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Amazon MLA-C01 Exam - Topic 1 Question 7 Discussion

A company wants to improve the sustainability of its ML operations.Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)
A) Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected. and D) Use AWS Trainium instances for training.
B) Use Amazon SageMaker Ground Truth for data labeling.
C) Deploy models by using AWS Lambda functions.
E) Use PyTorch or TensorFlow with the distributed training option.

Amazon MLA-C01 Exam - Topic 1 Question 7 Discussion

Actual exam question for Amazon's MLA-C01 exam
Question #: 7
Topic #: 1
[All MLA-C01 Questions]

A company wants to improve the sustainability of its ML operations.

Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

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Suggested Answer: A, D

by Georgene at Jul 04, 2025, 12:48 PM

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Tyra
7 months ago
I’m not sure about D, are AWS Trainium instances really that efficient?
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Weldon
7 months ago
Totally agree, A makes a lot of sense!
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Denae
7 months ago
A is a solid choice for stopping wasted training time.
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Kattie
8 months ago
C could help, but I think A is the best option here!
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Ming
8 months ago
B seems irrelevant for energy savings, just saying.
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Yuonne
8 months ago
I recall that distributed training with PyTorch or TensorFlow can sometimes lead to higher resource consumption, so I'm not sure if that's the best choice for sustainability.
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Margart
8 months ago
I practiced a question similar to this, and I feel like deploying with AWS Lambda could help reduce resource usage, but I'm not confident.
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Shonda
8 months ago
I'm not entirely sure, but I think using AWS Trainium instances might be more efficient for training compared to regular instances.
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Domingo
9 months ago
I remember that using SageMaker Debugger can help stop jobs that aren't converging, which should save energy.
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Carin
9 months ago
The distributed training option in PyTorch or TensorFlow seems like it could be a good strategy to explore. I'll make sure to consider that as well.
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Karrie
9 months ago
I'm not familiar with some of these AWS services, so I'll need to do a quick review to make sure I understand how they can help with sustainability.
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Sophia
9 months ago
Using Amazon SageMaker Debugger to stop non-converging training jobs sounds like a good way to save resources. And using AWS Trainium instances could also be helpful.
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Merrilee
9 months ago
Hmm, I'm a bit unsure about this one. I'll need to think through the different options and how they might impact the company's sustainability goals.
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Annice
9 months ago
This question seems straightforward. I'll focus on options that can reduce energy usage and computational resources for the training jobs.
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Edna
11 months ago
B? Really? Ground Truth for data labeling? I guess they want us to label our data with crayons and glitter to be more 'sustainable'.
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Lura
11 months ago
D all the way! AWS Trainium instances are specifically designed for training, so they're bound to be more efficient.
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Kimbery
10 months ago
C: Deploying models by using AWS Lambda functions could also help in reducing computational resources.
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Bernardo
10 months ago
B: I think using Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected is also a good idea.
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Lashawn
10 months ago
A: I agree, using AWS Trainium instances for training would definitely help reduce energy usage.
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Ernest
12 months ago
I'm surprised option C is there. Using Lambda for model deployment? That's like trying to run a marathon with cement shoes!
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Honey
10 months ago
Option C is definitely not the best choice for reducing energy usage.
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Ashlee
12 months ago
A and E are the way to go! Stopping non-converging jobs and distributed training are key to saving resources.
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Michal
10 months ago
I agree, using Amazon SageMaker Debugger can really help in detecting non-converging conditions.
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Lizette
11 months ago
A and E are the way to go! Stopping non-converging jobs and distributed training are key to saving resources.
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Lasandra
12 months ago
Using PyTorch or TensorFlow with distributed training can be another effective way to reduce computational resources.
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Aleta
12 months ago
I believe deploying models with AWS Lambda functions can also help in reducing energy usage.
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Lorenza
1 year ago
I agree with Bettina, stopping non-converging conditions early can save resources.
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Bettina
1 year ago
I think using Amazon SageMaker Debugger can help reduce energy usage.
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