- A
Increase the number of Kinesis Processing Units (KPUs) for the application
More KPUs increase parallelism and throughput.
- B
Increase the number of shards in the Kinesis data stream
Why wrong: More shards increase ingestion capacity, but the analytics app is the bottleneck.
- C
Enable auto-scaling on the Kinesis data stream
Why wrong: Auto-scaling is for the stream, not the analytics app.
- D
Decrease the retention period of the Kinesis data stream
Why wrong: Retention period affects data availability, not processing speed.
Question 476 of 1,786
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 company uses Kinesis Data Analytics for SQL-based real-time analytics on streaming data. They notice that the application is processing data slower than the incoming rate, causing increased latency. Which action is MOST likely to improve the throughput?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 Kinesis Processing Units (KPUs) for the application
Kinesis Data Analytics for SQL applications processes data using Kinesis Processing Units (KPUs), which define the compute and memory resources available. When the incoming data rate exceeds the processing capacity, increasing the number of KPUs directly scales the application's parallelism and throughput, allowing it to keep up with the stream. This is the most direct way to reduce latency caused by insufficient processing power.
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 number of Kinesis Processing Units (KPUs) for the application
Why this is correct
More KPUs increase parallelism and throughput.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of shards in the Kinesis data stream
Why it's wrong here
More shards increase ingestion capacity, but the analytics app is the bottleneck.
- ✗
Enable auto-scaling on the Kinesis data stream
Why it's wrong here
Auto-scaling is for the stream, not the analytics app.
- ✗
Decrease the retention period of the Kinesis data stream
Why it's wrong here
Retention period affects data availability, not processing speed.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse scaling the source stream (shards) with scaling the analytics application (KPUs), assuming that more shards automatically improve processing throughput, when in fact the application's compute resources are the limiting factor.
Detailed technical explanation
How to think about this question
Kinesis Data Analytics allocates KPUs in increments, with each KPU providing 1 GB of memory and corresponding compute. The application's parallelism is tied to the number of KPUs, and the service automatically distributes work across them using the stream's shard count as a parallelism hint. In practice, if the application has fewer KPUs than shards, some shards will be processed sequentially, creating a bottleneck; increasing KPUs allows concurrent processing of more shards, directly improving throughput.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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 Kinesis Processing Units (KPUs) for the application — Kinesis Data Analytics for SQL applications processes data using Kinesis Processing Units (KPUs), which define the compute and memory resources available. When the incoming data rate exceeds the processing capacity, increasing the number of KPUs directly scales the application's parallelism and throughput, allowing it to keep up with the stream. This is the most direct way to reduce latency caused by insufficient processing power.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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