DEA-C01 Data Operations and Support Practice Question
A data engineer is monitoring an Amazon Kinesis Data Stream used to ingest clickstream data. The engineer notices that the stream's 'WriteProvisionedThroughputExceeded' metric is frequently above zero. Which TWO actions could help mitigate this issue? (Choose TWO.)
⚠ Common exam trap
DEA-C01 often tests the misconception that read-side features like enhanced fan-out or retention settings can fix producer-side throttling — candidates must separate write capacity (shards, partition key distribution) from read capacity (fan-out, consumers).
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.
Option A is correct because WriteProvisionedThroughputExceeded indicates that producers are exceeding the stream's ingest capacity, and each shard supports a fixed write throughput of 1 MB/s or 1,000 records/s, so adding shards (resizing/shard splitting) increases total write capacity. Option D is correct because a hot shard caused by an uneven partition key (for example, a constant or low-cardinality key) can throttle writes even when aggregate capacity is sufficient; adding a random prefix to the partition key spreads records across more shards, balancing the load. Option B is incorrect because the retention period only controls how long data is stored and has no effect on write throughput capacity. Option C is incorrect because decreasing shards reduces total write capacity and would worsen throttling. Option E is incorrect because enhanced fan-out increases read throughput for consumers (dedicated 2 MB/s per consumer per shard) and does not address write-side throttling.
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
Adding shards raises the stream's total ingest capacity, since each shard provides a fixed write throughput ceiling (1 MB/s, 1,000 records/s). The stem's persistent WriteProvisionedThroughputExceeded metric indicates producers are exceeding provisioned capacity, so scaling shard count directly relieves that constraint.
- ✗
Reduce the data retention period to free up capacity.
Why it's wrong here
Retention governs how long records remain accessible, not how many bytes per second producers may write, so shortening it leaves the write exception unchanged. It is tempting because retention is a capacity-related setting, but it controls storage duration; write throughput is governed by shard count.
- ✗
Decrease the number of shards to reduce overhead.
Why it's wrong here
Fewer shards reduce total ingest capacity, worsening the throughput exception rather than relieving it. It is tempting because shard count is the stream's scaling lever, but the correct remedy increases shards or uses on-demand mode; decreasing them suits cost reduction on under-utilised streams.
- ✓
Implement a random prefix for the partition key to distribute data evenly.
Why this is correct
Randomising the partition key prefix spreads records across all shards rather than concentrating them on one, directly relieving the hot-shard skew that drives WriteProvisionedThroughputExceeded. This satisfies the stem's constraint of uneven distribution, since Kinesis throttles per shard, not per stream.
- ✗
Enable enhanced fan-out on the stream.
Why it's wrong here
Enhanced fan-out increases read throughput per consumer; it does not raise the write capacity that the WriteProvisionedThroughputExceeded metric measures. It is tempting because fan-out addresses throughput limits, but those are consumer-side reads, whereas this error is producer-side, fixed by adding shards.
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Same concept, more angles
2 more ways this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data engineer is monitoring an Amazon Kinesis Data Stream and notices that the 'WriteProvisionedThroughputExceeded' metric is frequently elevated. The stream has 5 shards and is used by multiple producers. What is the BEST action to resolve this issue?
medium- A.Increase the consumer's processing speed to reduce lag.
- ✓ B.Increase the number of shards in the Kinesis data stream.
- C.Reduce the data retention period of the stream.
- D.Implement exponential backoff and retries in the producer applications.
Why B: WriteProvisionedThroughputExceeded indicates that the write rate exceeds the shards' capacity. Increasing the number of shards increases the total write capacity. Option A is incorrect because increasing the consumer's processing speed does not affect write throttling; it addresses read-side lag. Option C is incorrect because reducing the retention period does not affect write throughput. Option D is incorrect because implementing exponential backoff and retries in the producer applications helps with transient failures but does not resolve the root cause of insufficient capacity.
Variation 2. A data engineer is monitoring an Amazon Kinesis Data Stream with a shard count of 10. The stream receives 5 MB/s of write traffic and 10 MB/s of read traffic. The engineer notices that writes are throttled with ProvisionedThroughputExceededException errors. Which action should the engineer take to resolve the throttling?
easy- A.Increase the shard count to 20.
- B.Decrease the shard count to 5.
- C.Enable enhanced fan-out on the stream.
- ✓ D.Configure auto-scaling on the stream.
Why D: ProvisionedThroughputExceededException occurs when a shard's write throughput exceeds 1 MB/s, often due to hot shards from uneven partition key distribution. While increasing shard count (Option A) can help spread the load, it does not automatically fix the root cause if partition keys remain skewed. The best action is to configure auto-scaling (Option D), which in Amazon Kinesis Data Streams can be achieved by switching to on-demand mode. On-demand mode automatically scales capacity based on traffic patterns, eliminating throttling without manual intervention. Option B decreases write capacity, worsening the issue. Option C only improves read throughput, not write.
JA
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.