- A
Configure the Processing job with multiple instances and use ShardedByS3Key for data splitting.
This distributes the data across instances, leveraging parallel processing and reducing memory per instance.
- B
Write the script to process data in chunks and write intermediate results to local ephemeral storage.
Why wrong: Chunking helps but a single instance still has aggregated memory limits; multi-instance is better.
- C
Increase the instance type to a larger one like ml.p3dn.24xlarge with more memory.
Why wrong: A larger instance is costly and may still not suffice; horizontal scaling is more effective.
- D
Reduce the number of instances to one and increase the volume size for swap space.
Why wrong: Swap space is slower and does not solve memory limit for in-memory processing.
Quick Answer
The answer is to configure the Processing job with multiple instances and use ShardedByS3Key for data splitting. This is correct because ShardedByS3Key partitions the input dataset by S3 object boundaries across the specified compute nodes, enabling distributed processing of the 500 million rows without overwhelming any single instance’s memory. For scaling SageMaker Processing jobs for large datasets, this approach is both cost-effective and scalable, as it allows you to use multiple smaller instances like ml.r5.xlarge instead of a single oversized ml.r5.24xlarge, reducing cost while achieving linear scaling. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this tests your understanding of distributed data processing patterns in SageMaker, often appearing as a trap where candidates mistakenly choose to increase instance memory or use a single larger instance. Remember the key: when you need to scale out, think “shard by key”—ShardedByS3Key is your go-to for splitting large datasets across workers.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 engineer is using Amazon SageMaker Processing to run a data preprocessing script on a dataset with 500 million rows. The script runs out of memory on a single ml.r5.24xlarge instance. The engineer needs to modify the processing job to handle the dataset size. Which approach is most cost-effective and scalable?
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 Processing job with multiple instances and use ShardedByS3Key for data splitting.
Option A is correct because SageMaker Processing with ShardedByS3Key splits the input dataset by S3 object boundaries across multiple instances, allowing distributed processing of the 500 million rows without exceeding memory on any single instance. This approach is cost-effective as it uses multiple smaller instances (e.g., ml.r5.xlarge) rather than a single oversized instance, and scales linearly with data size.
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.
- ✓
Configure the Processing job with multiple instances and use ShardedByS3Key for data splitting.
Why this is correct
This distributes the data across instances, leveraging parallel processing and reducing memory per instance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Write the script to process data in chunks and write intermediate results to local ephemeral storage.
Why it's wrong here
Chunking helps but a single instance still has aggregated memory limits; multi-instance is better.
- ✗
Increase the instance type to a larger one like ml.p3dn.24xlarge with more memory.
Why it's wrong here
A larger instance is costly and may still not suffice; horizontal scaling is more effective.
- ✗
Reduce the number of instances to one and increase the volume size for swap space.
Why it's wrong here
Swap space is slower and does not solve memory limit for in-memory processing.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that increasing instance size or using swap space is the primary solution for memory issues, whereas the correct approach is to distribute the workload horizontally using SageMaker's built-in data sharding feature.
Detailed technical explanation
How to think about this question
ShardedByS3Key works by assigning each S3 object (or a range of objects) to a single processing instance, ensuring no two instances process the same data. Under the hood, SageMaker uses the S3 ETag or byte-range requests to split objects, but for large datasets with many small files, this can lead to uneven sharding; a better practice is to use a single large file with S3 Select or to pre-partition data into equal-sized objects. In real-world scenarios, combining ShardedByS3Key with a distributed framework like PySpark or Dask within the processing script further optimizes memory usage across instances.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure the Processing job with multiple instances and use ShardedByS3Key for data splitting. — Option A is correct because SageMaker Processing with ShardedByS3Key splits the input dataset by S3 object boundaries across multiple instances, allowing distributed processing of the 500 million rows without exceeding memory on any single instance. This approach is cost-effective as it uses multiple smaller instances (e.g., ml.r5.xlarge) rather than a single oversized instance, and scales linearly with data size.
What should I do if I get this MLA-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: Jun 30, 2026
This MLA-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 MLA-C01 exam.
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