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Machine Learning Implementation and Operations →hardMultiple SelectObjective-mapped

MLS-C01 Practice Question: Machine Learning Implementation and Operations

This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

A data science team is training a large deep learning model using Amazon SageMaker. The training job is taking a long time because the model has many layers and the dataset is large. The team wants to reduce training time by distributing the training across multiple GPUs on a single instance, as well as across multiple instances. Which TWO actions should the team take? (Choose two.)

A
Use SageMaker's distributed data parallelism (SMDDP) library to shard the model across GPUs.
Why wrong: SMDDP is for data parallelism, not model parallelism. It replicates the model on each GPU and splits the data, which does not reduce memory footprint per GPU for large models.
B
Configure the training job to use SageMaker's model parallelism (SMP) library for pipeline or tensor parallelism.
SMP allows splitting the model across multiple GPUs and instances, reducing memory footprint per GPU and enabling training of large models that would otherwise not fit. This complements data parallelism.
C
Use SageMaker's managed training with a single instance containing multiple GPUs and enable data parallelism.
Why wrong: This approach uses only one instance, so it does not distribute across multiple instances. While multiple GPUs on one instance help, the question asks for actions to reduce training time by distributing across both GPUs and instances.
D
Use Horovod for data parallelism across multiple instances.
Horovod is a distributed training framework that supports data parallelism across multiple instances, reducing training time by processing more data in parallel.
E
Set the instance type to a single GPU instance and rely on automatic model parallelism.
Why wrong: A single GPU instance limits parallelism. Automatic model parallelism is not a built-in SageMaker feature; training on one GPU would be slower.

Full breakdown with real-world context →

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Configure the training job to use SageMaker's model parallelism (SMP) library for pipeline or tensor parallelism.

Option B is correct because the SageMaker model parallelism (SMP) library is specifically designed to split large deep learning models across multiple GPUs using pipeline or tensor parallelism. This allows the team to train models that are too large to fit on a single GPU and to reduce training time by parallelizing computation across devices within and across instances.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Use SageMaker's distributed data parallelism (SMDDP) library to shard the model across GPUs.
Why it's wrong here
SMDDP is for data parallelism, not model parallelism. It replicates the model on each GPU and splits the data, which does not reduce memory footprint per GPU for large models.
✓
Configure the training job to use SageMaker's model parallelism (SMP) library for pipeline or tensor parallelism.
Why this is correct
SMP allows splitting the model across multiple GPUs and instances, reducing memory footprint per GPU and enabling training of large models that would otherwise not fit. This complements data parallelism.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use SageMaker's managed training with a single instance containing multiple GPUs and enable data parallelism.
Why it's wrong here
This approach uses only one instance, so it does not distribute across multiple instances. While multiple GPUs on one instance help, the question asks for actions to reduce training time by distributing across both GPUs and instances.
✓
Use Horovod for data parallelism across multiple instances.
Why this is correct
Horovod is a distributed training framework that supports data parallelism across multiple instances, reducing training time by processing more data in parallel.
Related concept
Read the scenario before looking for a memorised answer.
✗
Set the instance type to a single GPU instance and rely on automatic model parallelism.
Why it's wrong here
A single GPU instance limits parallelism. Automatic model parallelism is not a built-in SageMaker feature; training on one GPU would be slower.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse data parallelism (which shards data) with model parallelism (which shards the model), and assume that simply using multiple GPUs on a single instance automatically distributes the model, when in fact explicit model parallelism libraries like SMP are required for large models that do not fit in GPU memory.

Detailed technical explanation

How to think about this question

SageMaker model parallelism (SMP) partitions the model layers across GPUs using pipeline parallelism (where different GPUs handle different sequential stages) or tensor parallelism (where individual layers are split across GPUs). This is critical for models with hundreds of layers, such as GPT-3 or BERT-large, where the parameter count exceeds the memory of a single GPU. SMP also integrates with SageMaker's distributed training launcher to automatically handle communication between GPUs using NCCL, reducing the engineering overhead of manual sharding.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.

TExam Day Tips

Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

What to study next

Got this wrong? Here's your next step.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Configure the training job to use SageMaker's model parallelism (SMP) library for pipeline or tensor parallelism. — Option B is correct because the SageMaker model parallelism (SMP) library is specifically designed to split large deep learning models across multiple GPUs using pipeline or tensor parallelism. This allows the team to train models that are too large to fit on a single GPU and to reduce training time by parallelizing computation across devices within and across instances.

What should I do if I get this MLS-C01 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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