DEA-C01 Data Operations and Support Practice Question
A data pipeline using AWS Glue ETL jobs is failing intermittently with the error 'Rate exceeded' when writing to an Amazon Redshift cluster. Which action is MOST effective to resolve this issue?
⚠ Common exam trap
A common mix-up: candidates confuse 'Rate exceeded' with a timeout issue and choose to increase the job timeout (Option A), failing to recognize that the error is a capacity constraint on the Redshift side, not a duration issue.
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
✓
Enable auto-tuning on the Redshift cluster and use concurrency scaling.
Enabling auto-tuning on the Redshift cluster and using concurrency scaling dynamically adds cluster capacity to absorb spikes in write requests, directly addressing the 'Rate exceeded' error. This error typically occurs when the Glue ETL job's write throughput exceeds the cluster's current capacity, and concurrency scaling provides additional query queues to handle the load without manual intervention.
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 timeout of the Glue ETL job to allow more time for retries.
Why it's wrong here
Increasing timeout does not resolve the rate limiting error.
- ✗
Disable workload management (WLM) concurrency scaling in Redshift.
Why it's wrong here
Disabling concurrency scaling would reduce capacity, worsening the issue.
- ✓
Enable auto-tuning on the Redshift cluster and use concurrency scaling.
Why this is correct
Auto-tuning with concurrency scaling dynamically adds capacity to handle increased write requests.
- ✗
Change the output file format from Parquet to CSV to reduce write size.
Why it's wrong here
Data format has no impact on write rate limits.
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Same concept, more angles
1 more way 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 engineering team notices that an AWS Glue ETL job fails intermittently with a 'ThrottlingException' error. The job reads from an Amazon S3 bucket and writes to an Amazon Redshift table. What is the MOST likely cause of this error?
medium- A.The S3 bucket's request rate is exceeding the bucket's performance limits.
- ✓ B.The Redshift cluster's write throughput is exceeding its provisioned capacity.
- C.The Glue job is exceeding the maximum number of concurrent runs allowed.
- D.The Glue job's allocated memory is insufficient for the data volume.
Why B: The 'ThrottlingException' error occurs when the rate of API requests exceeds the allowed limit. In this scenario, the Glue job writes to Amazon Redshift. Redshift has a provisioned write throughput capacity; if the Glue job attempts to write data faster than Redshift can handle, Redshift throttles the requests, resulting in a ThrottlingException. This is the most likely cause. Option A is incorrect because S3 throttling would manifest as a different error (e.g., 'SlowDown' or 'RequestTimeout'). Option C is incorrect because Glue job concurrency limits would cause a 'ConcurrentRunsExceededException' or similar, not ThrottlingException. Option D is incorrect because insufficient memory would typically lead to an 'OutOfMemoryError' or job failure, not a ThrottlingException.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.