Test Objectives and Acceptance Criteria CT-AI Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A social media platform is testing an AI content moderation system. The legal team requires that the system not disproportionately flag content from minority groups, and the user experience team requires that flagged content be reviewed quickly. Which test objective best addresses both requirements?

   • AVerify that the model's training data includes content from all demographic groups.
   • BMeasure the overall precision of the moderation system on a random sample of content.
   • CMeasure the false positive rate across different demographic groups and verify that the average review time for flagged content is under 30 minutes.
   • DConfirm that the system can process at least 1,000 posts per second.
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2. 7foundation · easy

   Which acceptance criterion is appropriately derived from the functional requirement 'the AI system shall classify images into one of 10 predefined categories'?

   • AThe system must process images in less than 1 second
   • BThe system must be able to explain its classification decisions
   • CThe system must be able to classify images accurately
   • DThe system must classify at least 95% of test images into the correct one of the 10 predefined categories
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4. 8foundation · easy

   Which of the following is an example of a testable and unambiguous acceptance criterion for an AI system?

   • AThe system should process requests quickly
   • BThe system should work in most cases
   • CThe system should be user-friendly and easy to use
   • DThe system should achieve a mean average precision (mAP) of at least 0.85 on the test set
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5. 9application · medium

   An e-commerce company uses an AI recommendation system to suggest products. The marketing team wants to increase sales, while the customer service team is concerned about user trust. The test lead must define test objectives that align with AI quality characteristics. Which set of test objectives best balances these concerns?

   • AMeasure the model's training time and the size of the training dataset.
   • BMeasure the model's inference latency and the number of recommendations displayed per page.
   • CVerify that the model's code is well-documented and follows coding standards.
   • DMeasure the click-through rate of recommendations and verify that recommendations are not biased against any product category.
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6. 10expert · hard

   A team is documenting test objectives and acceptance criteria for an AI system that personalizes news feeds. The product manager wants to track user engagement, and the ethics board requires transparency about how content is selected. The test lead must document the objectives to support both needs. Which documentation approach best supports this?

   • AStore the test objectives in a shared spreadsheet without any links to quality characteristics.
   • BDocument only the acceptance criteria in a test case management tool.
   • CWrite a narrative description of the system's features in a wiki page.
   • DDocument each test objective with its associated AI quality characteristics (e.g., engagement, transparency) and link each to specific acceptance criteria.
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