AWS Certified AI Practitioner Practice Exam

What role does "model monitoring" play in AI systems?

1. A. It provides model explanations during inference.

2. B. It tracks model performance and identifies drift in data.

3. C. It is responsible for the initial training of the model.

4. D. It eliminates the need for data preprocessing.

The correct answer is: B. It tracks model performance and identifies drift in data.

Model monitoring is a critical component of AI systems, as it ensures that models remain effective and relevant over time after deployment. The primary function of model monitoring is to track the performance of the model in real-world scenarios. This involves evaluating various metrics to assess how well the model is functioning, such as accuracy, precision, recall, or any other relevant performance indicators. One of the key aspects of model monitoring is its ability to identify drift in data. Data drift occurs when the statistical properties of the input data change over time, which can adversely affect the model's performance. Monitoring can detect these changes, allowing teams to respond appropriately, whether by retraining the model with new data or adjusting the model's parameters. This proactive approach helps maintain the model's reliability and accuracy, ensuring that predictions remain valid as the input data evolves. In contrast, the other choices focus on aspects of AI that are not directly related to the role of monitoring. Providing model explanations pertains more to interpretability rather than ongoing performance evaluation. The initial training of the model occurs before deployment and is a one-time process, while data preprocessing is a preparatory step that cleans and transforms the input data, rather than a function of ongoing model assessment.