The correct choice is to use Lambda Layers to manage dependencies separately from the function code. This works because Lambda Layers allow you to offload bulky libraries like Python packages into a separate, cached archive, which keeps the core deployment package small. When a cold start occurs, Lambda must download and extract the function code; a smaller package directly reduces this overhead, while the layer itself is cached across executions, further minimizing latency. On the AWS Certified Developer Associate DVA-C02 exam, this question tests your understanding of deployment optimization and the Lambda execution environment—a common trap is thinking that packaging everything into a single zip is simpler, but that actually increases cold start time. Remember the memory tip: “Layer the weight, shrink the wait.”
DVA-C02 Deployment Practice Question
This DVA-C02 practice question tests your understanding of deployment. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 developer is deploying a Python application to AWS Lambda. The application has several dependencies. The developer wants to ensure that the deployment package is as small as possible to reduce cold start times. What should the developer do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Lambda Layers to manage dependencies separately from the function code.
Lambda Layers allow you to package and manage dependencies (like Python libraries) separately from your function code. By moving dependencies to a layer, the function code deployment package becomes smaller, which reduces the time Lambda spends downloading and extracting the package during cold starts. Layers are cached across executions, further improving performance.
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 Lambda Layers to manage dependencies separately from the function code.
Why this is correct
Layers reduce the function code size and are reused across functions.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use an S3 bucket to store the deployment package.
Why it's wrong here
S3 is storage, not size reduction.
✗
Use AWS CodeBuild to optimize the package.
Why it's wrong here
CodeBuild does not reduce package size.
✗
Include all dependencies in the deployment package.
Why it's wrong here
This increases package size.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think including all dependencies in the package is simpler or more reliable, but they overlook that Lambda Layers are the designed AWS mechanism to reduce deployment package size and improve cold start performance.
Detailed technical explanation
How to think about this question
Lambda Layers are stored separately from the function code and are extracted into /opt in the execution environment. When a function uses a layer, Lambda only downloads and extracts the function code (typically smaller) during a cold start, while the layer is reused across invocations if already cached. This separation is especially beneficial for Python applications with heavy libraries like NumPy or Pandas, where the layer can be shared across multiple functions without bloating each deployment package.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Deployment — This question tests Deployment — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Lambda Layers to manage dependencies separately from the function code. — Lambda Layers allow you to package and manage dependencies (like Python libraries) separately from your function code. By moving dependencies to a layer, the function code deployment package becomes smaller, which reduces the time Lambda spends downloading and extracting the package during cold starts. Layers are cached across executions, further improving performance.
What should I do if I get this DVA-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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Question Discussion
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