Quick Answer
The answer is AWS Glue, used together with Kinesis Data Firehose and Athena, because this trio directly addresses the requirement for processing streaming data to Parquet in S3 for Athena with Firehose and Glue. Kinesis Data Firehose can automatically convert incoming streaming data from Kinesis Data Streams into Parquet format and deliver it to S3 in near-real-time, while AWS Glue provides the Data Catalog that defines the table schema and partition structure, enabling Athena to immediately discover and query the data as it lands. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of serverless, managed services for low-latency analytics pipelines—a common trap is selecting Kinesis Data Analytics for storage or EMR for batch processing, but remember that Firehose handles the conversion and delivery, not real-time computation. A helpful memory tip is “Firehose feeds, Glue catalogs, Athena queries”—think of it as a three-step assembly line for streaming data.
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 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.)
Amazon EMR
Why wrong: EMR is for batch processing, not for streaming ingestion.
Amazon Redshift
Why wrong: Redshift is a data warehouse, not needed for Athena querying on S3.
Amazon Kinesis Data Firehose
Firehose can deliver streaming data to S3 in Parquet format.
Amazon Kinesis Data Analytics
Why wrong: Kinesis Data Analytics is for real-time analytics, not storage.
AWS Glue
Glue provides the Data Catalog for Athena to query.
Answer choices
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
Kinesis Data Firehose can write data to S3 in Parquet format with near-real-time delivery. AWS Glue provides the Data Catalog for table metadata, and Athena queries the data. Option A (Kinesis Data Analytics) is for real-time analytics on streams, not for storage. Option C (EMR) is for batch processing, not streaming. Option E (Redshift) is for data warehousing, not immediate S3 querying.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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
Firehose can deliver streaming data to S3 in Parquet format.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Kinesis Data Analytics
Why it's wrong here
Kinesis Data Analytics is for real-time analytics, not storage.
AWS Glue
Why this is correct
Glue provides the Data Catalog for Athena to query.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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.
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FAQ
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon Kinesis Data Firehose — Kinesis Data Firehose can write data to S3 in Parquet format with near-real-time delivery. AWS Glue provides the Data Catalog for table metadata, and Athena queries the data. Option A (Kinesis Data Analytics) is for real-time analytics on streams, not for storage. Option C (EMR) is for batch processing, not streaming. Option E (Redshift) is for data warehousing, not immediate S3 querying.
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.
Read the scenario before looking for a memorised answer.
About these practice questions
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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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