Download AWS Certified Developer - Associate.DVA-C02.Dump4Pass.2025-03-07.138q.vcex

Enable API caching in API Gateway.Enabling API caching in API Gateway can help enhance the responsiveness of the APIs by reducing the need to repeatedly process the same requests and responses. When a client makes a request to an API, the API Gateway can cache the response, and subsequent identical requests can be served from the cache, saving processing time and reducing the load on backend resources like AWS Lambda.This option makes the most sense in the context of improving responsiveness. While the other options (B, C, and D) are important considerations for various aspects of API development and security, they are not directly related to enhancing responsiveness in the same way that caching is.

Define a development stage on the API Gateway API. Instruct the other developers to point to the development stage.Creating a separate development stage within the existing API Gateway REST API allows the other developers to work on the new version of the API without affecting the customers who are using the existing frontend UI and Amazon authentication. This approach provides isolation and flexibility for development while keeping the existing production version intact.Option A minimizes operational overhead by allowing the new version to be developed and tested independently in a controlled environment (the development stage) without impacting the production stage that customers are using. It also avoids the need to create a completely new API or modify the existing one.The other options (B, C, and D) involve more complex changes, such as creating entirely new APIs, implementing version selection mechanisms in the application code, or specifying new endpoints, which could introduce additional operational complexity and potential disruption to existing customers.

Lambda@Edge functions can be created only in the us-east-1 Region.Lambda@Edge functions, which are designed to run in conjunction with Amazon CloudFront distributions to provide serverless compute capabilities closer to the user, can currently only be created in the us-east-1 Region. This restriction means that when using Lambda@Edge, you must create the Lambda@Edge functions in the us-east-1 Region, even if your other resources (like the CloudFront distribution, S3 bucket, etc.) are in a different region.Given that the developer is creating a Lambda@Edge function in the CloudFront distribution, and the deployment is failing in the eu-west-1 Region, the most likely reason is that the Lambda@Edge function is not supported in the eu-west-1 Region. Therefore, Option B is the correct explanation for the issue.The other options (A, C, and D) are not accurate explanations for the problem:Option A is not true. CloudFront distributions can be created in multiple AWS regions, not just us-east-1.Option C is not true. A single AWS SAM template can contain multiple Lambda functions.Option D is not true. There is no restriction preventing a CloudFront distribution and an S3 bucket from being created in the same region.

The correct answer is:CodeDeployDefault.ECSCanary10Percent15MinutesIn AWS CodeDeploy, predefined configurations help you define deployment strategies based on the platform you are deploying to. In this case, you are deploying an application to Amazon ECS and you have specific requirements for gradually exposing traffic to the new version."Canary" deployment strategy involves gradually shifting traffic from the old version to the new version."ECSCanary" specifies that you are deploying to Amazon ECS."10Percent" indicates that initially, only 10% of live traffic will be exposed to the new version."15Minutes" means that after 15 minutes have elapsed, all the remaining live traffic will be routed to the new version.So, the correct predefined configuration that meets your requirements is"CodeDeployDefault.ECSCanary10Percent15Minutes" (Option A).

Run the cdk synth and sam local invoke commands with the function construct identifier and the path to the synthesized CloudFormation template.To test a specific Lambda function locally when using AWS CDK, you can follow these steps:Use the cdk synth command to generate the CloudFormation template that represents your AWS CDK stack.Use the sam local invoke command along with the function construct identifier to test the specific Lambda function. The sam local invoke command simulates the Lambda invocation environment locally.Here's how you would do it:shCopy codecdk synth --output cdk.outsam local invoke MyFunctionName -t cdk.out/MyStack.template.jsonReplace MyFunctionName with the name of your Lambda function and MyStack with the name of your AWS CDK stack.This approach leverages the sam local invoke command from AWS SAM to locally test a specific Lambda function defined in your AWS CDK stack.Option A is incorrect because it mentions using SAM commands (sam package and sam deploy), which are related to AWS SAM, not AWS CDK.Option B is incorrect because it mentions using CDK commands (cdk synth and cdk deploy), but it doesn't use the appropriate method for locally testing a specific Lambda function.Option D is incorrect because there's no sam local start-lambda command in AWS SAM or AWS CDK.

Install and run the X-Ray daemon on the on-premises servers to capture and relay the data to the X-Ray service.The X-Ray daemon (Option B) is the correct option to enable X-Ray tracing on on-premises servers with the least amount of configuration. The X-Ray daemon simplifies the process of capturing and relaying tracing data from on-premises applications to the X-Ray service. It requires minimal configuration and can be quickly set up to send trace data to X-Ray.The other options (A, C, and D) involve more complex setup and configuration:Option A requires installing and integrating the X-Ray SDK into the application code running on the on-premises servers.Option C and Option D involve setting up AWS Lambda functions to pull, process, and relay trace data to X-Ray, which introduces additional complexity compared to using the X-Ray daemon.By using the X-Ray daemon, you can achieve X-Ray tracing with minimal configuration and quickly start capturing trace data from the on-premises servers.

Create an Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system in Lambda.Store the result files and log file in the mount point. Append the log entries to the log file.The requirements of sharing files and appending results and log entries to an existing file are well suited for using Amazon Elastic File System (Amazon EFS). Here's how this solution meets the requirements:**Shared Access**: Amazon EFS allows you to create a centralized file system that can be mounted by multiple AWS Lambda functions, AWS services, and on-premises resources. This enables shared access to files among various resources.**Appending Log Entries**: Since Amazon EFS allows multiple writers to the same file, you can append log entries to the shared log file from different Lambda functions. This ensures that all log entries are captured in the same log file.**Persisting Result Files**: You can store the result files and log files in the mounted Amazon EFS file system. This provides persistent storage for your data, and all resources with access to the EFS mount point can read and write to the files.Option B (Amazon EBS Multi-Attach enabled volume) doesn't provide shared access to the volume across different resources in the same way that Amazon EFS does. EBS volumes can only be attached to a single instance at a time.Options C and D (storing in /tmp and /opt directories) are not ideal for sharing files across different resources and preserving data between invocations. The /tmp directory, in particular, is limited by local storage and is cleared between invocations, so it's not suitable for persistently storing result files or log entries.For these reasons, Option A is the most suitable solution for the requirements mentioned in the scenario.

Create an AWS Lambda function by using the AWS Serverless Application Model (AWS SAM). Expose the Lambda functionality by using Amazon API Gateway.Given the requirements for a proof of concept application that shows the weather forecast for a specific office location, with limited traffic during testing and the need to use caching features provided by AWS to limit backend requests, using AWS Lambda along with Amazon API Gateway is the most cost-effective and suitable option.Here's why Option B is the best choice:**Serverless Architecture**: AWS Lambda is well-suited for small-scale applications with infrequent traffic. You don't need to worry about provisioning and managing servers, and you're only billed based on actual usage.**AWS SAM**: The AWS Serverless Application Model (AWS SAM) simplifies the process of creating, deploying, and managing serverless applications. It's a great fit for quick prototyping and proof of concept development.**Limited Traffic**: Since the application is expected to receive a small amount of traffic during testing, AWS Lambda's pay-as-you-go model ensures you're not overpaying for unused resources.**Caching**: Amazon API Gateway supports caching at the API level, allowing you to reduce the number of requests to the backend service. This can help limit costs and improve response times.Options A, C, and D involve more complex setups with services like Amazon EKS, Amazon ECS, and Elastic Beanstalk. These options might be overkill for a proof of concept with limited traffic and could incur unnecessary costs.Option B aligns well with the requirements for a cost-effective, serverless, and cached REST endpoint for the weather forecast application.

Use the AWS::Region pseudo parameter.The AWS::Region pseudo parameter is the most operationally efficient way to determine the Region in which the CloudFormation template is being deployed. This parameter provides the current AWS Region where the stack is being created or updated.Using this pseudo parameter ensures that you always have the correct AWS Region value without the need for additional configuration or input. It's automatically populated by CloudFormation and provides a convenient and accurate way to reference the current Region within your template.Option B (requiring the Region as a CloudFormation parameter) adds unnecessary complexity by requiring the user to input the Region value, which should ideally be determined automatically.Option C (finding the Region from the AWS::StackId pseudo parameter) using the Fn::Split intrinsic function introduces additional complexity and might not be as straightforward as using the AWS::Region pseudo parameter.Option D (dynamically importing the Region from AWS Systems Manager Parameter Store) adds complexity and overhead compared to simply using the AWS::Region pseudo parameter.Overall, Option A is the most straightforward and operationally efficient way to determine the AWS Region in which the CloudFormation template is being deployed.