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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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 5	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 6	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 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	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 (Q145-Q150):

NEW QUESTION # 145
Which supervised-learning classification/regression statement is correct?

A. Recognizing a dog from many different images is a regression problem
B. Deciding whether an object is a bicycle or a motorcycle is a classification problem
C. In classification, objects are always assigned to exactly two classes
D. Predicting that diesel prices will increase by ~10% is a classification problem

Answer: B

Explanation:
The ISTQB CT-AI syllabus explains supervised learning under Section1.6 - Machine Learning Approaches. It defines classification as predicting categorical labels, where as regression predicts continuous numerical values. OptionB--deciding whether an object is a bicycle or a motorcycle-- fits the definition of classification precisely because the model chooses between discrete categories. The syllabus also uses similar examples to illustrate classification tasks, reinforcing that this is the correct interpretation.

NEW QUESTION # 146
Which ONE of the below statements BEST describes how combinatorial testing can be applied to AI based systems?

A. Two variants of the system can be used and compared
B. Each neuron can be treated as a parameter for pairwise tests
C. Inputs to the system and environment factors can be considered parameters for pairwise tests
D. Combinatorial testing cannot yet be applied to AI

Answer: C

Explanation:
The correct answer is C . The CT-AI syllabus explains that AI-based systems can have extremely high numbers of parameters, especially where they use big data or interact with the external world. Exhaustive testing of all parameter combinations is usually infeasible, so combinatorial techniques, especially pairwise testing, can reduce the number of required test cases while preserving useful defect detection capability.
The syllabus gives autonomous driving as an example, where high-level system tests should consider different operational environments and vehicle functions. Relevant parameters may include road types, surfaces, weather, traffic conditions, visibility, vehicle functions, and sensor effectiveness. This directly supports treating system inputs and environmental factors as parameters in pairwise tests.
Option A is not the intended application; neuron coverage is a neural-network coverage topic, not pairwise system scenario design. Option B describes A/B testing or back-to-back comparison, not combinatorial testing. Option D is false because the syllabus explicitly describes pairwise testing for AI-based systems and includes it as an examinable method.
References/topics: CT-AI Syllabus Chapter 9, Section 9.2 "Pairwise Testing."
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NEW QUESTION # 147
A neural network has been designed and created to assist day-traders improve efficiency when buying and selling commodities in a rapidly changing market. Suppose the test team executes a test on the neural network where each neuron is examined. For this network, the shortest path indicates a "buy" and it will only occur when the one-day predicted value of the commodity is greater than the spot price by 0.75%. The neurons are stimulated by entering commodity prices and testers verify that they activate only when the future value exceeds the spot price by at least 0.75%.
Which of the following statements BEST explains the type of coverage being tested on the neural network?

A. Neuron coverage
B. Sign-change coverage
C. Threshold coverage
D. Value-change coverage

Answer: C

Explanation:
The syllabus details that threshold coverage requires each neuron to achieve an activation value greater than a specified threshold:
"Threshold coverage: Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold." (Reference: ISTQB CT-AI Syllabus v1.0, Section 6.2, page 48 of 99)

NEW QUESTION # 148
You have access to the training data that was used to train an AI-based system. You can review this information and use it as a guideline when creating your tests. What type of characteristic is this?

A. Transparency
B. Accessibility
C. Explorability
D. Autonomy

Answer: A

Explanation:
AI-based systems can sometimes behave like black boxes, where the internal decision-making process is unclear. Transparency refers to the ability to inspect and understand the training data, algorithms, and decision-making process of the AI system.
Transparency ensures that testers and stakeholders can review how an AI system was trained.
Access to training data is a key factor in transparency because it allows testers to analyze biases, completeness, and representativeness of the dataset.
Transparency is an essential characteristic of explainable AI (XAI). Having access to training data means that testers can investigate how data influences AI behavior.
Regulatory and ethical AI guidelines emphasize transparency. Many AI ethics frameworks, such as GDPR and Trustworthy AI guidelines, recommend transparency to ensure fair and explainable AI decision-making.

NEW QUESTION # 149
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 ensure that the AI always returns the same correct test result.
B. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting a sufficient volume of input data.
C. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting precise and accurate input data.
D. Run the test several times to generate a statistically valid test result to ensure that an appropriate number of answers are accurate.

Answer: D

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 # 150
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