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Data Ingestion and Transformation →mediumMultiple ChoiceObjective-mapped

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 company is using Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data must be transformed from JSON to Parquet format before landing in S3. The transformation logic is simple: convert the JSON schema to Parquet. Which approach meets the requirements with the least operational overhead?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "least"
Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.

A
Use the built-in data format conversion feature of Firehose with an AWS Glue Data Catalog table
Firehose can convert to Parquet automatically.
B
Use an AWS Lambda function to transform records to Parquet before sending to Firehose
Why wrong: Adds complexity and cost for Lambda execution.
C
Use Amazon Kinesis Data Analytics to convert the stream to Parquet
Why wrong: Unnecessarily complex for simple format conversion.
D
Provision an Amazon EMR cluster to convert the data in micro-batches
Why wrong: High operational overhead and not real-time.

Full breakdown with real-world context →

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Use the built-in data format conversion feature of Firehose with an AWS Glue Data Catalog table

Option A is correct because Amazon Kinesis Data Firehose provides a built-in data format conversion feature that can automatically convert incoming JSON data to Parquet format using an AWS Glue Data Catalog table as the schema reference. This approach requires no custom code, no additional infrastructure, and no manual transformation logic, making it the simplest solution with the least operational overhead for a straightforward JSON-to-Parquet conversion.

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 the built-in data format conversion feature of Firehose with an AWS Glue Data Catalog table
Why this is correct
Firehose can convert to Parquet automatically.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use an AWS Lambda function to transform records to Parquet before sending to Firehose
Why it's wrong here
Adds complexity and cost for Lambda execution.
✗
Use Amazon Kinesis Data Analytics to convert the stream to Parquet
Why it's wrong here
Unnecessarily complex for simple format conversion.
✗
Provision an Amazon EMR cluster to convert the data in micro-batches
Why it's wrong here
High operational overhead and not real-time.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often overcomplicate the solution by choosing Lambda or EMR, not realizing that Firehose's built-in format conversion with Glue Data Catalog is the simplest, fully managed option for JSON-to-Parquet conversion without any custom code.

Detailed technical explanation

How to think about this question

Under the hood, Firehose's built-in format conversion uses the AWS Glue Data Catalog to infer the Parquet schema from the JSON data, then writes the converted records to S3 in optimized columnar storage. This feature leverages the same Parquet writer libraries used by AWS Glue ETL, but without requiring any Spark or serverless compute; it operates within the Firehose delivery stream's buffer interval (60 seconds default) and buffer size (up to 128 MB), ensuring minimal latency. A real-world scenario where this matters is when ingesting high-volume clickstream data that must be queried by Amazon Athena or Amazon Redshift Spectrum, as Parquet reduces storage costs and improves query performance by up to 10x compared to JSON.

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.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

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.

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FAQ

Questions learners often ask

What does this DEA-C01 question test?

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 the built-in data format conversion feature of Firehose with an AWS Glue Data Catalog table — Option A is correct because Amazon Kinesis Data Firehose provides a built-in data format conversion feature that can automatically convert incoming JSON data to Parquet format using an AWS Glue Data Catalog table as the schema reference. This approach requires no custom code, no additional infrastructure, and no manual transformation logic, making it the simplest solution with the least operational overhead for a straightforward JSON-to-Parquet conversion.

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: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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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.