- A
Enable DynamoDB auto scaling to handle write spikes.
Why wrong: Auto scaling helps with write capacity but does not address streaming latency.
- B
Decrease the parallelism level in the Kinesis Data Analytics application.
Why wrong: Decreasing parallelism reduces processing capacity.
- C
Increase the number of shards in the Kinesis data stream.
More shards increase parallelism and reduce processing backlog.
- D
Increase the sliding window size to reduce computational frequency.
Why wrong: Larger windows increase latency, not reduce it.
Question 947 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 runs a real-time fraud detection pipeline using Amazon Kinesis Data Analytics. The pipeline reads from a Kinesis data stream, performs sliding window aggregations, and writes results to a DynamoDB table. The application is experiencing high latency during peak hours. Which action would MOST effectively reduce latency?
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 in the Kinesis data stream.
Increasing the number of shards in the Kinesis data stream directly increases the ingestion capacity and parallelism of the stream, allowing the Kinesis Data Analytics application to consume and process records faster. This addresses the root cause of high latency during peak hours by scaling the data source throughput, which is the bottleneck in a streaming pipeline.
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.
- ✗
Enable DynamoDB auto scaling to handle write spikes.
Why it's wrong here
Auto scaling helps with write capacity but does not address streaming latency.
- ✗
Decrease the parallelism level in the Kinesis Data Analytics application.
Why it's wrong here
Decreasing parallelism reduces processing capacity.
- ✓
Increase the number of shards in the Kinesis data stream.
Why this is correct
More shards increase parallelism and reduce processing backlog.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the sliding window size to reduce computational frequency.
Why it's wrong here
Larger windows increase latency, not reduce it.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the symptom (high latency) with a downstream issue (DynamoDB write capacity) or computational efficiency (window size), rather than recognizing that the bottleneck is upstream at the data ingestion layer, which is the most common cause of latency in Kinesis-based streaming pipelines.
Detailed technical explanation
How to think about this question
Kinesis Data Analytics uses the number of shards in the source stream to determine the application's parallelism (KPU count), where each shard can be processed by one or more KPUs. Under the hood, the application's throughput is bounded by the stream's shard capacity (1 MB/s or 1,000 records/s per shard for ingestion), so increasing shards scales both ingestion and processing parallelism, reducing backpressure and latency. In real-world scenarios, peak-hour spikes often saturate shard limits, causing records to be throttled or delayed, which is resolved by resharding.
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 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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — 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. — Increasing the number of shards in the Kinesis data stream directly increases the ingestion capacity and parallelism of the stream, allowing the Kinesis Data Analytics application to consume and process records faster. This addresses the root cause of high latency during peak hours by scaling the data source throughput, which is the bottleneck in a streaming pipeline.
What should I do if I get this MLS-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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