DEA-C01 Data Ingestion and Transformation Practice Question
A company is ingesting streaming data into Kinesis Data Streams. The consumer application experiences high latency due to a single shard bottleneck. What is the most effective way to reduce latency?
⚠ Common exam trap
The trap is confusing KCL (a consumer-side library for coordination) with a throughput solution — KCL does not add capacity, only shard count does.
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 data stream.
Increasing the number of shards in the Kinesis data stream distributes the incoming records across more shards, allowing the consumer application to process them in parallel and reducing the per-shard bottleneck that causes high latency. Each shard supports up to 1 MB/s or 1,000 records/s ingress, so adding shards directly increases throughput capacity. This is the most direct and effective way to address a single-shard bottleneck.
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 in the data stream.
Why this is correct
A single shard caps throughput and processing parallelism, creating the bottleneck. Adding shards spreads records across more consumers, raising aggregate throughput and reducing per-record latency, directly addressing the stem's single-shard constraint. Resharding via UpdateShardCount is the standard remedy.
- ✗
Wait for automatic scaling to add shards.
Why it's wrong here
Kinesis Data Streams does not automatically scale shard count; scaling is a manual operation via UpdateShardCount or the console. Waiting therefore changes nothing, leaving the single shard saturated. Automatic scaling applies to other services, so this option would suit a scenario where the platform genuinely provisions capacity on demand.
- ✗
Use the Kinesis Client Library (KCL) to process records.
Why it's wrong here
KCL handles record processing, checkpointing and resharding coordination, but it does not redistribute load across shards. The bottleneck is a single shard's throughput limit, so latency persists. KCL would be right when building a consumer that must track progress across resharded streams, not for relieving a hot shard.
- ✗
Switch to Amazon Kinesis Data Firehose.
Why it's wrong here
Firehose is a managed delivery service to destinations such as S3, Redshift or OpenSearch; it does not consume from a Data Streams shard to relieve a hot shard's throughput ceiling. It would be correct when the requirement is batched loading into a data store rather than low-latency custom consumption.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.