Virtual Test Environments for Testing AI-Based Systems CT-AI Practice Questions (Page 3)

Questions 11–15

1. 11application · medium

   A team is planning to test an AI-based recommendation system. They want to integrate a virtual test environment into their existing CI/CD pipeline to run automated tests on every code change. What is the most important consideration for this integration?

   • AUse a physical test environment to ensure accuracy.
   • BRun tests only when a human manually triggers them.
   • CAvoid using virtual environments because they are not realistic enough.
   • DEnsure the virtual environment can be provisioned and torn down automatically as part of the pipeline.
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2. 12expert · hard

   A team is training an AI model for facial recognition. They have a large dataset of real faces, but it lacks diversity in age, ethnicity, and lighting conditions. They plan to generate synthetic faces using a simulation environment. What is the most important consideration to avoid introducing bias into the AI model?

   • AGenerate as many synthetic faces as possible, regardless of realism.
   • BRely on the simulation tool's default settings to ensure unbiased data.
   • CUse only synthetic data and discard the real data to ensure consistency.
   • DEnsure the synthetic data generation process is validated to produce realistic and diverse faces that match the target population distribution.
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4. 13expert · hard

   A team has developed a digital twin of a chemical plant to test an AI-based anomaly detection system. The digital twin is based on historical data and expert knowledge. Before relying on the digital twin for safety-critical testing, the team wants to ensure it accurately reflects the plant's behavior. Which approach is most effective?

   • AIncrease the complexity of the digital twin to capture more details.
   • BValidate the digital twin against a set of real-world operational scenarios and compare its outputs with actual plant data.
   • CUse the digital twin only for non-safety-critical testing to avoid risk.
   • DRely on expert opinion to confirm the digital twin is accurate.
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5. 14application · medium

   A team is developing an AI model for medical image analysis. They have a small dataset of real X-ray images, but it is not enough to train a robust model. They decide to generate synthetic X-ray images using a simulation environment. What is a critical consideration when using synthetic data for training?

   • AThe synthetic data should be kept separate from real data during training.
   • BThe synthetic data should be generated in large quantities regardless of quality.
   • CThe synthetic data should be used without any real data to avoid bias.
   • DThe synthetic data must be validated to ensure it is realistic and representative of real medical images.
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6. 15application · medium

   A manufacturing company wants to test an AI-based predictive maintenance system for a specific industrial robot. The robot is in continuous operation and cannot be taken offline for testing. The team needs a virtual replica that reflects the robot's real-time state and can be used to test the AI's responses to various operating conditions. Which approach is most suitable?

   • AWait until the robot is scheduled for maintenance and test the AI on the physical robot then.
   • BUse a simulation environment that models generic industrial robots but not the specific robot.
   • CDevelop a digital twin of the robot that is continuously updated with sensor data from the physical robot.
   • DTest the AI model in a sandboxed software environment without any virtual representation of the robot.
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