DEA-C01 Data Operations and Support Practice Question
A data engineer is using Amazon Kinesis Data Streams to ingest clickstream data. The stream has 10 shards and each record is 50 KB. The engineer notices that the PutRecords API is frequently returning ProvisionedThroughputExceededException errors, even though the total incoming data rate is below the stream's overall capacity. What is the MOST likely cause?
⚠ Common exam trap
The trap here is assuming that overall stream capacity is the only factor, while ignoring per-shard limits and partition key distribution.
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
✓
The partition key is not uniformly distributed across shards, causing hot shards.
ProvisionedThroughputExceededException occurs when a specific shard exceeds its 1 MB/s or 1000 records/s limit. If total incoming rate is below stream capacity but partition keys are not uniformly distributed, some shards become hot and throttle. Using a well-distributed partition key or resharding can mitigate this.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The partition key is not uniformly distributed across shards, causing hot shards.
Why this is correct
Kinesis Data Streams distributes records to shards based on the partition key hash. If the partition key is skewed, some shards receive more traffic than others, exceeding their individual limits (1 MB/s or 1000 records/s per shard) even if total stream capacity is sufficient. This leads to ProvisionedThroughputExceededException on the hot shards. Ensuring a uniform partition key distribution resolves the issue.
- ✗
The stream's retention period is set too low, causing data loss.
Why it's wrong here
Retention period controls how long data is stored, not the write throughput. A low retention period does not cause ProvisionedThroughputExceededException; it would only affect read operations after data expires. The error is specifically about exceeding write capacity, so retention is irrelevant here.
- ✗
The PutRecords API call is using too many records per request, exceeding the 500-record limit.
Why it's wrong here
PutRecords supports up to 500 records per request, and exceeding that would return a different error (e.g., InvalidArgumentException). The ProvisionedThroughputExceededException indicates throughput limits, not request size limits. While batching is important, the error points to per-shard capacity, not the number of records per call.
- ✗
The stream's shards are not encrypted, causing throttling.
Why it's wrong here
Encryption (server-side or client-side) does not affect throughput limits. Lack of encryption may be a security concern but does not cause ProvisionedThroughputExceededException. Throttling is based on data volume and request rate per shard, independent of encryption status.
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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
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