- A
Amazon Kinesis Data Firehose
Kinesis Data Firehose is a serverless service that can directly deliver streaming data to S3 with buffering.
- B
Amazon Kinesis Data Streams
Why wrong: Kinesis Data Streams is a real-time streaming service but requires a custom consumer to write to S3; not a direct buffer for S3.
- C
AWS Glue
Why wrong: AWS Glue is a batch ETL service, not suitable for real-time streaming ingestion.
- D
Amazon Simple Queue Service (SQS)
Why wrong: SQS is a message queue service, not designed for high-throughput streaming data ingestion into S3.
Question 1,598 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 needs to continuously ingest streaming data from thousands of IoT devices and store the raw data in Amazon S3 for archival processing. The data volume varies significantly throughout the day, and the solution must be serverless, scalable, and cost-effective. Which AWS service should be used to capture and buffer the streaming data before writing to S3?
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, serverless service designed to reliably capture, buffer, and automatically load streaming data into Amazon S3 without requiring any custom code or infrastructure management. It handles variable data volumes by scaling automatically and provides built-in buffering (up to 128 MB or 900 seconds) before writing to S3, making it cost-effective for archival 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.
- ✓
Amazon Kinesis Data Firehose
Why this is correct
Kinesis Data Firehose is a serverless service that can directly deliver streaming data to S3 with buffering.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon Kinesis Data Streams
Why it's wrong here
Kinesis Data Streams is a real-time streaming service but requires a custom consumer to write to S3; not a direct buffer for S3.
- ✗
AWS Glue
Why it's wrong here
AWS Glue is a batch ETL service, not suitable for real-time streaming ingestion.
- ✗
Amazon Simple Queue Service (SQS)
Why it's wrong here
SQS is a message queue service, not designed for high-throughput streaming data ingestion into S3.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Kinesis Data Streams (a real-time processing layer requiring custom consumers) with Kinesis Data Firehose (a managed delivery service), and overlook that Firehose's built-in buffering and direct S3 integration make it the serverless, cost-effective choice for archival ingestion.
Detailed technical explanation
How to think about this question
Kinesis Data Firehose uses a configurable buffer size (1 MB to 128 MB) and buffer interval (60 to 900 seconds) to accumulate records before writing to S3, which optimizes cost by reducing the number of S3 PUT requests. Under the hood, Firehose can optionally transform data using AWS Lambda and compress it (e.g., GZIP, Snappy) before delivery, which is critical for minimizing storage costs in archival scenarios. In real-world deployments, Firehose can ingest up to 5,000 records per second per delivery stream and automatically retries failed S3 writes, ensuring data durability.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Data Engineering — study guide chapter
Learn the concepts, then practise the questions
- →
Data Engineering practice questions
Targeted practice on this topic area only
- →
All MLS-C01 questions
1,755 questions across all exam domains
- →
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLS-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Engineering practice questions
Practise MLS-C01 questions linked to Data Engineering.
Machine Learning Implementation and Operations practice questions
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Modeling practice questions
Practise MLS-C01 questions linked to Modeling.
Exploratory Data Analysis practice questions
Practise MLS-C01 questions linked to Exploratory Data Analysis.
MLS-C01 fundamentals practice questions
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
MLS-C01 scenario practice questions
Practise MLS-C01 questions linked to MLS-C01 scenario.
MLS-C01 troubleshooting practice questions
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Start a free MLS-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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, serverless service designed to reliably capture, buffer, and automatically load streaming data into Amazon S3 without requiring any custom code or infrastructure management. It handles variable data volumes by scaling automatically and provides built-in buffering (up to 128 MB or 900 seconds) before writing to S3, making it cost-effective for archival 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 →
Keep practising
More MLS-C01 practice questions
- A company needs to transfer 10 TB of data from an on-premises data center to Amazon S3. The network bandwidth is limited…
- A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Lambda fu…
- A team is building a data pipeline to process terabytes of log data daily using Amazon EMR. The data arrives in 5-minute…
- A data science team is building a real-time fraud detection system. Transactions are streamed via Amazon Kinesis Data St…
- A company uses Amazon SageMaker to train and deploy machine learning models. The training data is stored in Amazon S3 (P…
- A data engineering team is designing a data lake on AWS for machine learning workloads. The data includes structured, se…
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.