- A
Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose
Kinesis Data Streams ingests high-throughput data; Kinesis Data Firehose buffers and delivers data to S3 within minutes.
- B
AWS IoT Core and Amazon DynamoDB Streams
Why wrong: IoT Core connects devices, but DynamoDB Streams is for CDC from DynamoDB, not for direct S3 delivery.
- C
Amazon SQS and AWS Lambda
Why wrong: SQS and Lambda can process streaming data, but they are not optimized for high-throughput IoT ingestion and may not meet the sub-minute delivery requirement consistently.
- D
Amazon Kinesis Data Analytics and AWS Glue ETL
Why wrong: Kinesis Data Analytics is for real-time analytics, not storage. AWS Glue ETL is batch-oriented and not designed for streaming to S3 in minutes.
Question 909 of 1,000
mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A machine learning engineer needs to ingest…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 machine learning engineer needs to ingest streaming data from thousands of IoT devices into Amazon S3 for batch training. The data should be available in S3 within minutes of arrival. Which combination of services should the engineer use?
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 and Amazon Kinesis Data Firehose
Amazon Kinesis Data Streams ingests and stores streaming data from thousands of IoT devices durably, while Amazon Kinesis Data Firehose automatically delivers that data to Amazon S3 with near-real-time latency (typically 60–90 seconds). This combination provides the required buffering, scaling, and direct S3 integration without custom code, meeting the 'within minutes' requirement for batch training data.
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 Streams and Amazon Kinesis Data Firehose
Why this is correct
Kinesis Data Streams ingests high-throughput data; Kinesis Data Firehose buffers and delivers data to S3 within minutes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AWS IoT Core and Amazon DynamoDB Streams
Why it's wrong here
IoT Core connects devices, but DynamoDB Streams is for CDC from DynamoDB, not for direct S3 delivery.
- ✗
Amazon SQS and AWS Lambda
Why it's wrong here
SQS and Lambda can process streaming data, but they are not optimized for high-throughput IoT ingestion and may not meet the sub-minute delivery requirement consistently.
- ✗
Amazon Kinesis Data Analytics and AWS Glue ETL
Why it's wrong here
Kinesis Data Analytics is for real-time analytics, not storage. AWS Glue ETL is batch-oriented and not designed for streaming to S3 in minutes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose AWS IoT Core (Option B) because it seems IoT-specific, but they overlook that IoT Core does not natively stream data into S3 with low latency—it requires an additional integration like Kinesis or Lambda, making the direct Kinesis Data Streams + Firehose pipeline the correct and simpler choice.
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 accumulate records before writing to S3, ensuring data lands within minutes. Under the hood, Kinesis Data Streams shards provide ordered, replayable records with a 24-hour default retention (extendable to 365 days), which allows reprocessing if needed. In a real-world scenario with 10,000 IoT sensors emitting 1 KB messages every second, a single Kinesis Data Stream with 100 shards can handle 100 MB/s input, and Firehose can automatically partition data in S3 by timestamp or custom prefixes for efficient batch training.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance, and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance, and Security.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-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 MLA-C01 question test?
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 and Amazon Kinesis Data Firehose — Amazon Kinesis Data Streams ingests and stores streaming data from thousands of IoT devices durably, while Amazon Kinesis Data Firehose automatically delivers that data to Amazon S3 with near-real-time latency (typically 60–90 seconds). This combination provides the required buffering, scaling, and direct S3 integration without custom code, meeting the 'within minutes' requirement for batch training data.
What should I do if I get this MLA-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 →
Last reviewed: Jul 4, 2026
This MLA-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 MLA-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.