The answer is to partition the data by date and sensor ID and store it in Apache Parquet format. Partitioning by date and sensor ID creates a hierarchical folder structure that allows Athena to use partition pruning, skipping entire directories of irrelevant data during queries, which dramatically reduces the amount of data scanned. Parquet’s columnar storage then enables Athena to read only the columns needed for a given query, and when combined with compression like Snappy, it further minimizes I/O and scan costs—directly lowering Athena’s per-TB pricing. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of S3 data lake optimization for Athena, often appearing as a multi-select question where a common trap is choosing only one optimization or suggesting JSON or CSV formats. Remember the memory tip: “Partition to skip, Parquet to sip”—partitioning skips entire folders, and Parquet sips only the columns you need.
DEA-C01 Data Store Management Practice Question
This DEA-C01 practice question tests your understanding of data store management. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 engineering team is designing a data lake on Amazon S3 for storing sensor data from IoT devices. The data is written in near real-time and needs to be queried using Amazon Athena. Which TWO configurations should the team implement to optimize query performance and minimize costs?
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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Store the data in Apache Parquet format.
Apache Parquet is a columnar storage format that allows Athena to read only the columns needed for a query, drastically reducing I/O and scan costs. Combined with compression (like Snappy or GZIP), Parquet minimizes the amount of data scanned per query, which directly lowers Athena's cost (charged per TB scanned) and improves query performance through predicate pushdown and efficient encoding.
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.
✗
Compress the data using GZIP.
Why it's wrong here
GZIP is not splittable and less efficient for Athena.
✗
Use S3 Standard-IA storage class.
Why it's wrong here
Standard-IA incurs retrieval costs and minimum charges.
✓
Store the data in Apache Parquet format.
Why this is correct
Parquet is columnar and reduces scan size.
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.
✓
Partition the data by date and sensor ID.
Why this is correct
Partitioning reduces data scanned by Athena.
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.
✗
Enable Requester Pays on the S3 bucket.
Why it's wrong here
Requester Pays does not optimize performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that any compression (like GZIP alone) is sufficient for Athena optimization, but the trap is that without a columnar format like Parquet or ORC, compression alone does not enable column pruning or predicate pushdown, leading to higher scan costs and slower queries.
Detailed technical explanation
How to think about this question
Parquet stores data in a columnar layout with row groups and uses encoding techniques like dictionary encoding, run-length encoding (RLE), and delta encoding to compress data efficiently. Athena leverages Parquet's embedded statistics (min/max values per row group) to perform predicate pushdown, skipping entire row groups that do not match the WHERE clause, which is especially effective for time-series sensor data partitioned by date and sensor ID. Partition pruning in Athena works by reading only the partition directories that match the query filter, so partitioning by date and sensor ID reduces the number of objects scanned to only those relevant to the query time range and device.
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 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: Store the data in Apache Parquet format. — Apache Parquet is a columnar storage format that allows Athena to read only the columns needed for a query, drastically reducing I/O and scan costs. Combined with compression (like Snappy or GZIP), Parquet minimizes the amount of data scanned per query, which directly lowers Athena's cost (charged per TB scanned) and improves query performance through predicate pushdown and efficient encoding.
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.
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
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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 data engineering team is designing a data lake on Amazon S3. They need to store raw data in a format that supports schema evolution and is optimized for analytics with Amazon Athena. Which storage format should they use?
hard
✓ A.Parquet
B.CSV
C.Avro
D.JSON
Why A: Option B is correct because Parquet is a columnar format that supports schema evolution and is optimized for Athena. Option A (CSV) does not support schema evolution and is less efficient. Option C (Avro) is row-based and not as efficient for columnar queries. Option D (JSON) is text-based and not optimized.
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This DEA-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 DEA-C01 exam.
Question Discussion
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