- A
Amazon Kinesis Data Firehose
Firehose can load streaming data directly into S3 with near real-time latency.
- B
AWS Glue
Why wrong: Glue is a batch ETL service, not designed for real-time ingestion.
- C
Amazon Kinesis Data Streams
Why wrong: Kinesis Streams requires custom consumers and does not directly write to S3.
- D
Amazon Simple Queue Service (SQS)
Why wrong: SQS is a message queue, not optimized for large-scale streaming ingestion to S3.
Question 629 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 lake on Amazon S3. The data is collected from IoT devices and is highly variable in volume. The engineer needs to ensure that the data is ingested reliably and can be processed in near real-time. Which AWS service should be used to ingest the data into the data lake?
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 Firehose
Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service designed to reliably load streaming data into data lakes on Amazon S3 with near-real-time latency (typically 60 seconds). It automatically handles scaling to accommodate highly variable IoT data volumes, provides built-in data transformation and compression, and requires no manual shard management or consumer code, making it ideal for ingestion into S3-based data lakes.
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 Kinesis Data Firehose
Why this is correct
Firehose can load streaming data directly into S3 with near real-time latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AWS Glue
Why it's wrong here
Glue is a batch ETL service, not designed for real-time ingestion.
- ✗
Amazon Kinesis Data Streams
Why it's wrong here
Kinesis Streams requires custom consumers and does not directly write to S3.
- ✗
Amazon Simple Queue Service (SQS)
Why it's wrong here
SQS is a message queue, not optimized for large-scale streaming ingestion to S3.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Amazon Kinesis Data Streams (a raw streaming service requiring custom consumers) with Amazon Kinesis Data Firehose (a fully managed delivery service), leading them to select Data Streams for direct S3 ingestion when it actually requires additional code and infrastructure to write to S3.
Detailed technical explanation
How to think about this question
Under the hood, Kinesis Data Firehose uses a buffer interval (default 60 seconds) or buffer size (default 5 MB) to batch records before writing to S3, which enables near-real-time delivery while minimizing small file issues that degrade query performance in analytics engines like Athena or Spark. It also supports optional Lambda-based data transformation and can automatically convert data formats (e.g., JSON to Parquet or ORC) to optimize storage and query efficiency. In a real-world IoT scenario with bursty traffic, Firehose scales seamlessly by distributing load across internal shards without requiring you to pre-provision capacity, unlike Kinesis Data Streams which requires shard management.
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 Firehose — Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service designed to reliably load streaming data into data lakes on Amazon S3 with near-real-time latency (typically 60 seconds). It automatically handles scaling to accommodate highly variable IoT data volumes, provides built-in data transformation and compression, and requires no manual shard management or consumer code, making it ideal for ingestion into S3-based data lakes.
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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