NCA-GENM Question 135
Select 3You are managing a generative AI multimodal project that requires continuous monitoring of data collection and experiment processes. During a routine check, you notice a significant drop in model performance metrics. Which of the following actions should you take to identify and resolve the issue?
- A
Verify if there have been changes in the data collection pipeline, such as missing or corrupted data.
- B
Check the experiment logs for errors or unexpected behavior during training runs.
- C
Immediately deploy the last stable model version to production without further investigation.
- D
Analyze the distribution of incoming data to confirm it matches the training data characteristics.
- E
Conduct a hyperparameter tuning process to optimize the model's performance without checking input data quality.
Show answer and explanation
Correct answers: A, B, D
Explanation
Monitoring the functioning of data collection, experiments, and other software processes is crucial for maintaining model performance. When performance drops, investigating potential data collection issues, reviewing experiment logs, and analyzing data distributions are essential steps to identify and resolve the root cause. These actions ensure the model is functioning correctly and reduces the risk of deploying a flawed system.
- A. Correct.
Changes in the data collection pipeline, such as missing or corrupted data, can significantly impact model performance. This is a critical step to identify potential issues.
- B. Correct.
Experiment logs often provide detailed insights into errors or unexpected behavior that may have occurred during training. Checking logs is essential for debugging.
- C. Incorrect.
Deploying the last stable model version without investigating the root cause of the issue may temporarily resolve performance problems but does not address the underlying problem.
- D. Correct.
Analyzing the distribution of incoming data ensures that data characteristics align with the training data, helping to identify issues like data drift.
- E. Incorrect.
Hyperparameter tuning can improve model performance but is ineffective if the input data quality is compromised. Root cause analysis should take priority.