This DEA-C01 practice question tests your understanding of data store management. 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 data engineer is designing a data store for real-time analytics on high-velocity clickstream data. The data must be stored in a schema-on-read format and support SQL queries with sub-second latency. Which service should be used?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon Kinesis Data Firehose to S3 with Athena
Amazon Kinesis Data Firehose can ingest high-velocity clickstream data and deliver it to Amazon S3, where it is stored in a schema-on-read format (e.g., Parquet or ORC). Amazon Athena then allows SQL queries directly on the data in S3 with sub-second latency when using partitions, columnar formats, and optimizations like AWS Glue Catalog. This combination meets the requirements for real-time analytics without predefining a schema.
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 Redshift
Why it's wrong here
Redshift is for structured data warehousing, not schema-on-read.
✓
Amazon Kinesis Data Firehose to S3 with Athena
Why this is correct
Firehose streams data to S3, Athena queries with schema-on-read and partitioning for low latency.
Related concept
Read the scenario before looking for a memorised answer.
✗
Amazon Kinesis Data Analytics
Why it's wrong here
Kinesis Data Analytics is for real-time analytics on streams, not ad-hoc queries.
✗
Amazon DynamoDB
Why it's wrong here
DynamoDB is NoSQL, not designed for SQL queries on semi-structured data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Amazon Kinesis Data Analytics (which processes streams but does not store data) with a storage solution, or they assume Amazon Redshift is suitable for real-time streaming without recognizing its schema-on-write requirement and higher latency for ad-hoc queries.
Detailed technical explanation
How to think about this question
Under the hood, Kinesis Data Firehose can buffer incoming data and convert it to columnar formats like Parquet or ORC, which reduce storage and improve query performance in Athena. Athena leverages Presto under the hood to execute distributed SQL queries, and with partitioning and file compaction, it can achieve sub-second latency on datasets up to terabytes. A real-world scenario is ingesting web clickstream events, storing them partitioned by date/hour in S3, and querying with Athena for real-time dashboards without managing a separate data warehouse.
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: Amazon Kinesis Data Firehose to S3 with Athena — Amazon Kinesis Data Firehose can ingest high-velocity clickstream data and deliver it to Amazon S3, where it is stored in a schema-on-read format (e.g., Parquet or ORC). Amazon Athena then allows SQL queries directly on the data in S3 with sub-second latency when using partitions, columnar formats, and optimizations like AWS Glue Catalog. This combination meets the requirements for real-time analytics without predefining a schema.
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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