Quick Answer
The correct answer is Amazon Kinesis Data Streams and AWS Glue ETL jobs. Kinesis Data Streams provides the durable, real-time ingestion layer for streaming social media data, while AWS Glue ETL jobs can directly read from the stream, perform streaming deduplication and enrichment by connecting to a relational database via JDBC, and write the results to Amazon S3 in Parquet format. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how to build a streaming pipeline that requires both transformation logic and external data lookups—a common pattern for real-time feature engineering. A frequent trap is selecting Kinesis Data Firehose, which cannot perform enrichment from a relational database, or Amazon Athena, which is a query engine, not an ETL tool. Remember the memory tip: “Streams for raw, Glue for the chew”—Kinesis ingests the raw stream, and Glue chews on deduplication and enrichment before writing to S3.
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 engineering team is designing a data pipeline to process streaming data from social media feeds. The data must be deduplicated, enriched with customer information from a relational database, and stored in Amazon S3 in Parquet format. Which AWS services should the team use to build this pipeline? (Select TWO.)
AWS Glue
Glue ETL can transform and enrich data from streams and databases.
Amazon Kinesis Data Firehose
Why wrong: Firehose cannot perform enrichment with a relational database.
Amazon Athena
Why wrong: Athena is for querying, not ETL.
Amazon SageMaker
Why wrong: SageMaker is for ML models, not data pipeline.
Amazon Kinesis Data Streams
Ingests streaming social media data.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
AWS Glue
Option A is correct because Kinesis Data Streams ingests streaming social media data. Option D is correct because AWS Glue ETL jobs can read from the stream, perform deduplication and enrichment using JDBC connections to the relational database, and write to S3 in Parquet. Option B is wrong because Kinesis Data Firehose does not support enrichment with a relational database. Option C is wrong because Athena is a query engine, not an ETL tool. Option E is wrong because SageMaker is for ML, not data pipeline.
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.
AWS Glue
Why this is correct
Glue ETL can transform and enrich data from streams and databases.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Kinesis Data Firehose
Why it's wrong here
Firehose cannot perform enrichment with a relational database.
Amazon Athena
Why it's wrong here
Athena is for querying, not ETL.
Amazon SageMaker
Why it's wrong here
SageMaker is for ML models, not data pipeline.
Amazon Kinesis Data Streams
Why this is correct
Ingests streaming social media data.
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: AWS Glue — Option A is correct because Kinesis Data Streams ingests streaming social media data. Option D is correct because AWS Glue ETL jobs can read from the stream, perform deduplication and enrichment using JDBC connections to the relational database, and write to S3 in Parquet. Option B is wrong because Kinesis Data Firehose does not support enrichment with a relational database. Option C is wrong because Athena is a query engine, not an ETL tool. Option E is wrong because SageMaker is for ML, not data pipeline.
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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