NCA-GENL Question 9
Select 3You are assisting in the deployment of a large language model (LLM) for a real-time chatbot application under the supervision of a senior team member. During testing, you notice that the model's response times increase significantly as the number of concurrent users grows. Which actions would best help you evaluate and address the model's scalability concerns?
- A
Conduct load testing to simulate increasing numbers of concurrent users and measure response times.
- B
Optimize the model architecture by reducing the number of layers without consulting the senior team member.
- C
Use a distributed inference framework to balance the workload across multiple GPUs.
- D
Analyze latency metrics and resource utilization during peak loads to identify bottlenecks.
- E
Deploy a smaller version of the model to handle all user requests without further testing.
Show answer and explanation
Correct answers: A, C, D
Explanation
Scalability testing and optimization are critical steps when deploying LLMs for applications with varying traffic levels. Conducting load testing, using a distributed inference framework, and analyzing latency metrics are all standard practices for assessing and improving scalability under the supervision of senior team members. These actions ensure the system can handle high traffic without compromising on performance or reliability, aligning with best practices in LLM deployment.
- A. Correct.
Conducting load testing is a crucial step to simulate real-world usage patterns and evaluate how the system behaves under different levels of traffic. This provides data to identify scalability limitations.
- B. Incorrect.
Optimizing the model architecture without consulting the senior team member could lead to unintended consequences, such as degraded model performance. This is not an appropriate action for an associate-level role.
- C. Correct.
Using a distributed inference framework can help improve scalability by distributing computational workloads across multiple GPUs, which reduces latency under heavy traffic.
- D. Correct.
Analyzing latency metrics and resource utilization helps pinpoint specific bottlenecks, such as hardware limits or inefficient resource allocation, which are critical for addressing scalability issues.
- E. Incorrect.
Deploying a smaller model without further testing might alleviate some scalability concerns but risks sacrificing the quality of the responses. Additionally, skipping testing is not recommended in a professional deployment process.