- A
Amazon Kinesis Data Analytics to Amazon S3
Why wrong: Kinesis Data Analytics provides real-time SQL analytics but does not directly write to S3; you'd need a destination like Firehose.
- B
AWS Glue ETL job to Amazon S3
Why wrong: AWS Glue is for batch ETL, not real-time streaming ingestion.
- C
Amazon Kinesis Data Firehose to Amazon S3
Firehose directly delivers streaming data to S3 with optional transformations, making it ideal for this use case.
- D
Amazon Kinesis Data Streams to Amazon SageMaker
Why wrong: Kinesis Data Streams is for real-time processing, but SageMaker is not a storage destination; you'd need a consumer like Lambda to write to S3.
Question 456 of 1,000
easyMultiple ChoiceObjective-mapped
Ingest Streaming Data to S3 with Amazon Kinesis Firehose
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 company is setting up a data pipeline to ingest streaming clickstream data from their website for real-time analytics and machine learning. The data must be reliably ingested, transformed, and stored in Amazon S3 for batch processing. Which combination of AWS services should be used?
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 to Amazon S3
Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service designed to reliably ingest streaming data, transform it (e.g., convert to Parquet/ORC, compress, or invoke AWS Lambda for custom transformations), and automatically deliver it to Amazon S3 without requiring custom code or manual scaling. This directly meets the requirement for real-time ingestion, transformation, and storage in S3 for batch processing.
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 Analytics to Amazon S3
Why it's wrong here
Kinesis Data Analytics provides real-time SQL analytics but does not directly write to S3; you'd need a destination like Firehose.
- ✗
AWS Glue ETL job to Amazon S3
Why it's wrong here
AWS Glue is for batch ETL, not real-time streaming ingestion.
- ✓
Amazon Kinesis Data Firehose to Amazon S3
Why this is correct
Firehose directly delivers streaming data to S3 with optional transformations, making it ideal for this use case.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon Kinesis Data Streams to Amazon SageMaker
Why it's wrong here
Kinesis Data Streams is for real-time processing, but SageMaker is not a storage destination; you'd need a consumer like Lambda to write to S3.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Kinesis Data Streams (a raw streaming layer requiring custom consumers) with Kinesis Data Firehose (a managed delivery service to destinations like S3), often picking Data Streams because it is more commonly discussed, but it does not directly write to S3 without additional components.
Detailed technical explanation
How to think about this question
Kinesis Data Firehose uses a buffer interval (default 60 seconds, configurable down to 60 seconds) and buffer size (default 5 MB) to batch records before writing to S3, which can introduce up to 60 seconds of latency but ensures efficient storage. It supports automatic retries for failed S3 writes and can optionally transform data using Lambda functions, making it ideal for clickstream pipelines where near-real-time delivery is acceptable. Under the hood, Firehose uses the Kinesis Streams API for ingestion but abstracts away shard management, scaling automatically based on throughput.
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 Firehose to Amazon S3 — Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service designed to reliably ingest streaming data, transform it (e.g., convert to Parquet/ORC, compress, or invoke AWS Lambda for custom transformations), and automatically deliver it to Amazon S3 without requiring custom code or manual scaling. This directly meets the requirement for real-time ingestion, transformation, and storage in S3 for batch processing.
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 →
Same concept, more angles
2 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist needs to ingest streaming customer clickstream data from a website into an S3 data lake for ML training. The data must be delivered within 1 minute of ingestion, and JSON records must be converted to Parquet. Which AWS service combination should be used?
medium- A.Amazon S3 Transfer Acceleration with direct PUT requests by clients
- B.Amazon Kinesis Data Streams (KDS) with a Lambda consumer that writes JSON to S3
- C.AWS Glue ETL job triggered every minute to pull from Kinesis Data Streams and write Parquet to S3
- ✓ D.Amazon Kinesis Data Firehose with a 60-second buffer and Parquet conversion enabled
Why D: Amazon Kinesis Data Firehose can buffer incoming data and deliver to S3 with a 60-second buffer window, and it supports converting JSON to Parquet. KDS alone does not convert to Parquet. Glue ETL can do the conversion but adds latency. Lambda with S3 trigger is not streaming-oriented.
Variation 2. A data engineer needs to ingest streaming clickstream data from a website into an S3 data lake for ML training. The data arrives continuously and must be written to S3 in near real-time. Which AWS service is best suited for this task?
easy- A.AWS Lambda function writing to S3 on every click event
- B.Amazon Athena queries running on the website's source database
- ✓ C.Amazon Kinesis Data Firehose with S3 as destination
- D.AWS Glue ETL job triggered by a cron job every 5 minutes
Why C: Amazon Kinesis Data Firehose is the most appropriate service for loading streaming data into S3 with minimal effort and near-real-time latency. It can buffer, transform, and compress data before delivery.
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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