You are assisting in a project that involves deploying a large-scale AI model on a multi-GPU server. The server is experiencing unexpected performance degradation during inference, and you have been asked to analyze the system under the supervision of a senior engineer. Which approach would be most effective in identifying the source of the performance degradation?

• A.Monitor the system's power supply levels.
• B.Check the system's CPU utilization.
• C.Analyze the GPU memory usage using nvidia-smi.
• D.Inspect the training data for inconsistencies.

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Why is explainability critical in safety-critical AI systems?

• A.It improves training speed
• B.It reduces model size
• C.It supports trust, accountability, and risk control
• D.It guarantees perfect accuracy

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You are working with a large healthcare dataset containing millions of patient records. Your goal is to identify patterns and extract actionable insights that could improve patient outcomes. The dataset is highly dimensional, with numerous variables, and requires significant processing power to analyze effectively.
Which two techniques are most suitable for extracting meaningful insights from this large, complex dataset?
(Select two)

• A.SMOTE (Synthetic Minority Over-sampling Technique)
• B.Data Augmentation
• C.Batch Normalization
• D.K-means Clustering
• E.Dimensionality Reduction (e.g., PCA)

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