- A
Use S3 Select to retrieve only the last 6 months of data by applying an SQL expression on each object.
Why wrong: S3 Select works per object and does not support partition filtering across directories.
- B
Use AWS Glue to create a catalog table with partitions, then query with Athena to create a filtered dataset in S3.
Partition pruning ensures only relevant data is scanned.
- C
Use SageMaker Processing with a script that lists all objects in the bucket and reads only those with the desired prefixes.
Why wrong: This still lists all objects and may incur listing costs.
- D
Use SageMaker Processing with Input Mode 'File' and specify the S3 prefix for the last 6 months.
Why wrong: This still downloads all listed files; without partition pruning, it scans all files.
Quick Answer
The answer is to use AWS Glue to create a catalog table with partitions, then query with Athena to create a filtered dataset in S3. This approach is correct because it leverages partition pruning, allowing Athena to scan only the metadata for the last 6 months of partitions rather than the full 2 billion records, dramatically reducing data scanned and cost. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of cost-optimized data preparation for SageMaker training, often appearing as a trap where candidates mistakenly choose to load all data into SageMaker or use S3 Select, which lacks partition awareness. The key insight is that filtering partitioned S3 data by date range for SageMaker training to minimize cost requires a cataloged, queryable layer like Glue and Athena, not direct S3 operations. Memory tip: think “Glue the partitions, Athena the filter, SageMaker the feast” to remember the pipeline.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 company has a dataset of 2 billion records stored as text files in Amazon S3. The data is partitioned by year and month. The data science team wants to read only the last 6 months of data for model training using SageMaker. To minimize data scanned and reduce costs, which approach should the team use?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 AWS Glue to create a catalog table with partitions, then query with Athena to create a filtered dataset in S3.
Option B is correct because AWS Glue can crawl the S3 data to create a catalog table with partitions by year and month. Athena can then query only the partitions corresponding to the last 6 months, scanning minimal data and writing the filtered results back to S3 for SageMaker training. This approach leverages partition pruning to reduce costs and avoids loading or processing the full 2 billion records.
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 S3 Select to retrieve only the last 6 months of data by applying an SQL expression on each object.
Why it's wrong here
S3 Select works per object and does not support partition filtering across directories.
- ✓
Use AWS Glue to create a catalog table with partitions, then query with Athena to create a filtered dataset in S3.
Why this is correct
Partition pruning ensures only relevant data is scanned.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use SageMaker Processing with a script that lists all objects in the bucket and reads only those with the desired prefixes.
Why it's wrong here
This still lists all objects and may incur listing costs.
- ✗
Use SageMaker Processing with Input Mode 'File' and specify the S3 prefix for the last 6 months.
Why it's wrong here
This still downloads all listed files; without partition pruning, it scans all files.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that SageMaker's Input Mode 'File' or S3 Select can efficiently filter partitioned data, but the key trap is that partition pruning requires a catalog service (like Glue) and a query engine (like Athena) to avoid scanning all objects or listing the entire bucket.
Detailed technical explanation
How to think about this question
Athena uses Presto under the hood and supports partition projection, which can automatically infer partition locations from the S3 prefix pattern (e.g., year=2023/month=10). When querying with a WHERE clause on partition columns, Athena prunes partitions at the metastore level, reading only the manifest files for matching partitions—this reduces data scanned to as little as the sum of file sizes for the last 6 months. In contrast, SageMaker's built-in filtering mechanisms do not natively support partition pruning; they rely on client-side filtering after listing objects, which is less efficient for large-scale datasets.
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.
- →
Data Preparation for Machine Learning — study guide chapter
Learn the concepts, then practise the questions
- →
Data Preparation for Machine Learning practice questions
Targeted practice on this topic area only
- →
All MLA-C01 questions
507 questions across all exam domains
- →
AWS Certified Machine Learning Engineer Associate MLA-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Use AWS Glue to create a catalog table with partitions, then query with Athena to create a filtered dataset in S3. — Option B is correct because AWS Glue can crawl the S3 data to create a catalog table with partitions by year and month. Athena can then query only the partitions corresponding to the last 6 months, scanning minimal data and writing the filtered results back to S3 for SageMaker training. This approach leverages partition pruning to reduce costs and avoids loading or processing the full 2 billion records.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
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 →
Keep practising
More MLA-C01 practice questions
- A company is running a SageMaker endpoint serving multiple models. They need to monitor for data drift and model quality…
- A data scientist trained a logistic regression model on a dataset with 100 features. After training, the training accura…
- A team is training a deep learning model on Amazon SageMaker using a custom Docker container. Which three practices shou…
- A company is using SageMaker to train a neural network for image classification. The training job is taking too long. Th…
- A team is developing a model to predict customer churn. The dataset has 10,000 samples with 20 features. The target vari…
- A data engineer is processing a large dataset in Amazon S3 with AWS Glue ETL. The dataset contains timestamps in multipl…
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.