MLA-C01 · Question #214
MLA-C01 Question #214: Real Exam Question with Answer & Explanation
The correct answer is B: Add SageMaker Profiler annotations to the training script. Run the script and generate a report. SageMaker Profiler annotations instrument the training script to capture detailed, step-level performance and resource usage. The resulting profiling report pinpoints where the script is bottlenecked and where GPU utilization can be improved.
Question
An ML engineer is using an Amazon SageMaker Studio notebook to train a neural network by creating an estimator. The estimator runs a Python training script that uses Distributed Data Parallel (DDP) on a single instance that has more than one GPU. The ML engineer discovers that the training script is underutilizing GPU resources. The ML engineer must identify the point in the training script where resource utilization can be optimized. Which solution will meet this requirement?
Options
- AUse Amazon CloudWatch metrics to create a report that describes GPU utilization over time.
- BAdd SageMaker Profiler annotations to the training script. Run the script and generate a report
- CUse AWS CloudTrail to create a report that describes GPU utilization and GPU memory utilization
- DCreate a default monitor in Amazon SageMaker Model Monitor and suggest a baseline. Generate
Explanation
SageMaker Profiler annotations instrument the training script to capture detailed, step-level performance and resource usage. The resulting profiling report pinpoints where the script is bottlenecked and where GPU utilization can be improved.
Topics
Community Discussion
No community discussion yet for this question.