Quick Answer
The answer is Parquet, as it is the columnar storage format optimized for analytical queries and natively supported by Amazon SageMaker for efficient ML training data loading. When converting 10 TB of CSV data to Parquet, SageMaker can read only the specific columns required for training rather than scanning entire rows, drastically reducing I/O operations and storage costs. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of how columnar formats like Parquet minimize data scan volume in S3, directly cutting training time and cost—a common trap is choosing Avro (row-oriented) or ORC (less native SageMaker integration). Remember the memory tip: Parquet is the “columnar pick” for ML, because it lets SageMaker “skip the rows you don’t need.”
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 preparing a large dataset of 10 TB for ML training on Amazon SageMaker. The data is stored in Amazon S3 as CSV files. To reduce training time and cost, the engineer wants to use a columnar format that is optimized for analytical queries. Which format should the engineer convert the data to?
XML
Why wrong: XML is verbose and not columnar.
Parquet
Parquet is a columnar format that speeds up data access and reduces storage costs.
ORC
Why wrong: ORC is columnar but less commonly used with SageMaker compared to Parquet.
JSON Lines
Why wrong: JSON Lines is a row-oriented format, not optimal for columnar access.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Parquet
Parquet is a columnar storage format that is highly optimized for analytical queries and is natively supported by Amazon SageMaker for efficient data loading. By converting the 10 TB of CSV data to Parquet, the data engineer can reduce I/O and storage costs because columnar formats allow SageMaker to read only the columns needed for training, rather than scanning entire rows. This directly addresses the goal of reducing training time and cost for ML workloads.
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.
XML
Why it's wrong here
XML is verbose and not columnar.
Parquet
Why this is correct
Parquet is a columnar format that speeds up data access and reduces storage costs.
Related concept
Read the scenario before looking for a memorised answer.
ORC
Why it's wrong here
ORC is columnar but less commonly used with SageMaker compared to Parquet.
JSON Lines
Why it's wrong here
JSON Lines is a row-oriented format, not optimal for columnar access.
Common exam traps
AWS often tests the distinction between columnar formats (Parquet vs. ORC) by making both appear correct, but the trap here is that ORC is tightly coupled with Hive and less commonly used with SageMaker, while Parquet is the de facto standard for AWS-native ML and analytics services.
Detailed technical explanation
Parquet stores data in a columnar layout using techniques like dictionary encoding, run-length encoding (RLE), and bit packing, which can achieve compression ratios of 75-90% compared to CSV for typical tabular data. Under the hood, Parquet files contain row groups and column chunks with embedded metadata, allowing SageMaker's built-in algorithms (e.g., XGBoost, Linear Learner) to use predicate pushdown and column projection to skip irrelevant data during training. In a real-world scenario, converting a 10 TB CSV dataset to Parquet can reduce training time by over 50% because SageMaker's Pipe mode can stream only the required columns directly from S3 without materializing the entire dataset.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Practise MLA-C01 questions linked to ML Model Development.
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
Practise MLA-C01 questions linked to MLA-C01 scenario.
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
The correct answer is: Parquet — Parquet is a columnar storage format that is highly optimized for analytical queries and is natively supported by Amazon SageMaker for efficient data loading. By converting the 10 TB of CSV data to Parquet, the data engineer can reduce I/O and storage costs because columnar formats allow SageMaker to read only the columns needed for training, rather than scanning entire rows. This directly addresses the goal of reducing training time and cost for ML workloads.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 →
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.