The answer is to partition the data by commonly filtered columns such as date or region, use a columnar storage format like Parquet, and compress the data. These three design changes directly improve Athena query performance and reduce costs because Athena charges based on the volume of data scanned; partitioning limits scans to relevant subdirectories, columnar formats read only the needed columns, and compression further shrinks the data footprint. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your understanding of Athena’s pricing model and the principle that reducing scan volume is the primary lever for both speed and cost. A common trap is to suggest indexing or caching, which Athena does not support natively. Remember the memory tip: “Partition, Parquet, and Press” — partition your data, store it in Parquet, and press it with compression to slash scan volume.
DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. 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 uses Amazon S3 to store raw data and runs AWS Glue ETL jobs to transform it into Parquet. The data is then queried using Amazon Athena. Queries are slow and expensive due to high scan volumes. Which THREE design changes can improve query performance and reduce costs? (Select THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Convert the data to a columnar format like Parquet or ORC if not already
Option B is correct because columnar formats like Parquet or ORC store data by column rather than by row, allowing Athena to read only the columns needed for a query. This drastically reduces the amount of data scanned per query, directly lowering both latency and cost since Athena charges based on the volume of data read.
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.
✗
Increase the number of files by reducing file size to 1 MB
Why it's wrong here
Many small files increase metadata overhead and slow down queries.
✓
Convert the data to a columnar format like Parquet or ORC if not already
Why this is correct
Columnar formats store data by column, reducing I/O for queries that select few columns.
Related concept
Read the scenario before looking for a memorised answer.
✓
Compress the data using a splittable compression format like Snappy
Why this is correct
Compression reduces storage and data scanned, and Snappy is splittable for parallel processing.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use bucketing on high-cardinality columns
Why it's wrong here
Bucketing helps with joins and sampling but not as universally as partitioning.
✓
Partition the data by commonly filtered columns such as date or region
Why this is correct
Partition pruning allows Athena to skip irrelevant partitions, reducing data scanned.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse bucketing with partitioning, or assume that increasing file count always improves parallelism, when in fact small files harm performance in distributed query engines like Athena.
Detailed technical explanation
How to think about this question
Parquet and ORC use techniques like dictionary encoding, run-length encoding, and predicate pushdown to skip irrelevant data blocks entirely. For example, with Parquet's row group statistics, Athena can skip entire row groups that do not match filter predicates, further reducing I/O. In practice, converting a 1 TB CSV dataset to Parquet with Snappy compression often reduces storage by 70-80% and query scan volume by 90% or more.
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 Operations and Support — This question tests Data Operations and Support — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Convert the data to a columnar format like Parquet or ORC if not already — Option B is correct because columnar formats like Parquet or ORC store data by column rather than by row, allowing Athena to read only the columns needed for a query. This drastically reduces the amount of data scanned per query, directly lowering both latency and cost since Athena charges based on the volume of data read.
What should I do if I get this DEA-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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Question Discussion
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