Pass the Amazon Web Services AWS Certified Associate DVA-C02 Questions and answers with ExamsMirror

The X-Ray daemon is a software that collects trace data from the X-Ray SDK and relays it to the X-Ray service. The X-Ray daemon can run on any platform that supports Go, including Linux, Windows, and macOS. The developer can install and run the X-Ray daemon on the on-premises servers to capture and relay the data to the X-Ray service with minimal configuration. The X-Ray SDK is used to instrument the application code, not to capture and relay data. The Lambda function solutions are more complex and require additional configuration.

This solution meets the requirements with the least amount of configuration because it uses a feature of AWS CDK that allows custom logic to be executed during stack deployment or deletion. The AWS Cloud Development Kit (AWS CDK) is a software development framework that allows you to define cloud infrastructure as code and provision it through CloudFormation. An AWS CDK custom resource is a construct that enables you to create resources that are not natively supported by CloudFormation or perform tasks that are not supported by CloudFormation during stack deployment or deletion. The developer can write a handler function in the code that uses AWS SDK calls tocheck for and delete unused resources, and create an AWS CDK custom resource that attaches the function code to a Lambda function and invokes it when the deployment stack runs. This way, the developer can automate the cleanup process without requiring additional configuration or integration. Creating a CloudFormation template from a JSON file will require additional configuration and integration with the central AWS CDK application. Creating an API in AWS Amplify will require additional configuration and integration with the central AWS CDK application and may not provide optimal performance or availability. Writing a handler function in the AWS Lambda console will require additional configuration and integration with the central AWS CDK application.

The solution that will meet the requirements most efficiently is to create a Lambda environment variable to store the table name. Use the standard method for the programming language to retrieve the variable. This way, the developer can avoid hard-coding the table name in the Lambda function code and easily change the table name by updating the environment variable. The other options either involve storing the table name in a file, which is less efficient and secure than using an environment variable, or creating a global variable, which is not recommended as it can cause concurrency issues.

Comprehensive Detailed and Lengthy Step-by-Step Explanation with All AWS Developer References:

1. Understanding the Use Case:

The company wants to gradually release a new feature to 15% of users to perform testing. AWS AppConfig is designed to manage and deploy configurations, including feature flags, allowing controlled rollouts.

2. Key AWS AppConfig Features:

Feature Flags:Enable or disable features dynamically without redeploying code.

Variants:Define different configurations for subsets of users.

Targeting Rules:Specify rules for which users receive a particular variant.

3. Explanation of the Options:

Option A:"Set up a custom script within the application to randomly select 15% of users. Assign a flag for the new feature to the selected users."While possible, this approach requires significant operational effort to manage user selection and ensure randomness. It does not leverage AWS AppConfig's built-in capabilities, which increases overhead.

Option B:"Create separate AWS AppConfig feature flags for both groups of users. Configure the flags to target 15% of users."Creating multiple feature flags for different user groups complicates configuration management and does not optimize the use of AWS AppConfig.

Option C:"Create an AWS AppConfig feature flag. Define a variant for the new feature, and create a rule to target 15% of users."This is the correct solution. Using AWS AppConfig feature flags with variants and targeting rules is the most efficient approach. It minimizes operational overhead by leveraging AWS AppConfig's built-in targeting and rollout capabilities.

Option D:"Use AWS AppConfig to create a feature flag without variants. Implement a custom traffic splitting mechanism in the application code."This approach requires custom implementation within the application code, increasing complexity and operational effort.

4. Implementation Steps for Option C:

Set Up AWS AppConfig:

Open theAWS Systems Manager Console.

Navigate toAppConfig.

Create a Feature Flag:

Define a new configuration for the feature flag.

Add variants (e.g., "enabled" for the new feature and "disabled" for no change).

Define a Targeting Rule:

Usepercentage-based targetingto define a rule that applies the "enabled" variant to 15% of users.

Targeting rules can use attributes like user IDs or geographic locations.

Deploy the Configuration:

Deploy the configuration using a controlled rollout to ensure gradual exposure.

Amazon CloudWatch is a service that monitors AWS resources and applications. The developer can use CloudWatch to monitor and troubleshoot all applications from one place. To do so, the developer needs to download the CloudWatch agent to the on-premises server and configure the agent to use IAM user credentials with permissions for CloudWatch. The agent will collect logs and metrics from the on-premises server and send them to CloudWatch.

Step-by-Step Breakdown:

Requirement Summary:

In-transit encryption (when data is moving between client and S3)

Encryptionat rest using AWS KMS keys(which arerotatableon-demand)

Option A: Write an S3 bucket policy to allow only encrypted connections over HTTPS by using permissions boundary

❌Incorrect: Permissions boundaries are for IAM policy control, not S3 bucket policies.

Also, encryption enforcement in-transit is not achieved through permissions boundaries.

Option B: Configure an S3 bucket policy to enable client-side encryption

❌Incorrect: AWS doesnotenforce client-side encryption via S3 bucket policies.

Client-side encryption must be handled entirely by the application.

Also, KMS is forserver-side encryption, not client-side.

Option C: Configure the application to encrypt the objects by using an AWS KMS customer managed key before uploading the objects

✅Correct: This fulfills the requirement of encrypting objectsat restusing aKMS CMK, which can be rotated.

You can specify the KMS key using the SSE-KMS option when putting an object.

Option D: Write an S3 bucket policy to allow only encrypted connections over HTTPS by using the aws:SecureTransport condition

✅Correct: This enforcesin-transit encryption, which ensures that only HTTPS connections are allowed.

This is the AWS-recommended way to enforce encryption in transit via bucket policy.

Option E: Configure S3 Block Public Access settings for the S3 bucket to allow only encrypted connections over HTTPS

❌Incorrect: Block Public Access is used to prevent public exposure of the bucket.

It hasnothing to do with encryption enforcement, whether in transit or at rest.

Using SSE-KMS:https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html

aws:SecureTransport condition in bucket policies:https://docs.aws.amazon.com/AmazonS3/latest/userguide/amazon-s3-policy-keys.html

Server-side encryption with AWS KMS:https://docs.aws.amazon.com/AmazonS3/latest/userguide/serv-side-encryption.html

Comprehensive Detailed Step by Step Explanation with All AWS Developer References:

To ensure that only a specific AWS Step Functions state machine (myStateMachine) can assume the service role, you must configure the correct trust policy in AWS IAM.

Trust Policies: Trust policies determine which entities (services or users) are allowed to assume the role. In this case, we want to restrict the trust policy to only allow the specific state machine (myStateMachine) to assume the role.

Using ArnLike: The condition "ArnLike" is used to specify that the SourceArn (which refers to the ARN of the entity assuming the role) must match a specific ARN. OptionAspecifies the exact ARN of the myStateMachine state machine, ensuring that only this state machine can assume the role.

Option B: This option is incorrect because it uses a wildcard (*) for the account ID, which would allow any state machine in the ap-south-1 region to assume the role, not just the specific one.

This is a frequent trouble. Web applications cannot access the resources in other domains by default, except some exceptions. You must configure CORS on the resources to be accessed.https://docs.aws.amazon.com/AmazonS3/latest/userguide/cors.html

Using a dead-letter queue (DLQ) with Amazon SQS is the most operationally efficient solution for handling unprocessable messages.

Amazon SQS Dead-Letter Queue:

A DLQ is used to capture messages that fail processing after a specified number of attempts.

Allows the application to continue processing other messages without being blocked.

Messages in the DLQ can be analyzed later for debugging and resolution.

Why DLQ is the Best Option:

Operational Efficiency: Automatically defers messages with errors, ensuring the queue is not blocked.

Analysis Ready: Messages in the DLQ can be inspected to identify recurring issues.

Scalable: Works seamlessly with Lambda and SQS at scale.

Why Not Other Options:

Option A: Logs the messages but does not resolve the queue blockage issue.

Option C: FIFO queues and 0-second retention do not provide error handling or analysis capabilities.

Option D: Alerts administrators but does not handle or store the unprocessable messages.

Steps to Implement:

Create a new SQS queue to serve as the DLQ.

Attach the DLQ to the primary queue and configure the Maximum Receives setting.

The solution that will meet the requirements is to add a test phase to the amplify.yml build settings for the application. This way, the developer can run end-to-end tests on every code commit and catch any bugs before deploying to production. The other options either do not support end-to-end testing, or do not run tests automatically.

AWS CloudFormation is a service that enables developers to model and provision AWS resources using templates. The developer can add a CloudFormation Deletion Policy attribute with the Retain value to the database resource. This will prevent the database from being deleted when the stack is deleted or updated. The developer can also update the CloudFormation stack policy to prevent updates to the database. This will prevent accidental changes to the database configuration or properties.