- A
AWS Lambda
Why wrong: Lambda can process data but is not a streaming analytics service; it is better suited for event-driven processing.
- B
Amazon Kinesis Data Analytics
Kinesis Data Analytics can run SQL queries on streaming data for near real-time analysis.
- C
Amazon Athena
Why wrong: Athena is an interactive query service on S3, not for real-time streaming analysis.
- D
Amazon Kinesis Data Firehose
Firehose can ingest streaming data and deliver to S3 with near real-time latency.
- E
Amazon Simple Queue Service (Amazon SQS)
Why wrong: SQS is a message queue, not designed for real-time analytics on streaming data.
Question 353 of 1,755
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 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 Analytics
Amazon Kinesis Data Firehose is the correct service because it can reliably ingest streaming data from thousands of IoT devices at up to 5 GB per hour, automatically buffer, compress, and deliver the data to Amazon S3 with near-real-time latency (typically 60 seconds). Amazon Kinesis Data Analytics is correct because it enables real-time SQL-based analytics on the streaming data before it is stored in S3, allowing the data engineer to derive insights as data arrives without needing to query the S3 bucket after storage.
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
The trap here is that candidates often confuse Amazon Kinesis Data Firehose with Amazon Kinesis Data Streams, or mistakenly think Amazon Athena can ingest streaming data because it can query S3 in near-real-time, but Athena is purely a query engine and cannot replace the ingestion and streaming analytics components required for this pipeline.
Detailed technical explanation
How to think about this question
Kinesis Data Firehose uses a configurable buffer interval (default 60 seconds, minimum 60 seconds) and buffer size (default 5 MB) to batch records before writing to S3, ensuring near-real-time delivery while minimizing small object overhead. Kinesis Data Analytics can run in-application SQL queries on the stream using a Flink-based runtime, allowing windowed aggregations (e.g., sliding windows of 1 minute) to detect anomalies or compute averages before the data lands in S3. In a real-world IoT scenario, this combination enables alerting on sensor thresholds within seconds, while the raw data is durably stored in S3 for historical analysis.
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.
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
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.
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
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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 is the correct service because it can reliably ingest streaming data from thousands of IoT devices at up to 5 GB per hour, automatically buffer, compress, and deliver the data to Amazon S3 with near-real-time latency (typically 60 seconds). Amazon Kinesis Data Analytics is correct because it enables real-time SQL-based analytics on the streaming data before it is stored in S3, allowing the data engineer to derive insights as data arrives without needing to query the S3 bucket after storage.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 →
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Last reviewed: Jul 4, 2026
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