How to Fix Kinesis Throughput Errors by Increasing Shards
A company uses AWS Kinesis Data Streams to ingest real-time data. The data engineer notices that the stream's 'WriteProvisionedThroughputExceeded' error occurs frequently during peaks. Which action should be taken to resolve this issue?
⚠ Common exam trap
DEA-C01 often tests the misconception that changing the partition key or compressing data can resolve throughput errors, but the fundamental solution is to increase shard capacity.
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 stream.
The 'WriteProvisionedThroughputExceeded' error occurs when the stream's write capacity is exceeded. Increasing the number of shards (A) increases the stream's write capacity, as each shard provides a fixed write throughput (1 MB/s or 1000 records/s). This directly resolves the issue by adding more capacity to handle peak loads.
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 stream.
Why this is correct
Increasing shard count raises the stream's total write capacity, since each shard provides a fixed 1 MB/s and 1,000 records/s ingest limit. The WriteProvisionedThroughputExceeded error signals that producers are exceeding the aggregate provisioned throughput, so adding shards directly satisfies the peak-demand constraint described in the stem.
- ✗
Modify the producer to use a different partition key.
Why it's wrong here
Changing the partition key only redistributes load across existing shards; if total write throughput exceeds the stream's provisioned capacity, hot-shard errors persist. It is tempting because a single hot partition key causes throttling, and rekeying fixes that specific skew — but the stem describes peak-wide throughput exhaustion, not one overloaded shard.
- ✗
Compress the data before sending to the stream.
Why it's wrong here
Compression reduces payload size per record but Kinesis throttles on records per second and bytes per second against provisioned shard limits; peak write volume still exceeds capacity. It is tempting because compression lowers bandwidth and storage costs, which helps when payload size drives the limit, but the stem's peak-driven WriteProvisionedThroughputExceeded needs increased shard capacity.
- ✗
Enable enhanced fan-out for consumers.
Why it's wrong here
Enhanced fan-out increases read throughput to consumers via dedicated 2 MB/s pipes; it does not raise the stream's write capacity, so producer throttling continues. It is tempting because it resolves consumer-side ReadProvisionedThroughputExceeded when multiple applications read the same stream, but the stem's error is on writes, requiring more shards or on-demand mode.
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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.