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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
3 months ago
I’m not sure about D, are AWS Trainium instances really that efficient?
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Weldon
3 months ago
Totally agree, A makes a lot of sense!
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Denae
3 months ago
A is a solid choice for stopping wasted training time.
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Kattie
3 months ago
C could help, but I think A is the best option here!
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Ming
4 months ago
B seems irrelevant for energy savings, just saying.
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Yuonne
4 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
4 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
4 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
4 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
5 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
5 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
5 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
5 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
5 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
7 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
7 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
6 months ago
C: Deploying models by using AWS Lambda functions could also help in reducing computational resources.
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Bernardo
6 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
6 months ago
A: I agree, using AWS Trainium instances for training would definitely help reduce energy usage.
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Ernest
7 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
6 months ago
Option C is definitely not the best choice for reducing energy usage.
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Ashlee
8 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
6 months ago
I agree, using Amazon SageMaker Debugger can really help in detecting non-converging conditions.
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Lizette
7 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
8 months ago
Using PyTorch or TensorFlow with distributed training can be another effective way to reduce computational resources.
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Aleta
8 months ago
I believe deploying models with AWS Lambda functions can also help in reducing energy usage.
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Lorenza
8 months ago
I agree with Bettina, stopping non-converging conditions early can save resources.
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Bettina
8 months ago
I think using Amazon SageMaker Debugger can help reduce energy usage.
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