The correct answer is to increase the number of shards in the Kinesis data stream to handle peak throughput and enable auto-scaling. This works because Kinesis shard scaling directly determines the stream’s ingestion capacity—each shard supports up to 1 MB/s or 1,000 records per second for writes, so the current 10 shards can only handle 10,000 records per second, which is insufficient during spikes to 50,000 records per second. By scaling shards to cover the peak and enabling auto-scaling, you ensure the stream can absorb bursts without throttling, allowing Kinesis Data Firehose to read data fast enough to meet the 1-minute SLA. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of Kinesis throughput fundamentals and the common trap of over-provisioning versus dynamic scaling. A key memory tip: think of shards as lanes on a highway—more lanes (shards) handle more traffic (throughput), and auto-scaling adds lanes only when a traffic jam (spike) occurs.
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.
A financial company needs to ingest real-time stock trade data from multiple sources and store it in Amazon S3 for compliance. The data must be delivered within 1 minute of the trade occurring. The data volume is approximately 10,000 records per second, with occasional spikes to 50,000 records per second. The engineer has set up Amazon Kinesis Data Streams with 10 shards and a Kinesis Data Firehose delivery stream that reads from the Kinesis stream and writes to S3. However, during spikes, the Firehose delivery stream falls behind, causing data to be delayed beyond the 1-minute SLA. What should the engineer do to meet the SLA without over-provisioning?
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 in the Kinesis data stream to handle peak throughput and enable auto-scaling.
Option A is correct: Increase shard count to handle peak throughput, and enable automatic scaling for Kinesis Data Streams. Option B (increase buffer size) would increase latency. Option C (use Lambda) may not handle the throughput. Option D (use SQS) would not reduce latency.
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 buffer size in Kinesis Data Firehose from 1 MB to 5 MB to batch more data per delivery.
Why it's wrong here
Larger buffers increase latency, not reduce it.
✗
Use Amazon SQS as a buffer between Kinesis and Firehose to absorb spikes.
Why it's wrong here
Adding SQS introduces additional latency and complexity.
✗
Replace Firehose with an AWS Lambda function that writes directly to S3 for lower latency.
Why it's wrong here
Lambda may throttle during spikes and cannot handle the throughput without careful configuration.
✓
Increase the number of shards in the Kinesis data stream to handle peak throughput and enable auto-scaling.
Why this is correct
More shards increase read capacity; auto-scaling adjusts during spikes.
Related concept
Read the scenario before looking for a memorised answer.
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 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.
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.
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 in the Kinesis data stream to handle peak throughput and enable auto-scaling. — Option A is correct: Increase shard count to handle peak throughput, and enable automatic scaling for Kinesis Data Streams. Option B (increase buffer size) would increase latency. Option C (use Lambda) may not handle the throughput. Option D (use SQS) would not reduce latency.
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 →
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 financial services company ingests real-time stock trade data using Amazon Kinesis Data Streams with 10 shards. Each shard receives about 500 records per second, each record approximately 1 KB. The data is consumed by a single AWS Lambda function that transforms the data and writes to Amazon S3. The Lambda function is configured with 1024 MB memory and a timeout of 5 minutes. The company notices that the Lambda function is frequently throttled, and data ingestion lags behind. The Lambda function's CloudWatch metrics show that the iterator age is increasing, and the function's concurrency is maxed out at 1000. The data engineer needs to resolve the throttling issue without changing the Lambda function code. What should the data engineer do?
hard
✓ A.Increase the number of shards in the Kinesis data stream to increase parallelism.
B.Reduce the Lambda function memory to 512 MB to increase concurrency limit.
C.Decrease the batch size to 10 records to reduce processing time per invocation.
D.Increase the Lambda function memory to 2048 MB to improve processing speed.
Why A: Option B is correct. Increasing the number of shards increases the number of Lambda consumers, allowing more parallel processing and reducing the iterator age. Option A is wrong because increasing Lambda memory may not improve throughput if the function is CPU-bound; also, the issue is concurrency, not memory. Option C is wrong because decreasing batch size may increase overhead; the function is already maxing concurrency. Option D is wrong because reducing memory would likely make the function slower.
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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.
Question Discussion
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