. Metamorphic testing - Test levels for AI systems 1. System integration testing 2. Model testing
AI Quality Characteristics	- Quality attributes 1. Explainability and transparency 2. Accuracy and robustness
Machine Learning Fundamentals for Testing	- ML lifecycle 1. Training, validation, and evaluation 2. Data collection and preparation - Model types 1. Supervised and unsupervised learning 2. Deep learning basics
Ethics and Risk in AI Testing	- Ethical considerations 1. Privacy and security concerns 2. Responsible AI principles - Risk-based testing for AI 1. Risk identification in AI systems 2. Mitigation strategies
Introduction to AI Testing	- AI systems overview 1. Differences between traditional and AI-based systems 2. What is AI and machine learning systems - Challenges in AI testing 1. Non-determinism in AI systems 2. Data dependency issues

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ISTQB CT-AI Exam Questions - Failure Will Result In A Refund

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q139-Q144):

NEW QUESTION # 139
Which ONE of the following statements about the hardware used to implement ML systems is MOST likely to be correct?

A. Specialist hardware is necessary for ML
B. A higher clock speed is required
C. Support for complex operations is required
D. Less bits are required for hardware supporting ML

Answer: C

Explanation:
ML systems often require hardware that supports complex operations, such as matrix multiplications and other linear algebra computations. These operations are fundamental to many machine learning algorithms, particularly in deep learning. Specialist hardware (e.g., GPUs) may be used for efficiency, but complex operations are the core requirement.

NEW QUESTION # 140
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 systems do not always produce the same output for identical inputs. This makes traditional testing approaches ineffective. Instead, the best approach is to run 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.

NEW QUESTION # 141
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. Expected results may not be available for AI-generated functional test cases.
D. AI-generated functional test cases typically result in poor requirements coverage.

Answer: C

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 # 142
Which of the following statements regarding experience-based testing for AI-based systems is correct?

A. In checklist-based testing of AI-based systems, the existing test cases are dynamically adapted, for example based on metamorphic testing.
B. Exploratory testing is often used for AI-based systems because there are often insufficient specifications or problems with the test oracle for AI-based systems.
C. Tour refers to intuitive test case design for AI-based systems based on multiple, sequential test cases using systematically biased training data.
D. Intuitive test case design for AI-based systems involves interactive, hypothesis-driven examination of data for correlations or developmental trends.

Answer: B

Explanation:
The ISTQB CT-AI syllabus explains inSection 4.4 - Experience-Based Testing for AI Systemsthat AI-based systems frequently suffer frominsufficient specifications, unpredictable model behavior, andtest oracle problems, especially when outputs depend on probabilistic or learned patterns.
The syllabus explicitly states thatexploratory testingis especially valuable in such contexts because it allows testers to investigate the system interactively, observe unexpected behavior, and evaluate system responses that cannot be fully predicted beforehand. Thus, Option C accurately reflects the role and justification of exploratory testing for AI systems.

NEW QUESTION # 143
While measuring the test coverage of a neural network, a test engineer wants to measure the number of neurons that have each output two activation function results with a minimum difference between the two results of 0.5.
Which ONE of the below coverage measures would achieve that goal?

A. Value-change coverage
B. Sign-change coverage
C. Neuron coverage
D. None of the coverage measures

Answer: A

Explanation:
Value-change coverage is a coverage measure that focuses on tracking the changes in the values of a neuron's output. In this case, the test engineer is interested in measuring the number of neurons that exhibit a difference of 0.5 between two activation function results, which is effectively a value change. This coverage measure ensures that the neuron undergoes a significant change in its output, fulfilling the engineer's goal.

NEW QUESTION # 144
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