NCA-GENL Question 179
Select 3You are managing a generative AI experiment where the training data is being collected in real-time from multiple sources. During the process, you notice a drop in the model's performance metrics. Which steps should you take to monitor and address potential issues with the data collection process?
- A
Check for data distribution drift by analyzing the characteristics of newly collected data compared to the original dataset.
- B
Immediately stop the data collection process to prevent further performance degradation.
- C
Examine logging and monitoring tools to identify anomalies or errors in the data collection pipeline.
- D
Run a validation test to confirm if the drop in performance is related to the new data or other factors.
- E
Increase the size of the training dataset by continuing to collect more data without performing any checks.
Show answer and explanation
Correct answers: A, C, D
Explanation
When monitoring the functioning of data collection and experiments, it is essential to investigate potential issues thoroughly rather than taking drastic actions without evidence. Data distribution drift, pipeline anomalies, and performance validation are key areas to examine to ensure the integrity and utility of the data collected. This approach ensures that any performance degradation is addressed systematically and effectively.
- A. Correct.
Analyzing data distribution drift is critical for identifying whether the new data differs significantly from the original dataset, which could affect model performance.
- B. Incorrect.
Stopping the data collection process immediately without investigation may cause unnecessary delays and does not address the root cause of the problem.
- C. Correct.
Using logging and monitoring tools to identify anomalies ensures that issues in the data collection pipeline, such as missing or corrupted data, are detected and resolved.
- D. Correct.
Running a validation test helps determine whether the performance drop is linked to new data or other factors, like model architecture or hyperparameter settings.
- E. Incorrect.
Blindly increasing the dataset size without analysis can exacerbate the problem and does not address underlying issues with data quality or relevance.