The answer is Amazon Kinesis Data Firehose and Amazon Kinesis Data Analytics. Kinesis Data Firehose is the ideal ingestion service for reliably capturing high-throughput IoT streaming data—up to 5 GB per hour—and delivering it directly to Amazon S3 for storage, while Kinesis Data Analytics enables near real-time analysis on the data stream before or after it lands in S3, using SQL or Apache Flink to detect patterns and anomalies. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this pairing tests your understanding of building a streaming data pipeline for ML workloads, where you must distinguish Firehose’s managed delivery from services like SQS (which decouples applications but lacks streaming analytics) or Lambda (a compute trigger, not a streaming engine). A common trap is choosing Athena for real-time analysis, but Athena is for ad-hoc queries on data already in S3, not for processing live streams. Remember the mnemonic “Firehose for flow, Analytics for action” to recall that Firehose handles the heavy lifting of ingestion and S3 delivery, while Analytics provides the near real-time processing engine.
MLS-C01 Data Engineering 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. 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 ingestion pipeline that will receive up to 5 GB of data per hour from thousands of IoT devices. The data must be stored in Amazon S3 and analyzed in near real-time. Which TWO services should be used together to meet these requirements? (Choose TWO.)
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 Analytics
Amazon Kinesis Data Firehose can ingest streaming data and deliver to S3. Amazon Kinesis Data Analytics can perform near real-time analysis on the data stream. Option A (Amazon SQS) is for decoupling applications, not streaming analytics. Option B (AWS Lambda) can be used for processing but is not a streaming analytics service. Option E (Amazon Athena) is for ad-hoc queries on S3, not real-time.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
AWS Lambda
Why it's wrong here
Lambda can process data but is not a streaming analytics service; it is better suited for event-driven processing.
✓
Amazon Kinesis Data Analytics
Why this is correct
Kinesis Data Analytics can run SQL queries on streaming data for near real-time analysis.
Related concept
Read the scenario before looking for a memorised answer.
✗
Amazon Athena
Why it's wrong here
Athena is an interactive query service on S3, not for real-time streaming analysis.
✓
Amazon Kinesis Data Firehose
Why this is correct
Firehose can ingest streaming data and deliver to S3 with near real-time latency.
Related concept
Read the scenario before looking for a memorised answer.
✗
Amazon Simple Queue Service (Amazon SQS)
Why it's wrong here
SQS is a message queue, not designed for real-time analytics on streaming data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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
How to think about this question
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.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
What to study next
Got this wrong? Here's your next step.
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.
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Amazon Kinesis Data Analytics — Amazon Kinesis Data Firehose can ingest streaming data and deliver to S3. Amazon Kinesis Data Analytics can perform near real-time analysis on the data stream. Option A (Amazon SQS) is for decoupling applications, not streaming analytics. Option B (AWS Lambda) can be used for processing but is not a streaming analytics service. Option E (Amazon Athena) is for ad-hoc queries on S3, not real-time.
What should I do if I get this MLS-C01 question wrong?
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data engineer needs to process streaming data from an IoT fleet and store the results in Amazon S3 for analysis. The solution must be serverless and handle data that arrives at irregular intervals. Which AWS service should be used to ingest the data?
easy
A.Amazon S3
✓ B.AWS IoT Core
C.Amazon Simple Queue Service (SQS)
D.Amazon Kinesis Data Streams
Why B: Option B is correct because AWS IoT Core is designed to ingest data from IoT devices securely and at scale, and it integrates with other AWS services for processing. Option A is wrong because Kinesis Data Streams is for real-time streaming but not specifically for IoT device connectivity. Option C is wrong because SQS is a message queue, not optimized for IoT ingestion. Option D is wrong because S3 is storage, not ingestion.
Last reviewed: Jun 20, 2026
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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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