DEA-C01 Data Operations and Support Practice Question
A data engineer manages an AWS Glue ETL job that reads JSON files from Amazon S3, transforms the data, and writes to an Amazon Redshift table. The job recently started failing with the error 'Communication link failure: connection reset'. The Redshift cluster is healthy and the IAM role used by the Glue job has the necessary permissions. The engineer notices that the job runs longer than before and the Redshift cluster's WLM queue is often full. Which action should the engineer take to resolve the failure?
⚠ Common exam trap
The trap here is assuming that connection reset errors are always network-related and can be fixed by increasing timeouts or retries, when in fact they often stem from Redshift workload management limits.
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
✓
Modify the Redshift WLM configuration to increase concurrency or add a new queue for the Glue job.
The failure is caused by Redshift's workload management (WLM) queue being full, which leads to connection timeouts and resets when the AWS Glue job attempts to write data. Adjusting the WLM configuration to increase concurrency or create a dedicated queue for the Glue job reduces wait times and prevents the connection resets. This directly addresses the root cause without unnecessary changes to the Glue job or architecture.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Modify the Redshift WLM configuration to increase concurrency or add a new queue for the Glue job.
Why this is correct
The error 'Communication link failure: connection reset' often occurs when Redshift terminates connections due to WLM queue timeouts. Increasing concurrency or adding a dedicated queue for the Glue job reduces wait times and prevents connection resets. This directly addresses the root cause of the failure, allowing the job to complete successfully without changing Glue resources.
- ✗
Configure the Glue job to use the Redshift JDBC driver with a longer connection timeout and retry logic.
Why it's wrong here
Increasing the connection timeout and adding retries may temporarily mask the symptom, but the underlying issue is that the Redshift WLM queue is full, causing connections to be dropped. Retries could succeed if the queue clears, but they do not address the root cause and can lead to longer job runtimes. This is a workaround, not a solution, and may still fail under sustained load.
- ✗
Increase the number of AWS Glue DPUs allocated to the job to speed up processing.
Why it's wrong here
Adding DPUs increases the number of concurrent connections and write throughput to Redshift, which can worsen contention on an already full WLM queue and cause more connection resets. The root cause is Redshift-side queuing, not insufficient Glue compute. While more DPUs can help with large datasets, here it would likely exacerbate the problem because the Redshift cluster cannot keep up with the increased load.
- ✗
Switch the Glue job to write to Amazon S3 first, then use a Redshift COPY command to load the data.
Why it's wrong here
Writing to S3 and then using COPY is a valid pattern for bulk loads, but it adds complexity and does not directly solve the WLM queue timeout issue. The COPY command would still be subject to the same WLM queue and could also fail if the queue is full. Moreover, the Glue job already writes to Redshift; changing the architecture is unnecessary and may not resolve the connection reset.
Visual reference
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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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
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.