- A
Amazon SQS
Why wrong: SQS is a message queue, not designed for streaming analytics. It's better for decoupling microservices.
- B
Amazon Kinesis Data Firehose
Why wrong: Firehose is for near-real-time delivery to S3, Redshift, etc., but does not provide a buffer for processing streams.
- C
Amazon Kinesis Data Streams
Kinesis Data Streams provides a durable buffer for real-time data, enabling multiple consumers.
- D
Amazon MQ
Why wrong: Amazon MQ is a managed message broker, not optimized for high-throughput streaming.
Question 316 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 company needs to ingest real-time clickstream data from thousands of web servers into AWS for near-real-time analytics. The data volume varies and can spike during promotions. Which service should be used to capture and buffer the data before processing?
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 Streams
Amazon Kinesis Data Streams is the correct choice because it is designed for real-time data ingestion and buffering of large streams of data, such as clickstream events from thousands of web servers. It provides durable, low-latency storage (up to 365 days retention) and supports multiple consumers for near-real-time analytics, making it ideal for handling variable and spiky data volumes during promotions.
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.
- ✗
Amazon SQS
Why it's wrong here
SQS is a message queue, not designed for streaming analytics. It's better for decoupling microservices.
- ✗
Amazon Kinesis Data Firehose
Why it's wrong here
Firehose is for near-real-time delivery to S3, Redshift, etc., but does not provide a buffer for processing streams.
- ✓
Amazon Kinesis Data Streams
Why this is correct
Kinesis Data Streams provides a durable buffer for real-time data, enabling multiple consumers.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon MQ
Why it's wrong here
Amazon MQ is a managed message broker, not optimized for high-throughput streaming.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common pitfall is confusing the buffering capabilities of Kinesis Data Streams versus Kinesis Data Firehose. Candidates often choose Firehose because they think 'buffer' implies a simple staging area, but Firehose lacks the multi-consumer and replay capabilities required for near-real-time analytics.
Detailed technical explanation
How to think about this question
Kinesis Data Streams uses shards as the base throughput unit, each providing 1 MB/s write and 2 MB/s read capacity, and you can dynamically scale shards to handle spikes. Under the hood, data is stored in shards for up to 365 days, enabling replay and reprocessing, which is critical for clickstream analytics where late-arriving data or reprocessing for model retraining may be needed. In a real-world scenario, during a promotion, you can use the UpdateShardCount API to increase shards in minutes, ensuring no data loss or throttling.
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
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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Amazon Kinesis Data Streams — Amazon Kinesis Data Streams is the correct choice because it is designed for real-time data ingestion and buffering of large streams of data, such as clickstream events from thousands of web servers. It provides durable, low-latency storage (up to 365 days retention) and supports multiple consumers for near-real-time analytics, making it ideal for handling variable and spiky data volumes during promotions.
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.
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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
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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