NCA-GENL Question 153
Select 3During the deployment of a large language model (LLM) for a production environment, your team observes a significant increase in latency as the number of user queries grows. Under the supervision of a senior team member, what steps should you take to evaluate and improve the model's scalability, performance, and reliability?
- A
Implement load testing to simulate high user traffic and measure system performance under stress.
- B
Optimize the model by reducing its parameters to improve latency without evaluating its impact on accuracy.
- C
Monitor system logs and performance metrics such as GPU utilization and memory consumption during peak loads.
- D
Collaborate with the team to implement caching mechanisms for frequently used model outputs.
- E
Deploy the model on a single GPU to simplify debugging and reduce deployment complexity.
Show answer and explanation
Correct answers: A, C, D
Explanation
To evaluate and improve a model's scalability, performance, and reliability, it is important to simulate real-world conditions (e.g., load testing), monitor system behavior under stress, and implement strategies like caching to optimize resource utilization. These steps help ensure the model can handle increased demand while maintaining performance and reliability.
- A. Correct.
Load testing is a crucial step to evaluate how the system handles high traffic and ensures it can scale effectively, making it a key part of assessing scalability and performance.
- B. Incorrect.
While parameter reduction can improve latency, doing so without evaluating the impact on accuracy is not a reliable or advisable strategy for maintaining model reliability.
- C. Correct.
Monitoring system metrics like GPU utilization and memory consumption helps identify bottlenecks and ensures the system is performing reliably under stress.
- D. Correct.
Caching frequently used model outputs can significantly reduce computation overhead and improve performance, especially during high traffic conditions.
- E. Incorrect.
Deploying on a single GPU may simplify debugging but does not address scalability or performance issues related to production-level traffic.