DEA-C01 Data Ingestion and Transformation Practice Question
Network Topology
A data engineer is troubleshooting a Kinesis Data Streams application that is experiencing high latency. The stream has 2 shards. The application is using a single Kinesis Client Library (KCL) worker to process all shards. Which change will MOST likely 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
✓
Deploy multiple KCL workers to process shards in parallel.
The application uses a single KCL worker to process all 2 shards, which processes records sequentially and causes high latency. Deploying multiple KCL workers (ideally one per shard) enables parallel processing of shards, significantly reducing latency. Option A is incorrect because increasing shard count to 4 adds more capacity but does not address the bottleneck of a single worker; the same worker would process all 4 shards sequentially, potentially worsening latency. Option C is incorrect because Kinesis Data Streams is a managed service; there is no instance type to change for the stream itself. The KCL worker runs on your compute resources, not on the stream. Option D is incorrect because decreasing shards to 1 reduces the level of parallelism, increasing the workload per shard and likely increasing latency further.
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 shards to 4.
Why it's wrong here
Adding shards increases stream capacity, but a single KCL worker leases at most one shard per worker thread, so the extra shards sit idle and latency is unchanged. The option is tempting because shard count governs throughput, yet the bottleneck here is worker-side parallelism, which needs more workers or threads.
- ✓
Deploy multiple KCL workers to process shards in parallel.
Why this is correct
Deploying multiple KCL workers lets each worker lease a distinct shard, so the two shards are processed concurrently rather than sequentially by one worker. This directly addresses the stem's constraint: a single worker cannot parallelise across shards, so adding workers raises aggregate throughput and reduces processing latency.
- ✗
Use a larger instance type for the Kinesis stream.
Why it's wrong here
Kinesis Data Streams is a fully managed service; shard capacity is set by shard count, not by instance size, and no instance type is selectable for the stream itself. The option is tempting because scaling compute often resolves latency, but here the fix lies in KCL worker or thread count.
- ✗
Decrease the number of shards to 1.
Why it's wrong here
Reducing to one shard halves the parallelism available to the KCL worker, so the single worker processes fewer records concurrently and latency worsens. The option is tempting because fewer shards can seem to mean less coordination overhead, but shards are the unit of parallel consumption, not a source of contention.
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