The answer is to enable data transformation in Kinesis Firehose to convert JSON to Parquet format with Snappy compression. This is correct because Parquet is a columnar storage format that significantly reduces the amount of data Athena must scan per query, while Snappy compression further shrinks file sizes without sacrificing decompression speed, directly addressing slow and expensive queries. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of how Firehose’s built-in data conversion feature optimizes downstream analytics; a common trap is choosing ORC, which is also columnar but less commonly used with Athena, or increasing buffer size, which only delays delivery without improving query performance. Remember the memory tip: “Parquet for performance, Snappy for speed” — columnar storage cuts scan volume, and fast compression keeps queries snappy.
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 ingest log data from web servers into an Amazon S3 bucket. The data is then queried by Amazon Athena. The company has noticed that the Athena queries are slow and expensive. The data engineer wants to optimize the storage format to improve query performance and reduce costs. Which configuration change should the data engineer make to the Firehose delivery stream?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable data transformation in Firehose to convert JSON to Parquet format with Snappy compression.
Option B is correct because converting data to Parquet format and compressing it reduces storage space and improves query performance in Athena. Option A is wrong because storing as ORC is also good but Parquet is more common with Athena. Option C is wrong because increasing buffer size delays delivery. Option D is wrong because enabling S3 server access logs adds cost and does not help query performance.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Increase the buffer interval to 600 seconds and buffer size to 128 MB to create larger files.
Why it's wrong here
Larger files improve performance but format matters more.
✗
Change the output format to ORC and enable GZIP compression.
Why it's wrong here
ORC is valid but Parquet is better for Athena.
✗
Enable S3 server access logs to track query patterns.
Why it's wrong here
This does not optimize query performance.
✓
Enable data transformation in Firehose to convert JSON to Parquet format with Snappy compression.
Why this is correct
Parquet is columnar and efficient for Athena.
Related concept
Static NAT maps one inside address to one outside address.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DEA-C01 NAT questions on configuration and troubleshooting.
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Enable data transformation in Firehose to convert JSON to Parquet format with Snappy compression. — Option B is correct because converting data to Parquet format and compressing it reduces storage space and improves query performance in Athena. Option A is wrong because storing as ORC is also good but Parquet is more common with Athena. Option C is wrong because increasing buffer size delays delivery. Option D is wrong because enabling S3 server access logs adds cost and does not help query performance.
What should I do if I get this DEA-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DEA-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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 company uses Amazon Kinesis Data Firehose to ingest data into an S3 bucket. The data is in JSON format and the team wants to convert it to Parquet before storage. Which TWO configurations are required?
medium
A.Use Kinesis Data Analytics to transform data to Parquet.
✓ B.Create a Glue table with the schema of the data.
C.Configure a Lambda function to convert data on the fly.
D.Set up an Athena table to read the data.
✓ E.Enable data format conversion in Firehose and set Output format to Parquet.
Why B: Options A and B are correct: Firehose can convert to Parquet if you specify a data format conversion and a Glue table (schema). Option C (Kinesis Data Analytics) is not needed. Option D (Athena) is for querying. Option E (Lambda) can be used but is not required.
Variation 2. A company is using Amazon Kinesis Data Firehose to ingest data into Amazon S3. The data must be transformed from JSON to Parquet format before delivery. Which feature should be enabled on the Firehose delivery stream?
easy
A.Amazon Kinesis Data Analytics
B.Amazon S3 event notifications
✓ C.Format conversion (Parquet/ORC)
D.AWS Lambda transformation
Why C: Option D is correct because Firehose can convert the input data format to Parquet or ORC using its built-in format conversion feature. Option A is wrong because Lambda transformation is for custom code, not format conversion. Option B is wrong because S3 events are for notifications, not transformation. Option C is wrong because Kinesis Data Analytics is for stream processing, not directly tied to Firehose.
Last reviewed: Jun 20, 2026
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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.
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