MLS-C01 Amazon Kinesis Data Firehose Practice Question
A data engineer is designing a data pipeline to process streaming data from Amazon Kinesis Data Streams and store the results in Amazon S3 in Parquet format. The data must be available for querying in Amazon Athena within minutes of arrival. Which THREE services should be used together? (Choose THREE.)
⚠ Common exam trap
The trap is that candidates may overlook Kinesis Data Analytics if they think only Firehose and Glue are needed, but Data Analytics enables real-time processing transformations that are often required in machine learning pipelines. Alternatively, they might incorrectly include EMR or Redshift, which are not necessary for this simple streaming-to-S3 pattern.
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
✓
Amazon Kinesis Data Firehose
Amazon Kinesis Data Firehose can directly ingest streaming data from Kinesis Data Streams, convert it to Parquet, and deliver to S3 with low latency. Amazon Kinesis Data Analytics can process and analyze the stream in real-time (e.g., aggregations or filtering) before sending to Firehose. AWS Glue provides a data catalog for the S3 data, making it queryable by Athena. Together, these three services enable near-real-time querying of streaming data in Athena.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon EMR
Why it's wrong here
EMR is for batch processing, not for streaming ingestion.
- ✗
Amazon Redshift
Why it's wrong here
Redshift is a data warehouse, not needed for Athena querying on S3.
- ✓
Amazon Kinesis Data Firehose
Why this is correct
Amazon Kinesis Data Firehose is correct because it is a fully managed service that can directly ingest streaming data from Kinesis Data Streams, convert it to Parquet format, and deliver it to Amazon S3 with minimal latency (typically 60 seconds). This enables near-real-time querying via Athena without custom code or infrastructure management.
- ✓
Amazon Kinesis Data Analytics
Why this is correct
Amazon Kinesis Data Analytics can process the streaming data in real-time (e.g., aggregations, filtering) before sending it to Firehose, enabling transformations that are often required in machine learning pipelines.
- ✓
AWS Glue
Why this is correct
AWS Glue is correct because it provides a data catalog that makes the data in S3 queryable by Athena. Glue can crawl the S3 data to update the catalog schema, enabling Athena to run SQL queries on the Parquet files.
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.