DVA-C02 Development with AWS Services Practice Question

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 language runtime with faster startup time, such as Python or Node.js.

  Why this is correct

  Python and Node.js runtimes generally exhibit significantly faster startup times compared to compiled languages like Java or .NET. This is primarily due to their lighter runtime environments and quicker code interpretation/JIT compilation processes. They require less time to load the runtime, initialize the execution environment, and parse/execute the initial function code, thereby reducing the duration of a cold start. This optimization directly minimizes the latency experienced by the end-user during the first invocation of an idle function.

• ✗

  Place the Lambda function inside a VPC.

  Why it's wrong here

  Placing a Lambda function inside a Virtual Private Cloud (VPC) actually increases cold start latency rather than improving it. When a Lambda function is configured to access resources within a VPC, AWS Lambda must provision and attach an Elastic Network Interface (ENI) to the function's execution environment. This ENI creation and attachment process adds a significant amount of time, typically several seconds, to the initial invocation of a new execution environment, directly contributing to longer cold start durations.

• ✓

  Enable provisioned concurrency for the function.

  Why this is correct

  Enabling provisioned concurrency for a Lambda function is a direct and effective method to eliminate cold starts. This feature pre-initializes a specified number of execution environments, ensuring that they are ready to process invocations immediately. AWS Lambda keeps these environments "warm" and responsive, completely bypassing the initialization phase (downloading code, starting the runtime, executing initialization code) that characterizes a cold start. This guarantees consistent, low-latency performance for critical applications.

• ✓

  Increase the function's memory allocation.

  Why this is correct

  Increasing a Lambda function's memory allocation is an effective strategy to reduce cold start times because memory is directly correlated with CPU power. AWS Lambda allocates proportional CPU resources based on the configured memory. A higher memory setting provides the execution environment with more CPU cycles, allowing the runtime to load faster, dependencies to initialize quicker, and the function's code to execute its initial setup phase more rapidly. This accelerated initialization process significantly shortens the cold start duration.

• ✗

  Increase the function's timeout setting.

  Why it's wrong here

  Increasing a Lambda function's timeout setting has no impact whatsoever on cold start latency. The timeout parameter merely defines the maximum duration an invocation is allowed to run before AWS Lambda terminates it. It does not influence the speed at which the execution environment is initialized or the function code starts executing. While a longer timeout prevents premature termination of long-running functions, it offers no benefit in reducing the initial startup time of a new execution environment.

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