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| Section | Objectives |
|---|---|
| Topic 1: Testing AI-Based Systems | - Test levels for AI systems
|
| Topic 2: Ethics and Risk in AI Testing | - Ethical considerations
|
| Topic 3: AI System Lifecycle and Operations | - Continuous learning systems
|
| Topic 4: Data Quality and Bias | - Bias and fairness
|
| Topic 5: Machine Learning Fundamentals for Testing | - Model types
|
| Topic 6: AI Quality Characteristics | - Quality attributes
|
| Topic 7: Introduction to AI Testing | - AI systems overview
|
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NEW QUESTION # 71
"BioSearch" is creating an Al model used for predicting cancer occurrence via examining X-Ray images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model.
A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.
Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert?
SELECT ONE OPTION
Answer: D
Explanation:
The question asks which deficiency is most likely to be discovered by the test expert given the scenario of poor real-world performance despite good isolated accuracy.
A lack of similarity between the training and testing data (A): This is a common issue in ML where the model performs well on training data but poorly on real-world data due to a lack of representativeness in the training data. This leads to poor generalization to new, unseen data.
The input data has not been tested for quality prior to use for testing (B): While data quality is important, this option is less likely to be the primary reason for the described issue compared to the representativeness of training data.
A lack of focus on choosing the right functional-performance metrics (C): Proper metrics are crucial, but the issue described seems more related to the data mismatch rather than metric selection.
A lack of focus on non-functional requirements testing (D): Non-functional requirements are important, but the scenario specifically mentions issues with detecting real cancer cases, pointing more towards data issues.
ISTQB CT-AI Syllabus Section 4.2 on Training, Validation, and Test Datasets emphasizes the importance of using representative datasets to ensure the model generalizes well to real-world data.
Sample Exam Questions document, Question #40 addresses issues related to data representativeness and model generalization.
NEW QUESTION # 72
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: A
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.
NEW QUESTION # 73
Which ONE of the following statements BEST describes how system complexity can cause challenges when testing an AI-based system?
Answer: A
Explanation:
Unexpected changes in system behavior can occur due to the complexity of AI-based systems.
These systems often involve many interacting components, which can lead to unpredictable results or variations in performance, making it difficult to anticipate how the system will behave under certain conditions. This presents a significant challenge in testing, as such behavior can be difficult to reproduce or control.
NEW QUESTION # 74
Which of the following is an example of an input change where it would be expected that the AI system should be able to adapt?
Answer: C
Explanation:
The syllabus explains that input changes that arein the same domainas what was used for training are expected to be handled with adaptability:
"Adaptability refers to the ability of a system to adjust its behavior in response to changes in its environment or inputs. This includes changes to the inputs which are still within the expected operational range of the system, such as resolution changes in images or sensor data." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.2)
NEW QUESTION # 75
Which data-labeling approach uses a two-step process where labeling is first done by a tool and then verified or completed by a human?
Choose ONE option (1 out of 4)
Answer: A
Explanation:
Section2.4 - Data Labeling Approachesof the ISTQB CT-AI syllabus explicitly definesAI-assisted data labelingas a hybrid process in which an automated tool performs the initial labeling and human annotators subsequently verify, correct, or complete the labels. This two-step process improves efficiency while retaining human oversight to ensure data quality. The syllabus describes this method as an effective compromise when manual labeling alone would be too slow or costly, and when initial automation can identify obvious patterns before a human provides the final authoritative labels .
Option A (internal labeling) refers to labeling conducted by the organization's own staff but does not imply automation. Option B (crowdsourced labeling) leverages a distributed workforce, typically without automation. Option C (outsourced labeling) transfers labeling tasks to external vendors but similarly does not involve an AI-first step. Only OptionDreflects the two-stage automated-then-human workflow described in the syllabus.
Therefore,AI-assisted data labeling(Option D) is unequivocally correct.
NEW QUESTION # 76
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