NCA-GENL Question 154
Select 3You are assisting in the deployment of a large language model (LLM) on a cloud-based platform. The team notices that as the number of users increases, the model's response time degrades significantly. Under the supervision of a senior team member, what steps should you take to evaluate the model's scalability and address the issue?
- A
Simulate increased traffic loads using a benchmarking tool to measure response times under stress.
- B
Optimize the model's architecture by reducing the number of layers without consulting the senior team member.
- C
Review the deployment's infrastructure to ensure adequate GPU resources and horizontal scaling capabilities.
- D
Conduct error analysis to identify if the degraded response times are due to incorrect model predictions.
- E
Monitor GPU utilization and memory usage during peak traffic to identify bottlenecks.
Show answer and explanation
Correct answers: A, C, E
Explanation
Evaluating model scalability, performance, and reliability involves simulating traffic, reviewing infrastructure adequacy, and identifying bottlenecks through resource monitoring. These steps help ensure the deployment can handle increased usage without significant performance degradation.
- A. Correct.
Simulating increased traffic loads using benchmarking tools helps determine how the model performs under varying levels of demand and is a critical step in evaluating scalability.
- B. Incorrect.
Optimizing the model's architecture without consulting a senior team member is outside the scope of assisting and can lead to unintended consequences.
- C. Correct.
Reviewing the deployment's infrastructure is essential to ensure that the system can scale horizontally and has adequate GPU resources to handle increased demand.
- D. Incorrect.
While error analysis is useful for understanding model accuracy, it does not directly address response time degradation caused by scalability issues.
- E. Correct.
Monitoring GPU utilization and memory usage during peak traffic provides insights into potential system bottlenecks, which is vital for evaluating performance and reliability.