- A
Use a custom build environment with the same compute type.
Why wrong: Custom environment does not change memory allocation.
- B
Enable local caching in the build project to reduce disk I/O.
Why wrong: Caching does not increase memory.
- C
Change the build project's compute type to a larger instance (e.g., from BUILD_GENERAL1_SMALL to BUILD_GENERAL1_MEDIUM).
Larger compute types provide more memory.
- D
Split the tests into multiple build projects and run them in parallel.
Why wrong: This may not solve the memory issue for individual tests.
Question 317 of 1,750
DOP-C02 SDLC Automation Practice Question
This DOP-C02 practice question tests your understanding of sdlc automation. 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 company uses AWS CodeBuild to run unit tests as part of their CI/CD pipeline. The tests are memory-intensive and occasionally fail due to insufficient memory. The buildspec.yml file uses the default compute type. What is the most cost-effective solution to resolve the memory 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
✓
Change the build project's compute type to a larger instance (e.g., from BUILD_GENERAL1_SMALL to BUILD_GENERAL1_MEDIUM).
Option C is correct because increasing the compute type (e.g., from BUILD_GENERAL1_SMALL to BUILD_GENERAL1_MEDIUM) directly provides more memory for the build environment, resolving the out-of-memory failures. This is the most cost-effective solution as it only increases resources for the specific build project that needs them, without requiring architectural changes or additional build projects.
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.
- ✗
Use a custom build environment with the same compute type.
Why it's wrong here
Custom environment does not change memory allocation.
- ✗
Enable local caching in the build project to reduce disk I/O.
Why it's wrong here
Caching does not increase memory.
- ✓
Change the build project's compute type to a larger instance (e.g., from BUILD_GENERAL1_SMALL to BUILD_GENERAL1_MEDIUM).
Why this is correct
Larger compute types provide more memory.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Split the tests into multiple build projects and run them in parallel.
Why it's wrong here
This may not solve the memory issue for individual tests.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse memory issues with disk I/O or think that parallelizing tests will reduce per-instance memory pressure, but in reality, each parallel build runs on its own instance with the same memory limit, so the failure persists and costs increase.
Detailed technical explanation
How to think about this question
CodeBuild compute types (BUILD_GENERAL1_SMALL, MEDIUM, LARGE, etc.) map to specific EC2 instance families with defined vCPU and memory allocations; for example, BUILD_GENERAL1_SMALL provides 3 GB of memory, while BUILD_GENERAL1_MEDIUM provides 7 GB. The memory-intensive tests likely exceed the 3 GB limit, causing the build process to be killed by the Linux OOM killer. Increasing the compute type is a direct scaling action that matches the resource profile to the workload, and because CodeBuild charges per minute based on the compute type, using a medium instance only for the duration of the build is more cost-effective than running multiple small instances in parallel.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
Visual reference
What to study next
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FAQ
Questions learners often ask
What does this DOP-C02 question test?
SDLC Automation — This question tests SDLC Automation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Change the build project's compute type to a larger instance (e.g., from BUILD_GENERAL1_SMALL to BUILD_GENERAL1_MEDIUM). — Option C is correct because increasing the compute type (e.g., from BUILD_GENERAL1_SMALL to BUILD_GENERAL1_MEDIUM) directly provides more memory for the build environment, resolving the out-of-memory failures. This is the most cost-effective solution as it only increases resources for the specific build project that needs them, without requiring architectural changes or additional build projects.
What should I do if I get this DOP-C02 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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Last reviewed: Jul 4, 2026
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