Databricks Generative AI Engineer Associate Question 297
Select 3You have deployed a Retrieval-Augmented Generation (RAG) application on Databricks and are using inference logging to monitor its performance. During the evaluation, you notice that user queries are taking longer to process than expected, and the quality of generated responses is inconsistent. Which steps should you take to assess and improve the performance of your RAG application using inference logging?
- A
Analyze latency metrics captured in inference logs to identify bottlenecks in the retrieval or generation stages.
- B
Examine the inference logs for patterns in user queries to optimize the retrieval component for frequent query types.
- C
Disable inference logging temporarily to reduce overhead on the deployed application and improve latency.
- D
Inspect the logs for low-confidence scores in generated responses and refine the retriever or prompt tuning accordingly.
- E
Focus exclusively on hardware scaling, such as upgrading to a more powerful cluster, to address latency issues.
Show answer and explanation
Correct answers: A, B, D
Explanation
Inference logging provides critical insights into the performance of a deployed RAG application. By analyzing metrics such as latency, query patterns, and confidence scores, you can identify and address bottlenecks, improve retrieval accuracy, and refine the generation process. Disabling logging or solely focusing on hardware scaling are less effective approaches, as they do not address the root causes of performance issues.
- A. Correct.
Analyzing latency metrics in inference logs helps identify which stage (retrieval or generation) is causing delays, enabling targeted optimizations.
- B. Correct.
Examining patterns in user queries from inference logs can reveal common query types, allowing you to optimize the retrieval component for these cases.
- C. Incorrect.
Disabling inference logging would prevent you from collecting critical data needed to diagnose performance issues and is not a recommended step.
- D. Correct.
Low-confidence scores in generated responses can indicate issues with the retriever or prompt tuning, which can be refined based on log data to improve response quality.
- E. Incorrect.
While hardware scaling can help improve performance, it is not the sole or primary solution. A data-driven analysis using inference logs is necessary to address root causes.