- A
Amazon Kinesis Data Streams → Kinesis Data Analytics → Kinesis Data Firehose → S3
Correct flow for real-time streaming, aggregations, and delivery to S3.
- B
Amazon Kinesis Data Streams → Amazon SageMaker Data Wrangler → S3
Why wrong: SageMaker Data Wrangler is for interactive data preparation, not real-time streaming ingestion.
- C
Amazon Kinesis Data Firehose → AWS Glue ETL → S3
Why wrong: AWS Glue is batch-oriented, not suitable for real-time streaming.
- D
Amazon SQS → AWS Lambda → S3
Why wrong: SQS is for message queuing, not streaming; Lambda can process but lacks native streaming ingestion optimizations.
Question 25 of 1,000
mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data engineer needs to ingest streaming…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 ingest streaming clickstream data from a website into an S3 data lake for ML training, with the ability to run real-time aggregations before storage. Which combination of AWS services meets these requirements?
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 → Kinesis Data Analytics → Kinesis Data Firehose → S3
Option A is correct because it provides a complete pipeline for both real-time aggregation and durable storage. Kinesis Data Streams ingests the streaming clickstream data, Kinesis Data Analytics performs real-time SQL or Apache Flink-based aggregations on the stream, and Kinesis Data Firehose delivers the aggregated results to S3 with optional data transformation and buffering, meeting the requirements for ML training.
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 → Kinesis Data Analytics → Kinesis Data Firehose → S3
Why this is correct
Correct flow for real-time streaming, aggregations, and delivery to S3.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon Kinesis Data Streams → Amazon SageMaker Data Wrangler → S3
Why it's wrong here
SageMaker Data Wrangler is for interactive data preparation, not real-time streaming ingestion.
- ✗
Amazon Kinesis Data Firehose → AWS Glue ETL → S3
Why it's wrong here
AWS Glue is batch-oriented, not suitable for real-time streaming.
- ✗
Amazon SQS → AWS Lambda → S3
Why it's wrong here
SQS is for message queuing, not streaming; Lambda can process but lacks native streaming ingestion optimizations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Kinesis Data Firehose's ability to invoke Lambda for simple transformations with the need for real-time aggregations, overlooking that Kinesis Data Analytics is the only service that provides continuous, stateful stream processing required for real-time aggregations before storage.
Detailed technical explanation
How to think about this question
Kinesis Data Analytics uses Apache Flink under the hood, allowing stateful stream processing with exactly-once semantics for aggregations like windowed counts or sessionization. Kinesis Data Firehose can optionally transform data with Lambda before delivery, but in this pipeline, the aggregation is done upstream in Kinesis Data Analytics, ensuring that only summarized data lands in S3, reducing storage costs and simplifying downstream ML 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.
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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 → Kinesis Data Analytics → Kinesis Data Firehose → S3 — Option A is correct because it provides a complete pipeline for both real-time aggregation and durable storage. Kinesis Data Streams ingests the streaming clickstream data, Kinesis Data Analytics performs real-time SQL or Apache Flink-based aggregations on the stream, and Kinesis Data Firehose delivers the aggregated results to S3 with optional data transformation and buffering, meeting the requirements for ML training.
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 →
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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.
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