NCA-GENM Question 115
Select 3As part of a team deploying a generative AI multimodal model, you are tasked with evaluating its scalability under the supervision of a senior team member. Which of the following steps would be most appropriate to ensure the model performs well under increasing workload demands?
- A
Conduct load testing by simulating concurrent user requests to assess the model's response time.
- B
Optimize the training dataset by adding more diverse examples to improve model accuracy.
- C
Monitor system resource usage during inference, such as GPU memory and compute utilization, to identify bottlenecks.
- D
Test the model with various hardware configurations to evaluate its performance scalability.
- E
Reduce the model size by pruning layers to improve training speed and scalability.
Show answer and explanation
Correct answers: A, C, D
Explanation
Evaluating the scalability of a generative AI multimodal model involves understanding how the system performs under increasing workload demands and identifying potential bottlenecks. Conducting load testing, monitoring resource usage, and testing with different hardware configurations are critical steps in this process. These actions help ensure the model remains reliable and performs optimally as usage scales.
- A. Correct.
Conducting load testing is essential for assessing how the model handles multiple simultaneous requests, which is a key part of evaluating scalability under real-world workloads.
- B. Incorrect.
Optimizing the training dataset improves model accuracy but does not directly address the scalability of the model under increased workload demands.
- C. Correct.
Monitoring system resource usage during inference helps identify bottlenecks, such as memory or compute limitations, which are critical for evaluating performance scalability.
- D. Correct.
Testing with various hardware configurations provides insights into how the model scales across different environments, an important aspect of performance evaluation.
- E. Incorrect.
Reducing the model size might improve training efficiency but does not directly address how well the model handles increased workloads or its scalability.