- A
Increase the Lambda function timeout.
Why wrong: Timeout does not affect record size; errors are due to payload size, not processing time.
- B
Enable compression on the producer side before sending records to Kinesis.
Compression reduces record size below the 256 KB limit.
- C
Use the Kinesis Producer Library (KPL) to aggregate multiple small records into a single larger record.
KPL aggregation packs small records into a single Kinesis record, reducing overhead.
- D
Switch from Kinesis Data Streams to Kinesis Data Firehose.
Why wrong: Firehose has a 1 MB limit per record; still may not fit if records are large.
- E
Increase the number of shards in the Kinesis stream.
Why wrong: More shards increase throughput but do not reduce record size.
Question 423 of 1,786
How to Handle the 256 KB Payload Limit in Kinesis Data Streams
This DEA-C01 practice question tests your understanding of data ingestion and transformation. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 using Amazon Kinesis Data Streams to ingest clickstream data from a website. The data is consumed by an AWS Lambda function that enriches records and writes to Amazon S3. The Lambda function is experiencing high error rates due to records exceeding the 256 KB payload limit. Which TWO actions should the team take to resolve this issue?
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
✓
Enable compression on the producer side before sending records to Kinesis.
Option B is correct because enabling compression on the producer side reduces the size of each record before it is sent to Kinesis Data Streams, directly addressing the 256 KB payload limit. Option C is correct because the Kinesis Producer Library (KPL) aggregates multiple small records into a single larger record, which is then stored as one Kinesis record, reducing the number of records that exceed the limit and improving throughput.
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.
- ✗
Increase the Lambda function timeout.
Why it's wrong here
Timeout does not affect record size; errors are due to payload size, not processing time.
- ✓
Enable compression on the producer side before sending records to Kinesis.
Why this is correct
Compression reduces record size below the 256 KB limit.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use the Kinesis Producer Library (KPL) to aggregate multiple small records into a single larger record.
Why this is correct
KPL aggregation packs small records into a single Kinesis record, reducing overhead.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch from Kinesis Data Streams to Kinesis Data Firehose.
Why it's wrong here
Firehose has a 1 MB limit per record; still may not fit if records are large.
- ✗
Increase the number of shards in the Kinesis stream.
Why it's wrong here
More shards increase throughput but do not reduce record size.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse increasing shards (which increases throughput) with reducing record size, or think that switching to Firehose bypasses the 256 KB limit, when in fact Firehose also has a per-record size limit and does not address the root cause.
Detailed technical explanation
How to think about this question
Kinesis Data Streams enforces a maximum record size of 256 KB for the data blob plus partition key. The Kinesis Producer Library (KPL) uses batching and aggregation to combine multiple user records into a single Kinesis record, which is then de-aggregated on the consumer side using the Kinesis Client Library (KCL) or a Lambda consumer with the KPL de-aggregation library. Compression (e.g., gzip) reduces the payload size before sending, but the Lambda consumer must decompress the data after reading from the stream.
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.
Visual reference
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: Enable compression on the producer side before sending records to Kinesis. — Option B is correct because enabling compression on the producer side reduces the size of each record before it is sent to Kinesis Data Streams, directly addressing the 256 KB payload limit. Option C is correct because the Kinesis Producer Library (KPL) aggregates multiple small records into a single larger record, which is then stored as one Kinesis record, reducing the number of records that exceed the limit and improving throughput.
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
This DEA-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 DEA-C01 exam.
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