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Amazon AWS Certified Developer - Associate Sample Questions (Q409-Q414):

NEW QUESTION # 409
A developer is creating a serverless application that uses an AWS Lambda function The developer will use AWS CloudFormation to deploy the application The application will write logs to Amazon CloudWatch Logs The developer has created a log group in a CloudFormation template for the application to use The developer needs to modify the CloudFormation template to make the name of the log group available to the application at runtime Which solution will meet this requirement?

A. Pass the log group's Amazon Resource Name (ARN) as an environment variable to the Lambda function
B. Pass the log group's name to the application in the user data section of the CloudFormation template.
C. Use the CloudFormation template's Mappings section to specify the log group's name for the application.
D. Use the AWS:lnclude transform in CloudFormation to provide the log group's name to the application

Answer: A

Explanation:
* CloudFormation and Lambda Environment Variables:
* CloudFormation is an excellent tool to manage infrastructure as code, including the log group resource.
* Lambda functions can access environment variables at runtime, making them a suitable way to pass configuration information like the log group ARN.
* CloudFormation Template Modification:
* In your CloudFormation template, define the log group resource.
* In the Lambda function resource, add an Environment section:
YAML
Environment:
Variables:
LOG_GROUP_ARN: !Ref LogGroupResourceName
Use code with caution.
content_copy
* The !Ref intrinsic function retrieves the log group's ARN, which CloudFormation generates during stack creation.
* Using the ARN in Your Lambda Function:
* Within your Lambda code, access the LOG_GROUP_ARN environment variable.
* Configure your logging library (e.g., Python's logging module) to send logs to the specified log group.
References:
* AWS Lambda Environment Variables: https://docs.aws.amazon.com/lambda/latest/dg/configuration- envvars.html
* CloudFormation !Ref Intrinsic Function: https://docs.aws.amazon.com/AWSCloudFormation/latest
/UserGuide/intrinsic-function-reference-ref.html

NEW QUESTION # 410
A company is adopting serverless computing for some of its new services. A development team needs to create a serverless infrastructure by using AWS Serverless Application Model (AWS SAM). All infrastructure must be deployed by using AWS CloudFormation templates.
What should the development team do to meet these requirements?

A. Add a Resources section to the CloudFormation templates that contains AWS::Lambda::Function resources.
B. Add a Parameters section to the CloudFormation templates that specifies the relevant AWS SAM Globals section.
C. Add a Transform section to the CloudFormation templates. Use the AWS SAM syntax to define the resources.
D. Add a Mappings section to the CloudFormation templates that contains AWS::Serverless::Function and AWS::Serverless::API.

Answer: B

NEW QUESTION # 411
A financial services company uses Amazon API Gateway HTTP APIs with AWS Lambda. All request logs are stored in Amazon CloudWatch Logs and are shared with developer AWS accounts and a separate fraud detection AWS account by using a cross-account IAM role.
The company requires that only the fraud detection account can view customer credit card numbers in logs.
Developers must not be able to view this sensitive data.
The developers create a CloudWatch Logs data protection policy that masks credit card numbers.
Which combination of actions must the developers take to comply with the policy? (Select TWO.)

A. Add an UnmaskConfig property to the data protection policy and specify the fraud detection IAM role.
B. Attach the data protection policy to the CloudWatch log group in the fraud detection account.
C. Grant the logs:Unmask permission to the IAM role assumed by the fraud detection account.
D. Attach the data protection policy to the CloudWatch log group that stores the application logs.
E. Attach the data protection policy to the IAM role assumed by the fraud detection account.

Answer: A,C

Explanation:
Amazon CloudWatch Logs data protection policies allow sensitive data such as credit card numbers to be automatically detected and masked at ingestion time. By default, masked data cannot be viewed by any user.
AWS documentation states that to allow specific principals to view masked data, two conditions must be met:
* The data protection policy must include an UnmaskConfig that specifies which IAM roles are permitted to view the unmasked data.
* The IAM role must have the logs:Unmask permission.
Option A configures the policy to allow the fraud detection role to unmask sensitive fields. Option B grants the necessary permission to that role. Together, these steps ensure that only the fraud detection account can view credit card numbers.
Attaching the policy to the log group (Option D) is necessary for masking but does not grant unmask access on its own. IAM roles cannot contain data protection policies (Option E), and policies are not attached in the consuming account (Option C).
Therefore, the correct combination is A and B, ensuring strict access control while maintaining compliance with security requirements.

NEW QUESTION # 412
A developer is building a three-tier application with an Application Load Balancer (ALB), Amazon EC2 instances, and Amazon RDS. There is an alias record in Amazon Route 53 that points to the ALB. When the developer tries to access the ALB from a laptop, the request times out.
Which logs should the developer investigate to verify that the request is reaching the AWS network?

A. Amazon CloudWatch agent logs
B. Amazon Route 53 logs
C. AWS Systems Manager Agent logs
D. VPC Flow Logs

Answer: D

NEW QUESTION # 413
A developer created an AWS Lambda function to process data in an application. The function pulls large objects from an Amazon S3 bucket, processes the data, and loads the processed data into a second S3 bucket.
Application users have reported slow response times. The developer checks the logs and finds that Lambda function invocations run much slower than expected. The function itself is simple and has a small deployment package. The function initializes quickly. The developer needs to improve the performance of the application.
Which solution will meet this requirement with the LEAST operational overhead?

A. Create a Lambda layer to package the function dependencies. Add the layer to the function.
B. Create an Amazon EventBridge rule to schedule invocations of the function every minute.
C. Configure the function to use ephemeral storage. Upload the objects and process data in the /tmp directory.
D. Store the data in an Amazon EFS file system. Mount the file system to a local directory in the function.

Answer: C

Explanation:
Configuring the Lambda function to use ephemeral storage and processing data in the /tmp directory improves performance by leveraging local storage during execution.
* Why Option C is Correct:
* Ephemeral Storage: Lambda provides temporary storage (up to 10 GB) in the /tmp directory for each invocation, which is faster than pulling data directly from S3 multiple times.
* Performance Boost: Data can be downloaded to /tmp, processed locally, and uploaded to the destination S3 bucket, minimizing S3 network calls.
* Low Overhead: This approach requires only minimal changes to the function's configuration.
* Why Not Other Options:
* Option A: Using Amazon EFS adds complexity and is unnecessary for this use case.
* Option B: Scheduling the function does not address the root cause of slow performance.
* Option D: Lambda layers improve deployment efficiency, not runtime performance for this scenario.
Using Ephemeral Storage in AWS Lambda
Best Practices for S3 and Lambda Performance

NEW QUESTION # 414
......

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