MLS-C01 Data Engineering Practice Question
A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Kinesis Data Analytics application that runs SQL queries. The application has been failing intermittently with 'ProvisionedThroughputExceededException' errors. Which action should be taken to resolve this issue?
⚠ Common exam trap
A common mix-up: candidates confuse 'ProvisionedThroughputExceededException' with a data format or service selection issue, rather than recognizing it as a direct capacity scaling problem that requires increasing shard count.
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 Kinesis data stream.
The 'ProvisionedThroughputExceededException' error indicates that the Kinesis Data Stream's read or write throughput limits have been exceeded. Increasing the number of shards in the stream directly increases the total provisioned throughput, allowing the Kinesis Data Analytics application to consume data without 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.
- ✗
Disable error logging in the Kinesis Data Analytics application.
Why it's wrong here
Incorrect: This does not resolve the throughput issue.
- ✗
Increase the record size in the Kinesis data stream.
Why it's wrong here
Incorrect: Larger records can increase the likelihood of exceeding throughput limits.
- ✗
Switch from Kinesis Data Analytics to Kinesis Data Firehose.
Why it's wrong here
Incorrect: Firehose is for delivering streams, not for running SQL analytics.
- ✓
Increase the number of shards in the Kinesis data stream.
Why this is correct
Correct: More shards increase read throughput capacity.
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