- A
The need for built-in data transformation and analytics.
Why wrong: Incorrect: Neither has built-in analytics; Firehose can invoke Lambda for transformation.
- B
The need for custom real-time processing logic using consumer applications.
Correct: Data Streams supports custom consumers; Firehose does not.
- C
The required end-to-end latency (seconds vs. minutes).
Correct: Data Streams has sub-second latency; Firehose buffers data, introducing minutes of delay.
- D
The need to manually manage shard capacity and scaling.
Correct: Data Streams requires manual shard management; Firehose auto-scales.
- E
The requirement for exactly-once delivery semantics.
Why wrong: Incorrect: Neither service guarantees exactly-once delivery.
Question 1,055 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 three.)
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
✓
The need for custom real-time processing logic using consumer applications.
Option B is correct because Kinesis Data Streams supports custom real-time processing via consumer applications using the Kinesis Client Library (KCL) or AWS Lambda, enabling fine-grained control over record processing, checkpointing, and custom logic. This is a key differentiator from Kinesis Data Firehose, which only supports built-in transformations via Lambda and does not allow direct consumer access to the stream.
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.
- ✗
The need for built-in data transformation and analytics.
Why it's wrong here
Incorrect: Neither has built-in analytics; Firehose can invoke Lambda for transformation.
- ✓
The need for custom real-time processing logic using consumer applications.
Why this is correct
Correct: Data Streams supports custom consumers; Firehose does not.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
The required end-to-end latency (seconds vs. minutes).
Why this is correct
Correct: Data Streams has sub-second latency; Firehose buffers data, introducing minutes of delay.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
The need to manually manage shard capacity and scaling.
Why this is correct
Correct: Data Streams requires manual shard management; Firehose auto-scales.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The requirement for exactly-once delivery semantics.
Why it's wrong here
Incorrect: Neither service guarantees exactly-once delivery.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Kinesis Data Firehose's built-in Lambda transformations with the custom real-time processing capabilities of Kinesis Data Streams, overlooking that Firehose does not allow direct consumer applications or sub-second latency.
Detailed technical explanation
How to think about this question
Kinesis Data Streams uses shards as the base throughput unit, each supporting 1 MB/s write and 2 MB/s read, requiring manual scaling or auto-scaling via the UpdateShardCount API. In contrast, Kinesis Data Firehose automatically scales and buffers data (default 60 seconds or 5 MB) before delivering to destinations like S3, Redshift, or Elasticsearch, making it suitable for near-real-time (minutes) rather than sub-second latency. The choice hinges on whether you need sub-second processing with custom consumers (Streams) or simplified, lower-latency-tolerant ingestion with automatic scaling (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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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: The need for custom real-time processing logic using consumer applications. — Option B is correct because Kinesis Data Streams supports custom real-time processing via consumer applications using the Kinesis Client Library (KCL) or AWS Lambda, enabling fine-grained control over record processing, checkpointing, and custom logic. This is a key differentiator from Kinesis Data Firehose, which only supports built-in transformations via Lambda and does not allow direct consumer access to the stream.
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
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