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| Section | Objectives |
|---|---|
| Topic 1: Ethics and Risk in AI Testing | - Risk-based testing for AI
|
| Topic 2: Data Quality and Bias | - Data quality assurance
|
| Topic 3: Testing AI-Based Systems | - Test design techniques
|
| Topic 4: Machine Learning Fundamentals for Testing | - Model types
|
| Topic 5: Introduction to AI Testing | - AI systems overview
|
| Topic 6: AI System Lifecycle and Operations | - Deployment and monitoring
|
| Topic 7: AI Quality Characteristics | - Quality attributes
|
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NEW QUESTION # 160
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?
Answer: B
Explanation:
The syllabus explains:
"Where the internal structure of an AI-based system is too complex for humans to understand, the system can only be tested as a black box. Even when the internal structure is visible, this provides no additional useful information to help with testing." This confirms that black-box testing is chosen because the tester does not need to understand the system's internal structure.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 8.5, page 61 of 99)
NEW QUESTION # 161
Which of the following statements about explainable AI is correct?
Answer: B
Explanation:
Section2.10 - Explainability and Transparency of the ISTQB CT-AI syllabus describes explain able AI as the ability of a system to provide human-understandable insight into its decisions. The syllabus references The Royal Society's reportas a foundational source explaining why explainability is important. Among the stated motivations is the need toincrease user trust and confidencein AI systems by making their decisions understandable and justifiable. Therefore, Option C directly reflects the syllabus content .
NEW QUESTION # 162
Which ONE of the following is a factor associated with the test data that can create challenges specific to testing AI based systems?
Answer: A
Explanation:
The correct answer is B . The CT-AI syllabus identifies several challenges associated with test data for AI- based systems. One important challenge is that if testers use the same implementation as the data scientists for data acquisition and data pre-processing, defects in those steps may be masked.
Therefore, obtaining test data that has not already been pre-processed by the system under test is important when the purpose is to test the data pipeline and reveal possible defects in acquisition, transformation, cleaning, feature preparation, or formatting. Option A is not the best answer because public benchmark datasets can be useful for comparison or initial evaluation, but using them is not inherently a test- data challenge. Option C may be impractical for high-volume AI data, but it is not the most syllabus-specific risk stated here. Option D may raise privacy or representativeness concerns, but it is less precise than the data- pipeline masking issue. The core testing risk is that pre-processed data can bypass the very processing steps whose quality the test should evaluate.
References/topics: CT-AI Syllabus Chapter 7, Section 7.3 "Test Data for Testing AI-Based Systems"; Section 7.2.1 "Input Data Testing."
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NEW QUESTION # 163
Which AI-specific test objective and acceptance criterion should be selected MOST LIKELY for testing GPT_Legal?
Answer: B
Explanation:
The ISTQB CT-AI syllabus introduces AI-specific quality characteristics, including evolution, functional safety, compatibility, andbias-related data quality. Section5.1 - AI-Specific Test Objectives explains that evolution refers to an AI system's capability to continue improving or at least maintain performance as it undergoes additional training. GPT_Legal is explicitly described as aself-learning systemexpected to:
continuously reduce false positives,
achieve weekly accuracy improvements of 10%,
reach and maintain 90% accuracy,
adapt to new environments (patent law firm -> corporate legal department).
This aligns perfectly with the syllabus definition ofevidence of evolution: ensuring the model doesnot degradeas additional training data is introduced. Option B therefore directly supports the described acceptance criteria for this evolving, self-learning application.
NEW QUESTION # 164
Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?
SELECT ONE OPTION
Answer: B
Explanation:
* A. Using a pixel comparison of the GUI before and after the change to check the differences.
Pixel comparison is a traditional method and does not involve AI . It compares images at the pixel level, which can be effective but is not an intelligent approach. It is not considered an AI usage and is the least likely usage of AI for detecting GUI changes.
* B. Using computer vision to compare the GUI before and after the test object changes.
Computer vision involves using AI techniques to interpret and process images. It is a likely usage of AI for detecting changes in the GUI .
* C. Using vision-based detection of the GUI layout changes before and after test object changes.
Vision-based detection is another AI technique where the layout and structure of the GUI are analyzed to detect changes. This is a typical application of AI .
* D. Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.
An ML-based classifier can intelligently determine significant changes and decide if they need human review, which is a sophisticated AI application.
NEW QUESTION # 165
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