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Data Engineering →mediumMultiple ChoiceObjective-mapped

Parquet Conversion for Athena: Cost and Performance Optimization

This MLS-C01 practice question tests your understanding of data engineering. 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 is building a data lake on Amazon S3. Data arrives from multiple sources in different formats (CSV, JSON, Parquet). The engineering team wants to query this data using Amazon Athena with minimal transformation. Which approach minimizes query cost and improves performance?

A
Use Amazon Redshift Spectrum to query the data directly without transformation
Why wrong: Redshift Spectrum can query S3 data, but still benefits from optimized formats; also adds Redshift cost.
B
Use AWS Glue to convert all data to Parquet format, partition by date, and store in a separate S3 bucket
This reduces data scanned, improves performance, and lowers cost.
C
Use Amazon EMR to convert data to CSV format and repartition
Why wrong: CSV is not optimal; Parquet is better. EMR adds complexity and cost.
D
Store data as-is in S3 and create external tables in Athena for each format
Why wrong: While possible, querying raw CSV/JSON scans more data and is less performant than optimized formats.

Full breakdown with real-world context →

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 convert all data to Parquet format, partition by date, and store in a separate S3 bucket

Option B is correct because converting data to Parquet format (a columnar storage format) significantly reduces the amount of data scanned by Athena, which directly lowers query cost (Athena charges per TB scanned). Partitioning by date further limits scanned data by pruning irrelevant partitions. AWS Glue provides a serverless ETL service to perform this conversion efficiently, and storing the output in a separate S3 bucket avoids polluting the raw data lake.

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 Amazon Redshift Spectrum to query the data directly without transformation
Why it's wrong here
Redshift Spectrum can query S3 data, but still benefits from optimized formats; also adds Redshift cost.
✓
Use AWS Glue to convert all data to Parquet format, partition by date, and store in a separate S3 bucket
Why this is correct
This reduces data scanned, improves performance, and lowers cost.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon EMR to convert data to CSV format and repartition
Why it's wrong here
CSV is not optimal; Parquet is better. EMR adds complexity and cost.
✗
Store data as-is in S3 and create external tables in Athena for each format
Why it's wrong here
While possible, querying raw CSV/JSON scans more data and is less performant than optimized formats.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often choose Option D (store as-is) thinking Athena can handle any format efficiently, but they overlook that Athena’s pricing is based on data scanned, and raw CSV/JSON scans are far more expensive than columnar formats like Parquet.

Detailed technical explanation

How to think about this question

Parquet uses columnar storage with compression (e.g., Snappy, Gzip) and encoding like dictionary encoding, which reduces I/O and speeds up queries by reading only the columns needed. Partitioning by date creates Hive-style partitions (e.g., s3://bucket/year=2025/month=01/day=15/), allowing Athena to use partition pruning via the WHERE clause to skip irrelevant directories. AWS Glue crawlers can automatically infer schemas and update the Glue Data Catalog, making the data immediately queryable in Athena without manual DDL.

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.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

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 AWS Glue to convert all data to Parquet format, partition by date, and store in a separate S3 bucket — Option B is correct because converting data to Parquet format (a columnar storage format) significantly reduces the amount of data scanned by Athena, which directly lowers query cost (Athena charges per TB scanned). Partitioning by date further limits scanned data by pruning irrelevant partitions. AWS Glue provides a serverless ETL service to perform this conversion efficiently, and storing the output in a separate S3 bucket avoids polluting the raw data lake.

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.

About these practice questions

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Same concept, more angles

2 more ways this is tested on MLS-C01

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A company wants to analyze historical data stored in Amazon S3 using Amazon Athena. The data is in CSV format and is partitioned by date. Which action will provide the best query performance and cost optimization?

easy

A.Use AWS Glue to compress the CSV files with gzip
B.Create an S3 event notification to trigger a Lambda function that warms up Athena
C.Keep CSV format but ensure partitions are in the format year=YYYY/month=MM/day=DD
✓ D.Convert the data to Parquet format and use the existing partition structure

Why D: Converting data to Parquet and partitioning provides the best performance and cost savings because Athena can use predicate pushdown and column pruning, scanning less data. Option A (using Glue to gzip compress) still uses CSV which requires full scan. Option B (S3 event notification to warm up Athena) is not relevant because Athena caches results but doesn't need warming. Option C (only partitioning) helps but CSV is still row-based and less efficient than Parquet.

Variation 2. A company is using Amazon Athena to query a data lake in S3. Queries are slow and expensive. The data is stored as JSON. Which action will improve query performance and reduce cost?

medium

A.Compress the JSON files using gzip
B.Partition the data by date
✓ C.Convert the data to Parquet format
D.Increase the number of Athena workers

Why C: Option C is correct because converting JSON data to Parquet format significantly improves Athena query performance and reduces cost. Parquet is a columnar storage format that allows Athena to scan only the columns needed for a query, drastically reducing the amount of data read from S3. This minimizes I/O and compute costs, as Athena charges based on the amount of data scanned. In contrast, JSON is row-based and requires scanning entire files even for queries that only touch a few columns.

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