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 needs to store semi-structured JSON data for a real-time analytics application. The data will be queried using SQL-like statements and must support high-speed ingestion with minimal latency. Which AWS service is best suited for this use case?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Amazon S3
Why wrong: S3 is object storage and does not support real-time SQL queries directly.
Amazon Redshift
Why wrong: Redshift is for batch analytics, not real-time streaming.
Amazon DynamoDB
Why wrong: DynamoDB is a NoSQL database with limited SQL support and not optimized for streaming queries.
Amazon Kinesis Data Analytics
Kinesis Data Analytics can query streaming data using SQL in real time.
Answer choices
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 Analytics
Amazon Kinesis Data Analytics is best suited because it natively processes streaming JSON data using SQL-like statements (via Kinesis Data Analytics for SQL applications) with sub-second latency, enabling real-time analytics on semi-structured data without requiring a separate storage layer for ingestion. It directly integrates with Kinesis Data Streams or Firehose for high-speed ingestion and supports in-application queries on JSON payloads using the `json_extract` function or schema discovery.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Amazon S3
Why it's wrong here
S3 is object storage and does not support real-time SQL queries directly.
Amazon Redshift
Why it's wrong here
Redshift is for batch analytics, not real-time streaming.
Amazon DynamoDB
Why it's wrong here
DynamoDB is a NoSQL database with limited SQL support and not optimized for streaming queries.
Amazon Kinesis Data Analytics
Why this is correct
Kinesis Data Analytics can query streaming data using SQL in real time.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse 'SQL-like queries' with traditional relational databases and pick Amazon Redshift, overlooking that Kinesis Data Analytics provides SQL-on-streaming capabilities specifically designed for real-time, semi-structured data without the batch-oriented latency of data warehouses.
Detailed technical explanation
Under the hood, Kinesis Data Analytics for SQL applications uses a continuous query engine that processes data in-memory using a sliding window approach, allowing real-time aggregations (e.g., TUMBLING, HOPPING windows) on JSON fields without persisting raw data to disk. A subtle behavior is that JSON schema discovery occurs at application startup, and fields not explicitly cast may be treated as VARCHAR, requiring careful use of `CAST` or `json_extract` to avoid type mismatches in streaming pipelines. In a real-world scenario, this service is ideal for clickstream analytics where JSON events (e.g., user actions with nested fields) must be aggregated per minute for dashboards, while S3 would introduce unacceptable latency for the same use case.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Data Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon Kinesis Data Analytics — Amazon Kinesis Data Analytics is best suited because it natively processes streaming JSON data using SQL-like statements (via Kinesis Data Analytics for SQL applications) with sub-second latency, enabling real-time analytics on semi-structured data without requiring a separate storage layer for ingestion. It directly integrates with Kinesis Data Streams or Firehose for high-speed ingestion and supports in-application queries on JSON payloads using the `json_extract` function or schema discovery.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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.
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