The answer is to use the built-in data format conversion feature of Kinesis Firehose with an AWS Glue Data Catalog table. This approach is correct because Firehose can natively convert JSON to Parquet by referencing a schema stored in the Glue Data Catalog, eliminating the need for any custom code or additional infrastructure. The service handles the transformation automatically during delivery, making it the most operationally efficient solution. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your understanding of Firehose’s native capabilities versus over-engineering with Lambda or Kinesis Data Analytics. A common trap is assuming you always need a custom transformation function, but Firehose’s built-in format conversion is purpose-built for simple schema-based conversions like JSON to Parquet. Remember the mnemonic: “Firehose + Glue = No code, just schema.”
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.
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 Firehose can natively convert JSON to Parquet using a schema from AWS Glue Data Catalog, without custom code. Option B (Lambda) requires writing and maintaining code. Option C (Kinesis Data Analytics) is overkill. Option D (EC2) adds management overhead.
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.
✓
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
Static NAT maps one inside address to one outside address.
✗
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: 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 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.
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: Use the built-in data format conversion feature of Firehose with an AWS Glue Data Catalog table — Option A is correct because Firehose can natively convert JSON to Parquet using a schema from AWS Glue Data Catalog, without custom code. Option B (Lambda) requires writing and maintaining code. Option C (Kinesis Data Analytics) is overkill. Option D (EC2) adds management overhead.
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.
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?
Static NAT maps one inside address to one outside address.
About these practice questions
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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 is using Amazon Kinesis Data Firehose to deliver streaming data to an Amazon S3 bucket. The data is delivered in JSON format. The company wants to convert the data to Apache Parquet format before delivery to reduce storage costs and improve query performance. How can this be achieved?
medium
A.Deliver data to S3 as JSON, then use Amazon Athena to convert to Parquet.
✓ B.Use the AWS Glue Data Catalog to define a schema and configure Firehose to use it for Parquet conversion.
C.Write an AWS Lambda function to transform the data to Parquet and deliver it to S3.
D.Configure the Firehose stream to convert data to Parquet automatically without any additional setup.
Why B: Option B is correct because Kinesis Data Firehose can convert the input data to Parquet or ORC format using a schema from the AWS Glue Data Catalog. Option A is incorrect because Firehose does not support a built-in transformation to Parquet without a schema. Option C is incorrect because Lambda can be used for custom transformations, but Firehose natively supports Parquet conversion using Glue. Option D is incorrect because Athena is a query service, not a transformation service.
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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.
Question Discussion
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