AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 142

An ML model is deployed in production. The model has performed well and has met its metric thresholds for months.

An ML engineer who is monitoring the model observes a sudden degradation. The performance metrics of the model are now below the thresholds.

What could be the cause of the performance degradation?

Answer options

• A. Lack of training data
• B. Drift in production data distribution
• C. Compute resource constraints
• D. Model overfitting

Correct answer: B

Explanation

The correct answer is B, as drift in production data distribution can lead to discrepancies between the model's training data and the new incoming data, causing performance issues. Options A, C, and D are less likely causes in this scenario since the model had been performing well previously and has not indicated issues related to insufficient training data, resource constraints, or overfitting.