- A
Send the data to Amazon Kinesis Data Analytics to convert to Parquet
Why wrong: Kinesis Data Analytics is for analytics, not format conversion.
- B
Configure Kinesis Data Firehose to convert the record format to Parquet using a schema from AWS Glue Data Catalog
Firehose can convert JSON to Parquet using a Glue Data Catalog schema.
- C
Use an AWS Lambda function to transform JSON to Parquet and write to S3
Why wrong: Lambda can transform but would need to be triggered by Firehose, which is not native.
- D
Use an AWS Glue ETL job to read from Firehose and write Parquet to S3
Why wrong: Glue is not a real-time streaming solution; Firehose handles streaming.
Question 488 of 1,755
Convert JSON to Parquet in Kinesis Firehose with AWS Glue Schema
This MLS-C01 practice question tests your understanding of data engineering. 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 an S3 bucket. The data is JSON and must be transformed into Parquet format before delivery. Which approach should the data engineer use?
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
✓
Configure Kinesis Data Firehose to convert the record format to Parquet using a schema from AWS Glue Data Catalog
Option B is correct because Amazon Kinesis Data Firehose can directly convert incoming JSON records to Parquet format by referencing a schema stored in the AWS Glue Data Catalog. This is a built-in feature of Firehose that does not require additional services for the conversion. Option A is wrong because Kinesis Data Analytics is for real-time analytics, not format conversion. Option C is wrong because while Lambda can transform data, using it for Parquet conversion adds latency and complexity; Firehose's native conversion is simpler. Option D is wrong because an AWS Glue ETL job is for batch processing, not real-time streaming transformation.
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.
- ✗
Send the data to Amazon Kinesis Data Analytics to convert to Parquet
Why it's wrong here
Kinesis Data Analytics is for analytics, not format conversion.
- ✓
Configure Kinesis Data Firehose to convert the record format to Parquet using a schema from AWS Glue Data Catalog
Why this is correct
Firehose can convert JSON to Parquet using a Glue Data Catalog schema.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use an AWS Lambda function to transform JSON to Parquet and write to S3
Why it's wrong here
Lambda can transform but would need to be triggered by Firehose, which is not native.
- ✗
Use an AWS Glue ETL job to read from Firehose and write Parquet to S3
Why it's wrong here
Glue is not a real-time streaming solution; Firehose handles streaming.
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.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure Kinesis Data Firehose to convert the record format to Parquet using a schema from AWS Glue Data Catalog — Option B is correct because Amazon Kinesis Data Firehose can directly convert incoming JSON records to Parquet format by referencing a schema stored in the AWS Glue Data Catalog. This is a built-in feature of Firehose that does not require additional services for the conversion. Option A is wrong because Kinesis Data Analytics is for real-time analytics, not format conversion. Option C is wrong because while Lambda can transform data, using it for Parquet conversion adds latency and complexity; Firehose's native conversion is simpler. Option D is wrong because an AWS Glue ETL job is for batch processing, not real-time streaming transformation.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-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.
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 →
Same concept, more angles
2 more ways this is tested on MLS-C01
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 deliver streaming data to Amazon S3. The data is in JSON format, and the company wants to convert it to Parquet for efficient querying. Which configuration should be used?
medium- ✓ A.Enable data transformation in Firehose using an AWS Lambda function to convert JSON to Parquet, and set the output format to Parquet.
- B.Use an AWS Glue job to convert the JSON files in S3 to Parquet after delivery.
- C.Use Amazon Kinesis Data Analytics to convert the stream to Parquet before sending to Firehose.
- D.Configure Firehose to deliver data directly to Amazon Redshift, which automatically converts to Parquet.
Why A: Option A is correct because Amazon Kinesis Data Firehose supports data transformation via AWS Lambda, allowing you to convert incoming JSON records to Parquet format before delivery to S3. By enabling a Lambda function to perform the conversion and setting the output format to Parquet, Firehose handles the transformation in-stream, ensuring the data lands in S3 already in the optimized columnar format for efficient querying with services like Amazon Athena or Amazon Redshift Spectrum.
Variation 2. A company is using Amazon Kinesis Data Firehose to load streaming data into Amazon S3. The data is in JSON format, and they want to convert it to Parquet before storage. What should they configure?
easy- ✓ A.Enable data format conversion in Firehose and specify a Glue table
- B.Use an AWS Lambda function to transform the data
- C.Run an AWS Glue ETL job after data is in S3
- D.Use Kinesis Data Analytics for Apache Flink to convert the format
Why A: Amazon Kinesis Data Firehose supports built-in data format conversion from JSON to Parquet or ORC. By enabling this feature and specifying an AWS Glue table that defines the schema, Firehose automatically converts incoming JSON records to Parquet before delivering them to the S3 destination. This eliminates the need for additional compute resources or post-processing steps.
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Last reviewed: Jun 20, 2026
This MLS-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 MLS-C01 exam.
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