- A
Increase the timeout of the Glue ETL job to allow more time for retries.
Why wrong: Increasing timeout does not resolve the rate limiting error.
- B
Disable workload management (WLM) concurrency scaling in Redshift.
Why wrong: Disabling concurrency scaling would reduce capacity, worsening the issue.
- C
Enable auto-tuning on the Redshift cluster and use concurrency scaling.
Auto-tuning with concurrency scaling dynamically adds capacity to handle increased write requests.
- D
Change the output file format from Parquet to CSV to reduce write size.
Why wrong: Data format has no impact on write rate limits.
Question 381 of 1,786
Resolving Redshift Write Throttling in AWS Glue
This DEA-C01 practice question tests your understanding of data operations and support. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option C is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Under the hood, the 'Rate exceeded' error in Redshift is often tied to the WLM queue's maximum concurrency level or the cluster's overall write throughput limit. Concurrency scaling works by provisioning additional transient clusters that share the same data and schema, allowing write operations to be distributed across multiple queues. In a real-world scenario, a Glue ETL job writing large batches of data during peak hours can overwhelm a single queue, and concurrency scaling automatically routes excess queries to the scaled-out capacity.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Visual reference
What to study next
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Operations and Support — This question tests Data Operations and Support — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable auto-tuning on the Redshift cluster and use concurrency scaling. — Option C is correct because 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.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Same concept, more angles
1 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 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.
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Last reviewed: Jul 4, 2026
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