MLS-C01 Data Engineering Practice Question
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?
⚠ Common exam trap
Many 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.
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.
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.
- ✗
Increase the sliding window size to reduce computational frequency.
Why it's wrong here
Larger windows increase latency, not reduce it.
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