- A
Ability to compress data before delivery
Why wrong: Both can compress data before delivery.
- B
Ability to encrypt data at rest
Why wrong: Both services support encryption at rest.
- C
Need for custom data processing using AWS Lambda
Kinesis Data Streams supports custom processing with Lambda, Firehose has limited transformation.
- D
Data retention requirements
Kinesis Data Streams retains data up to 365 days, Firehose does not retain data.
- E
Latency requirements for data delivery to S3
Kinesis Data Firehose delivers data within 60 seconds, while Kinesis Data Streams requires custom consumer.
Question 1,336 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.
Which THREE factors should be considered when choosing between Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose for a real-time data ingestion pipeline? (Choose 3.)
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
✓
Need for custom data processing using AWS Lambda
Option C is correct because Kinesis Data Streams supports custom processing via AWS Lambda consumers (using the Kinesis Client Library or direct integration), enabling real-time transformations, filtering, or enrichment. Kinesis Data Firehose does not natively support custom Lambda processing for transformation; it only allows optional Lambda functions for data format conversion or transformation before delivery, but not for arbitrary real-time processing logic.
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.
- ✗
Ability to compress data before delivery
Why it's wrong here
Both can compress data before delivery.
- ✗
Ability to encrypt data at rest
Why it's wrong here
Both services support encryption at rest.
- ✓
Need for custom data processing using AWS Lambda
Why this is correct
Kinesis Data Streams supports custom processing with Lambda, Firehose has limited transformation.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Data retention requirements
Why this is correct
Kinesis Data Streams retains data up to 365 days, Firehose does not retain data.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Latency requirements for data delivery to S3
Why this is correct
Kinesis Data Firehose delivers data within 60 seconds, while Kinesis Data Streams requires custom consumer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that Kinesis Data Firehose supports custom real-time processing like Streams, but Firehose only allows optional Lambda transformations with limited control and no data replay capability.
Detailed technical explanation
How to think about this question
Kinesis Data Streams uses shards to ingest data with a retention period of 24 hours (default) up to 365 days, allowing replay and custom processing via Lambda, EC2, or KCL applications. Kinesis Data Firehose is a fully managed delivery service with near-real-time latency (typically 60 seconds minimum buffer interval) and does not support data replay or custom processing beyond optional Lambda transformations. The choice often hinges on whether you need sub-second processing and data replay (Streams) or simplified, automated delivery to destinations like S3, Redshift, or Elasticsearch (Firehose).
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
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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: Need for custom data processing using AWS Lambda — Option C is correct because Kinesis Data Streams supports custom processing via AWS Lambda consumers (using the Kinesis Client Library or direct integration), enabling real-time transformations, filtering, or enrichment. Kinesis Data Firehose does not natively support custom Lambda processing for transformation; it only allows optional Lambda functions for data format conversion or transformation before delivery, but not for arbitrary real-time processing logic.
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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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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