Download AWS Certified Developer - Associate.DVA-C02.Dump4Pass.2025-03-28.209q.tqb

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.

This policy denies the s3:GetObject action for any request that is not made over a secure (encrypted) connection.  

Store the session state in Amazon ElastiCache.Amazon ElastiCache is a managed in-memory data store service provided by AWS. It's designed to enhance the performance of web applications by allowing you to retrieve information from fast, managed, in-memory data stores. In the context of session state management, ElastiCache offers several benefits that align with the given requirements:Fault Tolerance: ElastiCache provides fault tolerance by automatically replicating data across multiple Availability Zones, ensuring high availability even in the event of an infrastructure failure.Natively Highly Scalable: ElastiCache is designed for scalability, allowing you to scale the cache as your application demands grow. It supports clustering, which enables you to distribute data across multiple nodes.Service Interruption Mitigation: Storing session state in ElastiCache helps mitigate service interruptions because cached data is stored in-memory, which provides faster access than traditional databases. This can lead to a more responsive user experience even if there's a temporary interruption to other services.Session Stickiness: ElastiCache can be used to manage session state for applications that require session stickiness. Elastic Load Balancers (ELBs) can be configured to route requests to the appropriate cache node based on session information.Amazon CloudFront (Option B) is a content delivery network (CDN) service that helps distribute content globally with low latency. While it can enhance performance, it's not specifically designed for storing and managing session state.Amazon S3 (Option C) is a scalable object storage service, but it's not typically used for storing dynamic session state due to the fact that read and write latencies can be higher compared to in-memory data stores like ElastiCache.Enabling session stickiness using elastic load balancers (Option D) is a valid approach, but it doesn't address the need for a fault-tolerant, highly scalable, and natively responsive session state storage solution, which ElastiCache provides.Thus, Option A (Store the session state in Amazon ElastiCache) is the best option for the given requirements.

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.