DOP-C02 Resilient Cloud Solutions Practice Question
A media company runs a video processing pipeline on AWS. Raw videos are uploaded to an S3 bucket, which triggers a Lambda function to start an AWS Batch job for transcoding. The Batch job reads the source video from S3, processes it, and writes the output to another S3 bucket. Recently, the company has seen an increase in processing failures. Investigation shows that the Batch jobs are being terminated with a 'TIMEOUT' status after running for exactly 30 minutes. The video files are large, and some jobs legitimately take up to 45 minutes. The Batch job definition has a 'timeout' setting configured. Which action should be taken to resolve this issue?
⚠ Common exam trap
DOP-C02 often tests whether candidates can pinpoint the exact configuration parameter responsible for a symptom — here, confusing the Lambda trigger timeout with the Batch job definition timeout, or blaming the compute environment, is the trap.
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 Batch job definition to increase the 'timeout' value to 3600 seconds (60 minutes).
The Batch job is being terminated with TIMEOUT after exactly 30 minutes, and the job definition has a timeout setting — this is the Batch job attempt timeout (default 30 minutes if not explicitly set, or set to 1800 seconds). Since legitimate jobs take up to 45 minutes, the fix is to raise the job definition's timeout to at least 3600 seconds (60 minutes) to accommodate the longest jobs with headroom.
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 Batch job definition to increase the 'timeout' value to 3600 seconds (60 minutes).
Why this is correct
The AWS Batch job definition includes a `timeout` field that sets the maximum duration a job attempt is allowed to run before Batch forcibly terminates it as a timeout. Increasing this value to 3600 seconds (60 minutes) directly accommodates longer video processing tasks, preventing premature termination when the workload legitimately needs more than the current limit. This is the intended control for adjusting how long Batch permits a single job attempt to execute.
- ✗
Increase the S3 bucket lifecycle policy to retain videos longer.
Why it's wrong here
An S3 lifecycle policy is designed to manage object storage behavior—such as transitioning data to colder storage classes or expiring/deleting objects after a specified age—and has no bearing on the runtime constraints applied to AWS Batch jobs. Retaining videos for a longer period in S3 only ensures the source files remain accessible; it does not extend the duration for which a Batch job can execute. The job will still be terminated at the timeout defined in its job definition.
- ✗
Increase the Lambda function timeout to 60 minutes.
Why it's wrong here
AWS Lambda has a hard maximum invocation timeout of 15 minutes (900 seconds), so even attempting to set the Lambda function timeout to 60 minutes is technically impossible; the AWS API will reject the value. Moreover, Lambda in this architecture is likely only the orchestration step that submits the Batch job, and its own timeout merely bounds how long the function can run while submitting or polling—not how long the Batch job itself may process videos. Therefore, adjusting Lambda's timeout cannot increase the Batch job's allowed runtime.
- ✗
Change the Batch job queue to a different compute environment.
Why it's wrong here
The Batch job queue is a logical conduit that routes submitted jobs to a specific compute environment, which defines the infrastructure (EC2 instances or Fargate) on which jobs run. Changing the queue or compute environment alters resource provisioning, instance types, or scaling behavior, but it does not modify the `timeout` property stored in the job definition. A different compute environment might cause the job to start sooner or gain more CPU/memory, but it will still be killed by Batch at the same attempt-duration limit as before.
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 DOP-C02 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 DOP-C02 exam.