The answer is to use S3 event notifications to invoke a Lambda function that runs PySpark to convert the data. This approach is correct because it creates a fully serverless, event-driven pipeline: whenever a new JSON file lands in the S3 bucket, the event notification triggers the Lambda function, which uses PySpark to read the JSON, transform it into columnar Parquet format, and write it back to S3 for efficient Athena querying. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of serverless orchestration and the limitations of each service—Athena is query-only and cannot transform data, AWS Glue requires a scheduled or manual job trigger (not purely event-driven), and Amazon EMR is not serverless. A common trap is choosing Glue crawlers or Athena CTAS statements, but neither performs on-the-fly conversion triggered by an S3 event. Memory tip: “Event triggers Lambda, Lambda runs Spark, Parquet lands in S3—Athena queries happy.”
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. 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 transform JSON data from an S3 bucket into Parquet format for efficient querying with Amazon Athena. The transformation must be serverless and event-driven. Which approach meets these requirements?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use S3 event notifications to invoke a Lambda function that runs PySpark to convert the data.
S3 event notifications trigger a Lambda function that uses PySpark to convert JSON to Parquet. Athena is query-only, Glue requires a job trigger, and EMR is not serverless.
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.
✗
Use AWS Glue with a scheduled crawler to convert the data.
Why it's wrong here
Glue crawlers infer schema, they don't transform data; a Glue ETL job is needed but is not event-driven by default.
✗
Use Amazon Athena to convert JSON to Parquet on the fly.
Why it's wrong here
Athena queries data but does not transform and write it in a different format; it reads data as is.
✓
Use S3 event notifications to invoke a Lambda function that runs PySpark to convert the data.
Why this is correct
Lambda can run PySpark (e.g., using AWS Glue ETL library) and is triggered by S3 events, making it serverless and event-driven.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon EMR with a long-running cluster to process S3 data.
Why it's wrong here
EMR is not serverless and requires cluster management.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use S3 event notifications to invoke a Lambda function that runs PySpark to convert the data. — S3 event notifications trigger a Lambda function that uses PySpark to convert JSON to Parquet. Athena is query-only, Glue requires a job trigger, and EMR is not serverless.
What should I do if I get this DEA-C01 question wrong?
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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