- A
Amazon EMR
Why wrong: EMR is for batch processing, not for streaming ingestion.
- B
Amazon Redshift
Why wrong: Redshift is a data warehouse, not needed for Athena querying on S3.
- C
Amazon Kinesis Data Firehose
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.
- D
Amazon Kinesis Data Analytics
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.
- E
AWS Glue
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.
MLS-C01 Amazon Kinesis Data Firehose Practice Question
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. A key principle to apply: amazon Kinesis Data Firehose. 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 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.)
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.
Key principle: Amazon Kinesis Data Firehose
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.
Related concept
Amazon Kinesis Data Firehose
- ✓
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.
Related concept
Amazon Kinesis Data Firehose
- ✓
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.
Related concept
Amazon Kinesis Data Firehose
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Kinesis Data Firehose uses a built-in Parquet converter that relies on a schema from AWS Glue Data Catalog or inline schema definitions, enabling columnar storage for efficient Athena queries. The service buffers incoming data up to 128 MB or 60 seconds (whichever is reached) before writing to S3, ensuring data is available for querying within minutes. In real-world scenarios, this pattern is commonly used for clickstream analytics or IoT sensor data where low-latency querying is critical.
KKey Concepts to Remember
- Amazon Kinesis Data Firehose
- Amazon Kinesis Data Analytics
- AWS Glue
TExam Day Tips
- 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
Amazon Kinesis Data Firehose
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 — Amazon Kinesis Data Firehose.
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLS-C01 question wrong?
Review amazon Kinesis Data Firehose, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Amazon Kinesis Data Firehose
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Last reviewed: Jul 4, 2026
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