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NEW QUESTION # 140
You are conducting a user acceptance test of a decision support recommendation system used in a data processing business. In addition to testing the functional performance of the recommendation systems, what else would you be MOST likely to test?
Answer: B
Explanation:
The correct answer is D . A decision support recommendation system does not merely produce predictions; it influences human decision-making. The CT-AI syllabus explains that automation bias, also called complacency bias, occurs when humans become too trusting of automated recommendations. One form occurs when a human accepts system recommendations and fails to consider other sources, including their own judgment.
For that reason, testing should cover both the quality of the system's recommendations and the quality of the corresponding human input provided by representative users. The syllabus explicitly states that testers should understand how human decision-making may be compromised and should test both the system recommendations and the human input.
Options A and C are still functional performance measures of the recommendation system. Option B is a performance efficiency concern and may be relevant at system testing, but it does not address the decision- support risk. Option D directly targets the critical acceptance concern: whether users can detect and appropriately handle inaccurate AI recommendations rather than blindly following them.
References/topics: CT-AI Syllabus Chapter 7, Section 7.4 "Testing for Automation Bias in AI-Based Systems"; Section 7.2.6 "Acceptance Testing."
NEW QUESTION # 141
Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
Answer: A
Explanation:
Factors Affecting ML Functional Performance: The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models.
The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.
NEW QUESTION # 142
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION
Answer: D
Explanation:
Flexibility in AI systems is crucial for various reasons, particularly because it allows for easier modification and adaptation of the system as a whole.
AI systems are inherently flexible (A): This statement is not correct. While some AI systems may be designed to be flexible, they are not inherently flexible by nature. Flexibility depends on the system's design and implementation.
AI systems require changing operational environments; therefore, flexibility is required (B): While it's true that AI systems may need to operate in changing environments, this statement does not directly address the importance of flexibility for the modification of the system.
Flexible AI systems allow for easier modification of the system as a whole (C): This statement correctly describes the importance of flexibility. Being able to modify AI systems easily is critical for their maintenance, adaptation to new requirements, and improvement.
Self-learning systems are expected to deal with new situations without explicitly having to program for it (D):
This statement relates to the adaptability of self-learning systems rather than their overall flexibility for modification.
Hence, the correct answer isC. Flexible AI systems allow for easier modification of the system as a whole.
ISTQB CT-AI Syllabus Section 2.1 on Flexibility and Adaptability discusses the importance of flexibility in AI systems and how it enables easier modification and adaptability to new situations.
Sample Exam Questions document, Question #30 highlights the importance of flexibility in AI systems.
NEW QUESTION # 143
The stakeholders of a machine learning model have confirmed that they understand the objective and purpose of the model, and ensured that the proposed model aligns with their business priorities. They have also selected a framework and a machine learning model that they will be using. What should be the next step to progress along the machine learning workflow?
Answer: D
Explanation:
The ML workflow typically involves iterative steps, beginning with data preparation once the model and framework are selected. The syllabus explains:
"The steps shown in Figure 1 (the ML workflow) do not include the integration of the ML model with the non-ML parts of the overall system. Typically, ML models cannot be deployed in isolation and need to be integrated with the non-ML parts... The next step would be data preparation as part of the ML workflow to provide input data to support training by an ML algorithm or prediction by an ML model."
NEW QUESTION # 144
Which of the following statements about bias in AI based systems is MOST correct?
Answer: A
Explanation:
The correct answer is D . The CT-AI syllabus explains that ML systems make decisions and predictions using algorithms and collected data , and that both can introduce bias into the results. Algorithmic bias can occur when the learning algorithm is incorrectly configured, for example where it overvalues some data compared with other data. Sample bias can occur when training data is not fully representative of the data space to which ML is applied. The syllabus further states that inappropriate bias is often caused by sample bias, but can also be caused by algorithmic bias.
Option A is too narrow because it describes only one algorithmic mechanism. Option B is also incomplete because it describes sample bias only. Option C is incorrect because the syllabus states that bias can be introduced into many types of AI-based systems, not only ML systems involving personal data. Therefore, the most complete and syllabus-aligned statement is that inappropriate bias can arise from either the algorithm or the data.
References/topics: CT-AI Syllabus Chapter 2, Section 2.4 "Bias."
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NEW QUESTION # 145
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