- A
Use managed spot training to reduce cost and then use cost savings to train with a larger instance.
Why wrong: Spot instances do not affect training duration; they only reduce cost.
- B
Use a single ml.p3.16xlarge instance with more GPUs and memory.
Why wrong: Larger instances provide more compute but may not scale linearly; still limited by single-instance communication.
- C
Use multiple ml.p3.2xlarge instances with SageMaker's distributed data parallelism library, enabling automatic sharding of the training data.
Distributed training across multiple instances reduces time proportionally; minimal code changes with SageMaker's SDK.
- D
Change the input mode to Pipe mode to stream data from S3 directly, reducing I/O wait time.
Why wrong: Pipe mode reduces data loading time but does not parallelize computation; training time may still be high.
Quick Answer
The answer is to use multiple ml.p3.2xlarge instances with SageMaker’s distributed data parallelism library. This is correct because the library automatically shards the 500 GB CSV dataset across the instances, enabling parallel gradient computation via an AllReduce operation, which reduces wall-clock training time from 2 hours to under 30 minutes without requiring manual code changes to the training script. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of SageMaker’s built-in distributed training libraries versus manual sharding or model parallelism—a common trap is choosing data parallelism with manual code changes or assuming a larger single instance is more efficient. Remember: when the model supports distributed training and you need minimal code changes, SageMaker’s distributed data parallelism library handles the heavy lifting automatically. Memory tip: “Data parallelism shards data, not code.”
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 team is using Amazon SageMaker to train a model on a dataset that is 500 GB in size, stored as CSV files in S3. The training job takes 2 hours using a single ml.p3.2xlarge instance. The team wants to reduce training time to under 30 minutes. The model architecture supports distributed training. Which solution will achieve this goal with the LEAST amount of code changes?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"least"Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
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 multiple ml.p3.2xlarge instances with SageMaker's distributed data parallelism library, enabling automatic sharding of the training data.
Option C is correct because SageMaker's distributed data parallelism library automatically shards the training data across multiple ml.p3.2xlarge instances, enabling parallel gradient computation and reducing wall-clock training time from 2 hours to under 30 minutes without requiring manual code changes to the training script. The model architecture already supports distributed training, so the library handles the communication and synchronization (e.g., AllReduce) transparently.
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 managed spot training to reduce cost and then use cost savings to train with a larger instance.
Why it's wrong here
Spot instances do not affect training duration; they only reduce cost.
- ✗
Use a single ml.p3.16xlarge instance with more GPUs and memory.
Why it's wrong here
Larger instances provide more compute but may not scale linearly; still limited by single-instance communication.
- ✓
Use multiple ml.p3.2xlarge instances with SageMaker's distributed data parallelism library, enabling automatic sharding of the training data.
Why this is correct
Distributed training across multiple instances reduces time proportionally; minimal code changes with SageMaker's SDK.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Change the input mode to Pipe mode to stream data from S3 directly, reducing I/O wait time.
Why it's wrong here
Pipe mode reduces data loading time but does not parallelize computation; training time may still be high.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'larger instance' (Option B) with 'distributed training' (Option C), failing to realize that a single large instance cannot parallelize data loading and gradient computation across multiple nodes, while distributed data parallelism with multiple smaller instances can achieve the required speedup with minimal code changes.
Detailed technical explanation
How to think about this question
SageMaker's distributed data parallelism library uses NVIDIA NCCL for AllReduce gradient synchronization and automatically partitions the dataset into shards using a custom S3 data source, so each worker processes a unique subset of the 500 GB CSV files. Under the hood, the library implements gradient compression and asynchronous communication to minimize overhead, and it supports both Horovod and Parameter Server strategies, but for this scenario, the default AllReduce approach with multiple ml.p3.2xlarge instances (each with 1 GPU) can achieve near-linear speedup if the model is compute-bound.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use multiple ml.p3.2xlarge instances with SageMaker's distributed data parallelism library, enabling automatic sharding of the training data. — Option C is correct because SageMaker's distributed data parallelism library automatically shards the training data across multiple ml.p3.2xlarge instances, enabling parallel gradient computation and reducing wall-clock training time from 2 hours to under 30 minutes without requiring manual code changes to the training script. The model architecture already supports distributed training, so the library handles the communication and synchronization (e.g., AllReduce) transparently.
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.
Are there clue words in this question I should notice?
Yes — watch for: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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