MLS-C01 Data Engineering Practice Question
A company uses Amazon Kinesis Data Analytics for real-time anomaly detection on a stream of IoT sensor data. The application is experiencing high latency. The data volume has doubled. Which action would MOST effectively reduce latency?
⚠ Common exam trap
Candidates often confuse scaling the source stream (shards) with scaling the processing application (parallelism), mistakenly thinking that increasing shards will automatically reduce latency, when in fact the bottleneck is the application's compute capacity, not the stream's ingestion rate.
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 Parallelism setting of the Kinesis Data Analytics application
Increasing the Parallelism setting of the Kinesis Data Analytics application directly allocates more processing resources (e.g., more Kinesis Processing Units or KPUs) to handle the doubled data volume. This allows the application to process records concurrently, reducing the per-record processing time and overall latency. Parallelism is the primary scaling mechanism for Kinesis Data Analytics to match throughput increases.
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 Parallelism setting of the Kinesis Data Analytics application
Why this is correct
More KPUs allow parallel processing of records.
- ✗
Change the record format from JSON to Avro
Why it's wrong here
Switching to Avro reduces payload size and improves serialisation speed, but the latency here stems from doubled data volume overwhelming the application’s processing capacity, not from serialisation overhead. Avro’s schema-based binary format optimises throughput in bandwidth-constrained pipelines, making it tempting when network transfer is the bottleneck. However, in this scenario, scaling the Kinesis Data Analytics stream’s parallelism or increasing the number of in-application processing units directly addresses the volume-driven latency, whereas Avro only trims per-record size without resolving the fundamental throughput ceiling.
- ✗
Decrease the retention period of the source stream
Why it's wrong here
Retention does not affect processing latency.
- ✗
Increase the number of shards in the source Kinesis stream
Why it's wrong here
More shards increase ingestion capacity, but analytics parallelism is separate.
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