NCA-GENL exam dumps

NCA-GENL practice question 11 of 228

NVIDIA-Certified Associate - Generative AI LLMs. Associate level, NVIDIA. Free question with the correct answer and a full explanation.

NCA-GENL Question 11

Select 3

You are part of a team deploying a large language model (LLM) for a conversational AI application. The senior engineer instructs you to evaluate the model's scalability and performance during peak usage. Which steps should you prioritize to assist in this task?

  1. A

    Simulate concurrent user requests to stress-test the system.

  2. B

    Manually inspect the model's architecture for potential improvements.

  3. C

    Monitor GPU utilization and memory usage during the tests.

  4. D

    Compare inference latency under different batch sizes.

  5. E

    Optimize hyperparameters to improve training accuracy.

Show answer and explanation

Correct answers: A, C, D

Explanation

To assist in evaluating model scalability and performance, it is essential to focus on testing the system under stress (e.g., simulating concurrent requests), monitoring resource utilization (e.g., GPU and memory), and analyzing performance metrics (e.g., inference latency). These steps provide critical data for assessing how well the model can handle real-world deployment scenarios.

  • A. Correct.

    Simulating concurrent user requests helps evaluate how the model performs under high load, making it a critical step for assessing scalability and performance.

  • B. Incorrect.

    Manually inspecting the model's architecture is not directly related to evaluating scalability or performance during deployment. This is more relevant to model design or optimization tasks.

  • C. Correct.

    Monitoring GPU utilization and memory usage provides insights into resource consumption and potential bottlenecks during model operation, which is essential for evaluating performance and reliability.

  • D. Correct.

    Comparing inference latency under different batch sizes helps determine the model's throughput and responsiveness, which are key performance metrics during deployment.

  • E. Incorrect.

    Optimizing hyperparameters is a part of the training process, not directly related to evaluating performance or scalability during deployment.

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