- A
Reduce the number of training epochs
Why wrong: Reducing epochs may degrade model accuracy.
- B
Use a GPU instance type like ml.p3.2xlarge
GPUs accelerate training for deep learning.
- C
Use distributed training with multiple instances
Parallel processing reduces training time.
- D
Use Pipe input mode to stream data from S3
Pipe mode reduces download time.
- E
Increase the batch size in the training script
Why wrong: Increasing batch size may cause memory issues, not necessarily faster.
Question 284 of 1,755
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 company uses SageMaker to train a model. The training job is taking too long and the data scientist wants to speed it up. Which THREE strategies should the data scientist consider? (Select THREE.)
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
✓
Use a GPU instance type like ml.p3.2xlarge
Option B is correct because GPU instances like ml.p3.2xlarge are optimized for parallel computation, significantly accelerating the training of deep learning models by handling matrix operations more efficiently than CPUs. SageMaker supports a range of GPU instances (e.g., ml.p3, ml.p4, ml.g5) that can reduce training time for compute-intensive workloads.
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.
- ✗
Reduce the number of training epochs
Why it's wrong here
Reducing epochs may degrade model accuracy.
- ✓
Use a GPU instance type like ml.p3.2xlarge
Why this is correct
GPUs accelerate training for deep learning.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use distributed training with multiple instances
Why this is correct
Parallel processing reduces training time.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use Pipe input mode to stream data from S3
Why this is correct
Pipe mode reduces download time.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the batch size in the training script
Why it's wrong here
Increasing batch size may cause memory issues, not necessarily faster.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may incorrectly select 'Increase the batch size' (Option E) as a guaranteed speed-up, overlooking that it requires hyperparameter tuning and can cause convergence problems, while the question asks for strategies that are directly and reliably effective.
Detailed technical explanation
How to think about this question
Distributed training (Option C) leverages data parallelism or model parallelism across multiple instances, using frameworks like Horovod or SageMaker's built-in distributed training libraries to synchronize gradients via AllReduce. Pipe input mode (Option D) streams data directly from S3 to the training algorithm, reducing download latency and disk I/O bottlenecks, which is especially beneficial for large datasets that do not fit in local storage.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Use a GPU instance type like ml.p3.2xlarge — Option B is correct because GPU instances like ml.p3.2xlarge are optimized for parallel computation, significantly accelerating the training of deep learning models by handling matrix operations more efficiently than CPUs. SageMaker supports a range of GPU instances (e.g., ml.p3, ml.p4, ml.g5) that can reduce training time for compute-intensive workloads.
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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Last reviewed: Jul 4, 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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