DEA-C01 Data Ingestion and Transformation Practice Question
A company is ingesting real-time financial transactions into Amazon Kinesis Data Streams. The data is then consumed by a Kinesis Data Analytics for Apache Flink application that calculates running totals. The application is experiencing high latency and checkpoint failures. Which TWO steps should the engineer take to improve performance and reliability? (Select TWO.)
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.
Options C and D are correct. Increasing the number of shards (C) increases the throughput and parallelism of the stream, reducing latency. Increasing KPUs (D) provides more compute resources for the Flink application, helping to prevent checkpoint failures. Option A (enhanced fan-out) is designed for multiple consumers, not for a single Flink job. Option B (reducing batch size) may not improve overall throughput. Option E (decreasing checkpoint interval) could lead to more frequent checkpointing, increasing overhead and potentially causing more failures.
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 enhanced fan-out for the Flink application.
Why it's wrong here
Enhanced fan-out is beneficial for multiple consumers; with a single consumer, it does not help.
- ✗
Reduce the batch size of records processed per checkpoint.
Why it's wrong here
Smaller batches may increase overhead and not address resource constraints.
- ✓
Increase the number of shards in the Kinesis data stream.
Why this is correct
More shards increase parallelism, reducing latency and improving throughput.
- ✓
Increase the number of KPUs (Kinesis Processing Units) for the Flink application.
Why this is correct
More KPUs provide additional CPU and memory, reducing checkpoint failures and latency.
- ✗
Decrease the checkpoint interval to reduce state size.
Why it's wrong here
Shorter checkpoint intervals increase overhead and may worsen failures.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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