DEA-C01 Data Ingestion and Transformation Practice Question
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?
⚠ Common exam trap
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.
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.
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
Kinesis Data Analytics parallelism scales with KPU count; each KPU supplies a fixed slice of CPU and memory, so raising KPUs lifts the application's processing ceiling above the incoming stream rate, directly addressing the throughput shortfall causing latency.
- ✗
Increase the number of shards in the Kinesis data stream
Why it's wrong here
Adding shards scales the stream's ingest capacity, not the Kinesis Data Analytics application's SQL processing, so the operator bottleneck persists. It is tempting because shard count governs stream throughput, and it would be correct if consumers were throttled by per-shard read limits rather than by the application itself.
- ✗
Enable auto-scaling on the Kinesis data stream
Why it's wrong here
Auto-scaling adjusts shard count to match stream ingest volume; it cannot raise the Kinesis Data Analytics application's SQL processing capacity, which is the actual constraint. It is tempting because auto-scaling manages stream capacity dynamically, and it would be correct when incoming traffic varies and shard provisioning, not application compute, limits throughput.
- ✗
Decrease the retention period of the Kinesis data stream
Why it's wrong here
Retention governs how long records remain readable in the stream, not how quickly the Kinesis Data Analytics application executes SQL over them, so latency is unchanged. It is tempting as a cost or replay-window control, and it would be correct when storage duration or reprocessing windows, rather than processing throughput, is the requirement.
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