Test Environments for AI-Based Systems CT-AI Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A data science team is working on a model and wants to ensure that any test result can be reproduced exactly, even months later. They use different versions of the training data, code, and model. What is the best practice to achieve this?

   • AManually note the versions in a shared document.
   • BKeep only the final version of the code and data to avoid confusion.
   • CRely on the model's built-in random seed to ensure reproducibility.
   • DUse version control for code, data, and model artifacts, and record the exact versions used in each test run.
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2. 2foundation · easy

   What is a simulation environment used for in AI testing?

   • ATo store the model's training data
   • BTo monitor the model's performance in production
   • CTo model real-world scenarios that are difficult or dangerous to reproduce in reality
   • DTo compile the model's source code
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4. 3expert · hard

   A government agency is testing an AI model that uses citizen data. The data is highly sensitive and subject to strict compliance requirements. The team needs to test the model with realistic data, but they are concerned about data privacy. They also need to ensure that the test environment is isolated from other systems. What is the best approach?

   • ATest the model in production with real data to avoid duplicating the environment.
   • BUse real data but anonymize it before testing.
   • CUse a dedicated test environment with synthetic data that mimics the statistical properties of the real data, and apply strict access controls.
   • DUse the real data in a shared environment with encryption.
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5. 4expert · hard

   A healthcare AI team is testing a diagnostic model. They have a dataset that is updated daily with new patient records. They need to ensure that test results are reproducible, but they also need to comply with data privacy regulations that require restricting access to the data. What is the best way to manage the data in the test environment?

   • AStore the data in a shared location with open access to all team members.
   • BAllow the dataset to be updated in place and rely on the latest version for all tests.
   • CCreate immutable snapshots of the dataset at regular intervals, version them, and apply strict access controls.
   • DUse only synthetic data to avoid privacy issues, but do not version it.
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6. 5application · medium

   A healthcare startup is developing an AI model that predicts patient readmission risk. They need to test the model against historical patient data that includes sensitive personal health information. The team wants to ensure that test results are not affected by concurrent development work and that only authorized data scientists can access the data. What is the most important first step when setting up the test environment?

   • ASet up a test environment with no access controls to speed up data access for all team members.
   • BCreate a dedicated test environment isolated from the development environment, with role-based access controls on the data.
   • CUse the production environment for testing to ensure the model performs well on real data.
   • DAnonymize the data and store it in a shared development environment to save costs.
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