The answer is an Amazon S3 bucket with partitioned folders. This is the correct choice because partitioning by time—such as year, month, day, and hour—enables Athena to use partition pruning, drastically reducing the data scanned per query and allowing near-real-time analysis of semi-structured JSON logs within minutes of arrival. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of cost-effective, serverless query architecture for high-volume log ingestion, where the common trap is to over-engineer with a data warehouse or streaming service when S3’s native Athena integration and lifecycle policies already handle 1 TB per day efficiently. Remember the key trade-off: S3 provides the cheapest storage, but without partitioning, Athena would scan the entire bucket, killing performance and cost. Memory tip: “Partition by time to make Athena shine”—if logs arrive continuously, always think hour-level prefixes for immediate queryability.
DEA-C01 Data Store Management Practice Question
This DEA-C01 practice question tests your understanding of data store management. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 needs to store semi-structured JSON logs from multiple microservices in a cost-effective manner for later analysis using Amazon Athena. The logs are generated continuously, and the total volume is about 1 TB per day. The data must be queryable within minutes of arrival. Which storage solution is most appropriate?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon S3 bucket with partitioned folders
Amazon S3 with partitioned folders is the most appropriate solution because it provides a cost-effective, scalable storage layer for semi-structured JSON logs, and integrates natively with Amazon Athena for serverless querying. By partitioning the data by time (e.g., year/month/day/hour), Athena can use partition pruning to minimize scanned data, enabling queries within minutes of arrival. S3's low cost per GB and lifecycle policies further optimize storage for the 1 TB/day volume.
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.
✗
Amazon DynamoDB table with JSON attribute
Why it's wrong here
DynamoDB is expensive for large volumes and not optimal for analytics queries.
✗
Amazon RDS for PostgreSQL table with JSON column
Why it's wrong here
RDS is not cost-effective for 1 TB/day and has limited scalability.
✓
Amazon S3 bucket with partitioned folders
Why this is correct
S3 is cost-effective, and Athena can query the data directly.
Related concept
Read the scenario before looking for a memorised answer.
✗
Amazon Redshift cluster with JSON ingestion
Why it's wrong here
Redshift is more expensive and requires more management.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that a data warehouse (Redshift) or a NoSQL database (DynamoDB) is required for analytical queries on semi-structured data, when in fact S3 with Athena is the most cost-effective and scalable solution for serverless ad-hoc analysis on raw logs.
Detailed technical explanation
How to think about this question
Athena uses Presto under the hood to query data directly from S3, leveraging Hive-style partitioning (e.g., s3://bucket/logs/year=2025/month=03/day=15/hour=10/) to reduce data scanned. For near-real-time queryability, you can use S3 event notifications to trigger an AWS Glue crawler or partition projection to automatically update the table metadata as new logs arrive. A common real-world scenario is ingesting logs via Amazon Kinesis Firehose, which can directly write partitioned JSON to S3, achieving a latency of under 5 minutes from log generation to queryability.
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: Amazon S3 bucket with partitioned folders — Amazon S3 with partitioned folders is the most appropriate solution because it provides a cost-effective, scalable storage layer for semi-structured JSON logs, and integrates natively with Amazon Athena for serverless querying. By partitioning the data by time (e.g., year/month/day/hour), Athena can use partition pruning to minimize scanned data, enabling queries within minutes of arrival. S3's low cost per GB and lifecycle policies further optimize storage for the 1 TB/day volume.
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.
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 →
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 engineer needs to store semi-structured JSON logs from multiple sources in a centralized data store for querying using SQL. The logs are immutable and need to be retained for 90 days. Which AWS service should be used?
easy
A.Amazon RDS for MySQL.
B.Amazon DynamoDB.
✓ C.Amazon S3 with Amazon Athena.
D.Amazon ElastiCache for Redis.
Why C: Amazon S3 with Amazon Athena is the correct choice because S3 provides durable, cost-effective storage for immutable semi-structured JSON logs, and Athena enables serverless SQL querying directly against the data in S3 without needing to load or transform it. This combination meets the 90-day retention requirement and supports querying semi-structured data using standard SQL via Athena's built-in JSON SerDe.
Variation 2. A data engineer needs to store semi-structured JSON logs from multiple microservices in a cost-effective manner for ad-hoc querying using SQL. Which AWS service should be used?
medium
✓ A.Amazon Athena with data in S3
B.Amazon DynamoDB
C.Amazon RDS for MySQL
D.Amazon Kinesis Data Analytics
Why A: Amazon Athena is the correct choice because it allows you to query semi-structured JSON logs stored in S3 directly using standard SQL, without needing to load or transform the data. Athena's schema-on-read approach and pay-per-query pricing make it highly cost-effective for ad-hoc analysis of large volumes of log data, as you only pay for the data scanned during queries.
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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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