MLS-C01 Data Engineering Practice Question
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?
⚠ Common exam trap
Candidates often assume they need a separate transformation service like Lambda or Glue, not realizing that Firehose itself has a native, serverless data format conversion feature that directly writes Parquet to S3.
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
✓
Enable data format conversion in Firehose and specify a Glue table
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable data format conversion in Firehose and specify a Glue table
Why this is correct
Firehose can convert to Parquet using a Glue table schema.
- ✗
Use an AWS Lambda function to transform the data
Why it's wrong here
Lambda can be used but Firehose has built-in conversion.
- ✗
Run an AWS Glue ETL job after data is in S3
Why it's wrong here
Post-processing adds latency; Firehose can do it in transit.
- ✗
Use Kinesis Data Analytics for Apache Flink to convert the format
Why it's wrong here
KDA is for stream processing, not format conversion for Firehose.
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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.