How to Reduce Lambda Cold Start Times: Memory Increase and Scheduled Invocations
Which TWO actions can help reduce Lambda cold start times? (Choose two.)
⚠ Common exam trap
DVA-C02 often tests the misconception that a larger deployment package or VPC placement improves cold start performance, when in reality both tend to worsen it; the correct levers are memory allocation and Provisioned Concurrency.
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
✓
Increase the memory allocated to the function.
Option B is correct because AWS Lambda allocates CPU proportionally to the configured memory, so increasing the memory allocated to the function gives it more CPU power and speeds up initialization of the runtime and code, thereby reducing cold start duration. Option C is correct because Provisioned Concurrency pre-initializes a requested number of execution environments and keeps them warm, so invocations are served by already-initialized environments and avoid the cold start entirely. Option A is incorrect because a larger deployment package takes longer to download and unpack during initialization, which increases cold start time. Option D is incorrect because placing the function in a VPC adds ENI creation and attachment overhead during initialization, typically worsening cold starts. Option E is incorrect because the function timeout only limits how long an invocation may run; it does not affect initialization time and can even cause failures if set too low.
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 deployment package size.
Why it's wrong here
Cold start time scales with the size of the code package because the runtime must download, unpack, and load the code before executing. Larger deployment packages increase initialization time, not decrease it, so this option actually worsens cold starts. To reduce cold starts, keep the package lean by minimizing dependencies and using layers only when necessary.
- ✓
Increase the memory allocated to the function.
Why this is correct
Lambda allocates CPU proportionally to the amount of memory configured, so more memory means more CPU power available during initialization. This speeds up tasks like loading the runtime, unpacking code, and running static initializers, thereby shortening the cold start duration. It is a practical tuning knob, though it increases cost per invocation.
- ✓
Use Provisioned Concurrency.
Why this is correct
This initializes execution environments ahead of time and keeps them warm, so invocations skip the cold start initialization phase. It allocates pre-warmed instances that handle requests immediately, effectively eliminating latency from environment setup and code loading. This is a direct, guaranteed way to reduce cold starts, especially for latency-sensitive workloads.
- ✗
Place the function in a VPC.
Why it's wrong here
This does not reduce cold starts; it typically increases them because Lambda must attach an elastic network interface (ENI) to your VPC, adding several seconds to initialization. The ENI creation happens during the cold start phase, incurring extra overhead. Using a VPC is for secure access to private resources, not for performance, and you can mitigate the latency by using VPC endpoints or a NAT gateway, but those don't eliminate the ENI setup delay.
- ✗
Reduce the function timeout.
Why it's wrong here
The timeout setting caps how long an invocation can run; it has no impact on the initialization phase before your handler executes. Cold start measures the time to create the environment, load code, and run initializers, which happens before the timeout applies. Reducing the timeout might cause faster failures but does not accelerate environment provisioning.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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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 DVA-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 DVA-C02 exam.