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Exam AWS Certified Machine Learning - Specialty topic 1 question 253 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 253
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

An automotive company uses computer vision in its autonomous cars. The company trained its object detection models successfully by using transfer learning from a convolutional neural network (CNN). The company trained the models by using PyTorch through the Amazon SageMaker SDK.

The vehicles have limited hardware and compute power. The company wants to optimize the model to reduce memory, battery, and hardware consumption without a significant sacrifice in accuracy.

Which solution will improve the computational efficiency of the models?

  • A. Use Amazon CloudWatch metrics to gain visibility into the SageMaker training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set new weights based on the pruned set of filters. Run a new training job with the pruned model.
  • B. Use Amazon SageMaker Ground Truth to build and run data labeling workflows. Collect a larger labeled dataset with the labelling workflows. Run a new training job that uses the new labeled data with previous training data.
  • C. Use Amazon SageMaker Debugger to gain visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set the new weights based on the pruned set of filters. Run a new training job with the pruned model.
  • D. Use Amazon SageMaker Model Monitor to gain visibility into the ModelLatency metric and OverheadLatency metric of the model after the company deploys the model. Increase the model learning rate. Run a new training job.
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Suggested Answer: C 🗳️
by Mickey321 at June 9, 2023, 5:48 p.m.
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loict
Highly Voted 1 year, 1 month ago
Selected Answer: C
A. NO - CloudWatch cannot get filter ranks of filters. Run a new training job with the pruned model. B. NO - Ground Truth can help improve model performance, not reduce inference cost C. YES - Filter pruning based on ranks is specific to CNN and supported by SageMaker Debugger D. NO - learning rate impacts training, not inference
upvoted 6 times
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rinnno
Most Recent 2 weeks, 4 days ago
Selected Answer: C
https://aws.amazon.com/ko/blogs/machine-learning/pruning-machine-learning-models-with-amazon-sagemaker-debugger-and-amazon-sagemaker-experiments/
upvoted 1 times
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vkbajoria
7 months, 1 week ago
Selected Answer: C
Gain insight into the training metrics with debugger and prune
upvoted 1 times
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kaike_reis
1 year, 2 months ago
Selected Answer: C
Letter B uses AWS GT incorrectly, so it is wrong. Letter D is outside the scope of what the question asks, so it is wrong. Letter A falls into the same problem as Letter B. Letter C is correct.
upvoted 1 times
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ADVIT
1 year, 3 months ago
SageMaker Model Monitor is for model drift, not for utilization metrics. SageMaker Debugger is the best choose. So it's C.
upvoted 1 times
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Mickey321
1 year, 4 months ago
Selected Answer: C
C. Use Amazon SageMaker Debugger to gain visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set the new weights based on the pruned set of filters. Run a new training job with the pruned model.
upvoted 1 times
...
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