Quick Answer
The answer is Amazon SageMaker and Amazon EMR. SageMaker directly supports distributed training with its built-in model parallelism library, which automatically partitions large models across multiple GPUs to optimize memory and compute efficiency. Amazon EMR, when running Apache Spark, enables distributed machine learning across clusters by leveraging its in-memory processing for iterative algorithms, making it suitable for large-scale training workloads. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to differentiate between services designed for training versus data processing or warehousing—a common trap is confusing AWS Glue or Redshift for training tasks. Remember that SageMaker handles deep learning parallelism, while EMR excels at distributed ML with Spark; for a quick memory tip, think “SageMaker for models, EMR for data crunching across clusters.”
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 scientist needs to select a model training infrastructure that supports distributed training across multiple GPUs and provides automatic model parallelism. Which TWO AWS services should the scientist consider?
AWS Glue
Why wrong: Glue is for ETL, not model training.
AWS Lambda
Why wrong: Lambda has resource limits unsuitable for distributed training.
Amazon Redshift
Why wrong: Redshift is a data warehouse, not for training.
Amazon EMR
EMR with Spark MLlib can perform distributed training.
Amazon SageMaker
SageMaker offers distributed training libraries and model parallelism.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon EMR
Options A (SageMaker) and B (Amazon EMR) are correct. SageMaker supports distributed training with model parallelism. EMR with Spark supports distributed ML. Option C (AWS Glue) is for ETL, not training. Option D (Amazon Redshift) is a data warehouse. Option E (AWS Lambda) is not for large-scale training.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
AWS Glue
Why it's wrong here
Glue is for ETL, not model training.
AWS Lambda
Why it's wrong here
Lambda has resource limits unsuitable for distributed training.
Amazon Redshift
Why it's wrong here
Redshift is a data warehouse, not for training.
Amazon EMR
Why this is correct
EMR with Spark MLlib can perform distributed training.
Related concept
Read the scenario before looking for a memorised answer.
Amazon SageMaker
Why this is correct
SageMaker offers distributed training libraries and model parallelism.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon EMR — Options A (SageMaker) and B (Amazon EMR) are correct. SageMaker supports distributed training with model parallelism. EMR with Spark supports distributed ML. Option C (AWS Glue) is for ETL, not training. Option D (Amazon Redshift) is a data warehouse. Option E (AWS Lambda) is not for large-scale training.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is training a TensorFlow model on a single GPU instance. The training is taking too long. Which AWS service should be used to reduce training time by distributing the workload across multiple GPUs?
easyWhy A: Amazon SageMaker supports distributed training across multiple GPUs using the SageMaker distributed training libraries. Option B is correct. Option A is wrong because AWS Batch is for batch computing, not specifically optimized for GPU training. Option C is wrong because Amazon EMR is for big data processing. Option D is wrong because AWS Glue is for ETL jobs.
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Last reviewed: Jun 20, 2026
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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