The answer is to use columnar file formats like Parquet or ORC alongside partitioning by frequently queried columns. This combination works because columnar formats store data by column rather than by row, allowing Athena to read only the specific columns needed for a query, while partitioning enables partition pruning—skipping entire directories of data that don’t match filter conditions like date or region. On the AWS Certified Data Engineer Associate DEA-C01 exam, this concept tests your understanding of Athena’s pay-per-scan pricing model and the principle of minimizing data scanned. A common trap is assuming that partitioning alone is sufficient without also using a columnar format, or vice versa; the exam expects you to recognize that both strategies together yield the greatest performance gain. Remember the memory tip: “Partition to prune, columnar to consume less”—partitioning reduces the rows read, and columnar storage reduces the columns read.
DEA-C01 Data Store Management Practice Question
This DEA-C01 practice question tests your understanding of data store management. 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 company is designing a data lake on Amazon S3. Which TWO strategies improve query performance for Amazon Athena?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Partition the data by frequently queried columns such as date or region.
Partitioning data by frequently queried columns (e.g., date or region) allows Athena to prune the data scanned by only reading the relevant partitions, reducing the amount of data scanned and improving query performance. This is a core optimization for Athena, which charges based on data scanned and performs better with less I/O.
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.
✗
Enable S3 Versioning on the bucket.
Why it's wrong here
Versioning adds overhead and does not improve query performance.
✗
Use server-side encryption with AWS KMS (SSE-KMS).
Why it's wrong here
Encryption does not improve query performance.
✓
Partition the data by frequently queried columns such as date or region.
Why this is correct
Partitioning prunes the data scanned.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use columnar file formats like Parquet or ORC.
Why this is correct
Columnar formats reduce data scanned.
Related concept
Read the scenario before looking for a memorised answer.
✗
Store data in CSV format with header rows.
Why it's wrong here
CSV is not as efficient as columnar formats.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse data management features (like versioning or encryption) with performance optimizations, or assume that simpler formats like CSV are sufficient for analytics, ignoring the significant performance benefits of partitioning and columnar storage.
Detailed technical explanation
How to think about this question
Partitioning works by organizing data into a hierarchical folder structure (e.g., s3://bucket/table/year=2023/month=01/), which Athena uses to skip irrelevant directories during query planning. Columnar formats like Parquet store data by column, enabling predicate pushdown and compression (e.g., dictionary encoding, run-length encoding), which reduces I/O and decompression overhead. In practice, combining partitioning with columnar formats can reduce query costs by up to 90% compared to unpartitioned CSV data.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Partition the data by frequently queried columns such as date or region. — Partitioning data by frequently queried columns (e.g., date or region) allows Athena to prune the data scanned by only reading the relevant partitions, reducing the amount of data scanned and improving query performance. This is a core optimization for Athena, which charges based on data scanned and performs better with less I/O.
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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