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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	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 3	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 4	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
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	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	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
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	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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q66-Q71):

NEW QUESTION # 66
Which of the following aspects is a challenge when handling test data for an AI-based system?

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

Answer: C

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 # 67
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION

A. Non-determinism
B. Self-learning
C. High complexity
D. Robustness

Answer: D

Explanation:
The question asks which characteristic is least likely to cause safety-related issues for an AI system. Let's evaluate each option:
* Non-determinism (A): Non-deterministic systems can produce different outcomes even with the same inputs, which can lead to unpredictable behavior and potential safety issues.
* Robustness (B): Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.
* High complexity (C): High complexity in AI systems can lead to difficulties in understanding, predicting, and managing the system's behavior, which can cause safety-related issues.
* Self-learning (D): Self-learning systems adapt based on new data, which can lead to unexpected changes in behavior. If not properly monitored and controlled, this can result in safety issues.
:
ISTQB CT-AI Syllabus Section 2.8 on Safety and AI discusses various factors affecting the safety of AI systems, emphasizing the importance of robustness in maintaining safe operation.

NEW QUESTION # 68
Consider an AI system in which the complex internal structure has been generated by another software system. Why would the tester choose to do black-box testing on this particular system?

A. Test automation can be built quickly and easily from the test cases developed during black-box testing.
B. Black-box testing eliminates the need for the tester to understand the internal structure of the AI system.
C. The black-box testing method will allow the tester to check the transparency of the algorithm used to create the internal structure.
D. The tester wishes to better understand the logic of the software used to create the internal structure.

Answer: B

Explanation:
In AI-based systems, particularly those where theinternal structure has been generated by another software system, the complexity often makes it difficult for human testers to analyze the inner workings. As per the ISTQB Certified Tester AI Testing (CT-AI) Syllabus:
* Black-box testingis particularly useful when dealing with AI systems that have been generated by another system because:
* It allows testingwithout requiring knowledge of the internal logic.
* The AI model may be too complex for human testers to comprehend, making white-box testing ineffective.
* Black-box testing evaluates theinputs and outputs, ensuring functional correctnesswithout needing insight into how the system reaches a decision.
* Why other options are incorrect?
* A (Test automation and black-box testing): While automation is possible,black-box testing is not primarily about automationbut aboutabstracting the internal complexity.
* B (Understanding the logic of the software): This contradicts the premise of black-box testing, which is designed totest functionality without needing to understandthe inner workings.
* C (Checking transparency of the algorithm):Black-box testing does not check algorithm transparency-that would requirewhite-box testing or explainability techniques.
Thus, the best choice isOption D, as black-box testingremoves the need to analyze the internal structure of AI systems, making it the most appropriate testing method in this case.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 8.5 (Challenges Testing Complex AI-Based Systems)
* ISTQB CT-AI Syllabus v1.0, Section 8.6 (Testing the Transparency, Interpretability, and Explainability of AI-Based Systems)

NEW QUESTION # 69
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?

A. Selecting training data purposely excluding specific faulty conditions
B. Selecting testing data from a different dataset than the training dataset
C. Selecting training data by purposely including all known faulty conditions
D. Selecting testing data from a boat manufacturer's bolt longevity data

Answer: A

Explanation:
The syllabus defines bias as:
"Bias is the systematic difference in treatment of certain objects, people or groups in comparison to others." It also discusses:
"Sample bias can occur if the data used for training the model does not represent the operational environment, or if some relevant faulty conditions are excluded deliberately." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.3)

NEW QUESTION # 70
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?

A. Selecting testing data from a different dataset than the training dataset
B. Selecting training data by purposely including all known faulty conditions
C. Selecting training data by purposely excluding specific faulty conditions
D. Selecting testing data from a boat manufacturer's bolt longevity data

Answer: C

Explanation:
Bias in AI models often originates fromincomplete or non-representative training data. In this case, if the training datasetpurposely excludes specific faulty conditions, the machine learning model willfail to learn and detectthese conditions in real-world scenarios.
This results in:
* Sample bias, where the training data is not fully representative of all possible faulty conditions.
* Algorithmic bias, where the model prioritizes certain defect types while ignoring others.
* B. Selecting training data by purposely including all known faulty conditions# This would help reduce bias by improving model generalization.
* C. Selecting testing data from a different dataset than the training dataset# This is a good practice to evaluate model generalization but does not inherently introduce bias.
* D. Selecting testing data from a boat manufacturer's bolt longevity data# While using unrelated data can createpoor model accuracy, it does not directly introduce bias unless systematic patterns in the incorrect dataset lead to unfair decision-making.
* Section 8.3 - Testing for Algorithmic, Sample, and Inappropriate Biasstates thatsample bias can occur if the training dataset is not fully representative of the expected data space, leading to biased predictions.
Why are the other options incorrect?Reference from ISTQB Certified Tester AI Testing Study Guide:

NEW QUESTION # 71
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