- A
The need for a fully managed delivery destination
Firehose can directly deliver to S3, Redshift, etc., while Streams requires a consumer.
- B
Whether the application requires custom data processing logic
Streams allow custom consumers; Firehose uses built-in transformations.
- C
The ability to compress data before storage
Why wrong: Both support compression before delivery to destinations.
- D
The latency requirements for data delivery
Streams provide sub-second latency; Firehose has buffer delays.
- E
The maximum throughput supported per shard
Why wrong: Both can scale by adding shards or increasing buffer size.
Question 1,327 of 1,786
Kinesis Data Streams vs Data Firehose: Which Real-Time Ingestion Service?
This DEA-C01 practice question tests your understanding of data ingestion and transformation. 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.
Which THREE factors should be considered when choosing between Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose for real-time data ingestion? (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 a fully managed delivery destination
Amazon Kinesis Data Firehose is a fully managed service that automatically delivers streaming data to destinations like Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk, making it ideal when you need a fully managed delivery destination without managing the ingestion pipeline. In contrast, Amazon Kinesis Data Streams requires you to build and manage consumers to process and deliver data, so if you need a fully managed destination, Firehose is the correct choice.
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 a fully managed delivery destination
Why this is correct
Firehose can directly deliver to S3, Redshift, etc., while Streams requires a consumer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Whether the application requires custom data processing logic
Why this is correct
Streams allow custom consumers; Firehose uses built-in transformations.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The ability to compress data before storage
Why it's wrong here
Both support compression before delivery to destinations.
- ✓
The latency requirements for data delivery
Why this is correct
Streams provide sub-second latency; Firehose has buffer delays.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The maximum throughput supported per shard
Why it's wrong here
Both can scale by adding shards or increasing buffer size.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume compression or throughput limits are unique to one service, but both services support compression and throughput is a scaling detail of Streams, not a direct comparison factor for choosing between the two.
Detailed technical explanation
How to think about this question
Kinesis Data Streams uses shards as the base throughput unit, each providing 1 MB/s input and 2 MB/s output, and you must manually scale by splitting or merging shards. Kinesis Data Firehose, on the other hand, automatically scales to handle throughput up to 5,000 transactions/second or 5,000 records/second per delivery stream, and it can buffer data for up to 15 minutes or 128 MB before delivery, which directly impacts latency. In a real-world scenario, if you need sub-second processing and custom transformations (e.g., using AWS Lambda), Kinesis Data Streams is required; if you can tolerate a few seconds to minutes of latency and want automatic delivery to a destination, Firehose is the better fit.
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.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The need for a fully managed delivery destination — Amazon Kinesis Data Firehose is a fully managed service that automatically delivers streaming data to destinations like Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk, making it ideal when you need a fully managed delivery destination without managing the ingestion pipeline. In contrast, Amazon Kinesis Data Streams requires you to build and manage consumers to process and deliver data, so if you need a fully managed destination, Firehose is the correct choice.
What should I do if I get this DEA-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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