- A
Switch to Kinesis Data Firehose
Why wrong: Firehose would add buffering latency, not improve consumer throughput.
- B
Increase the number of shards
More shards increase the read capacity for consumers.
- C
Increase the retention period
Why wrong: Retention period does not affect ingestion or consumption throughput.
- D
Change the partition key to a constant value
Why wrong: A constant partition key would send all data to one shard, reducing throughput.
Quick Answer
The answer is to increase the number of shards, as this directly boosts the stream’s ingestion capacity and resolves throttling. Each shard provides a fixed 1 MB/s write and 2 MB/s read throughput, so adding shards scales the total available throughput linearly, allowing the consumer to catch up without being bottlenecked. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of Kinesis shard-level limits and the distinction between scaling throughput versus adjusting data retention or delivery methods. A common trap is confusing Firehose as a throughput fix—it only adds latency—or thinking a poor partition key is the issue when a UUID already ensures even distribution. Remember the memory tip: “More shards, more cards” to play with throughput, meaning shards are the only lever for scaling Kinesis performance.
DEA-C01 Data Ingestion and Transformation Practice Question
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.
Refer to the exhibit. A data engineer is using a Kinesis Data Stream with 2 shards. The producer uses a partition key that is the user ID (a UUID). The consumer is falling behind. Which change would improve throughput?
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
Increase the number of shards
Option B is correct because increasing the number of shards increases the ingestion capacity. Option A is wrong because Kinesis Data Firehose would add latency. Option C is wrong because the partition key is already random (UUID), which distributes data well. Option D is wrong because increasing the retention period does not affect 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.
- ✗
Switch to Kinesis Data Firehose
Why it's wrong here
Firehose would add buffering latency, not improve consumer throughput.
- ✓
Increase the number of shards
Why this is correct
More shards increase the read capacity for consumers.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the retention period
Why it's wrong here
Retention period does not affect ingestion or consumption throughput.
- ✗
Change the partition key to a constant value
Why it's wrong here
A constant partition key would send all data to one shard, reducing throughput.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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: Increase the number of shards — Option B is correct because increasing the number of shards increases the ingestion capacity. Option A is wrong because Kinesis Data Firehose would add latency. Option C is wrong because the partition key is already random (UUID), which distributes data well. Option D is wrong because increasing the retention period does not affect throughput.
What should I do if I get this DEA-C01 question wrong?
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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
3 more ways this is tested on DEA-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 engineering team is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data is then consumed by an AWS Lambda function that transforms and loads it into Amazon S3. Recently, the team noticed that the Lambda function is failing with throttling errors (HTTP 429) from the Kinesis API. Which configuration change should the team make to resolve this issue?
medium- A.Disable retries on the Lambda function and configure a dead-letter queue for failed records.
- B.Replace Kinesis Data Streams with Amazon DynamoDB Streams for ingestion.
- ✓ C.Reduce the batch size and increase the number of shards in the Kinesis stream to increase parallelism.
- D.Increase the batch size in the Lambda event source mapping to reduce the number of invocations.
Why C: The correct answer is to reduce the batch size and increase the number of shards. The Lambda function is experiencing throttling because it is trying to process too many records per invocation. Reducing the batch size lowers the number of records per invocation, and increasing shards increases parallelism. Option A is incorrect because increasing the batch size would worsen throttling. Option B is incorrect because using a DynamoDB stream is a different ingestion mechanism and doesn't address the Kinesis throttling. Option D is incorrect because disabling retries would cause data loss. Option C directly addresses the throttling by reducing load per invocation and increasing parallelism.
Variation 2. Refer to the exhibit. A data engineer is using a Kinesis Data Stream with one shard. The application writes 2000 records per second, each 1 KB. The put record calls are frequently throttled. What is the most likely cause?
medium- ✓ A.The stream has only one shard, which limits writes to 1000 records per second
- B.The retention period of 24 hours is too short
- C.The stream uses KMS encryption, causing additional latency
- D.Enhanced monitoring is not enabled, causing performance issues
Why A: Option B is correct. A single shard supports 1000 records/sec write. The application exceeds this. Option A is wrong because retention does not affect throttling. Option C is wrong because encryption does not affect throttling. Option D is wrong because enhanced monitoring is not related.
Variation 3. Refer to the exhibit. A data engineer is troubleshooting a Kinesis Data Streams consumer that is falling behind. The stream has 2 shards and is receiving data at a rate of 2 MB/s. The consumer is an AWS Lambda function with a batch size of 100 records. What should the engineer do to improve consumer throughput?
medium- A.Decrease the Lambda batch size to 10 records
- B.Increase the retention period of the stream to 168 hours
- ✓ C.Increase the number of shards in the stream to 4
- D.Increase the memory allocation of the Lambda function
Why C: Option A is correct because the stream is at its write limit (2 MB/s, but each shard supports 1 MB/s input, so 2 shards = 2 MB/s, but the consumer may need more read capacity. Increasing the number of shards increases both write and read capacity. Option B (Lambda memory) may help but not as effective. Option C (increasing retention) does not help throughput. Option D (decreasing batch size) reduces throughput.
Last reviewed: Jun 20, 2026
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