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Development with AWS Service 每 30%This subject area is dedicated to measuring the professionals＊ skills in implementing application design into an application code, writing code for serverless applications as well as code that interacts with the AWS services using AWS CLI & APIs, and translating the functional requirements into application designs.
Monitoring and Troubleshooting 每 12%The last objective is all about the individuals＊ skills required to identify faults as well as how to monitor them. The examinees need to have the expertise in performing root cause analysis on all the faults found during production or testing as well as writing code to get it monitored.
Deployment 每 22%The first exam part evaluates the test takers＊ expertise in deploying the written code in Amazon Webs Services using CD/CI pipelines & patterns as well as deploying serverless applications. It is also important that the candidates possess knowledge of how to get ready the deployment package applications so that they can be deployed into AWS and how to deploy applications using Elean Beanstalk.
Security 每 26%This domain validates the applicants＊ understanding of encryption and authentication methods. They have to be good at implementing encryption using AWS services, implementing applications for authentication & authorization, as well as making authenticated calls to Amazon Web Services.
Refactoring 每 10%This topic is focused on the students＊ knowledge of AWS services as well as the features that are included in it. They also have to demonstrate their competence in optimizing the applications for using the best features of AWS services and migrating the existing application code to run on AWS.
Amazon AWS Certified Developer Associate Exam (DVA-C02) Sample Questions (Q294-Q299):NEW QUESTION # 294
A Developer is writing a REST service that will add items to a shopping list. The service is built on Amazon API Gateway with AWS Lambda integrations. The shopping list items are send as query string parameters in the method request.
How should the Developer convert the query string parameters to arguments for the Lambda function?
A. Include the Amazon Resource Name (ARN) of the Lambda function
NEW QUESTION # 295
A company uses an AWS Lambda function to perform natural language processing (NLP) tasks. The company has attached a Lambda layer to the function. The Lambda layer contain scientific libraries that the function uses during processing.
The company added a large, pre-trained text-classification model to the Lambda layer. The addition increased the size of the Lambda layer to 8.7 GB. After the addition and a recent deployment, the Lambda function returned a RequestEntityTooLargeException error.
The company needs to update the Lambda function with a high-performing and portable solution to decrease the initialization time for the function.
Which solution will meet these requirements?
A. Create a Docker container that includes the scientific libraries and the pre-trained model. Update the Lambda function to use the container image.
B. Create an Amazon EFS file system to store the large pre-trained model. Mount the file system to an Amazon EC2 instance. Configure the Lambda function to use the EFS file system.
C. Split the components of the Lambda layer into five new Lambda layers. Zip the new layers, and attach the layers to the Lambda function. Update the function code to use the new layers.
D. Store the large pre-trained model in an Amazon S3 bucket. Use the AWS SDK to access the model.
Answer: A
Explanation:
Requirement Summary:
* NLP Lambda function with a large pre-trained model
* Lambda layer became 8.7 GB # Exceeds AWS limits
* Function returns RequestEntityTooLargeException
* Need: High-performing, portable, low initialization time
Important AWS Limits:
* Lambda Layers size limit (combined across all layers): 250 MB (unzipped)
* Deployment package size (unzipped): 250 MB
* Lambda container image support allows up to 10 GB image size
Evaluate Options:
A: Store model in S3 and load during execution
* # Leads to cold start latency every time
* Model loading from S3 is slower and not suitable for real-time NLP
* Not optimal for performance
B: Use EFS mounted to Lambda
* ## Valid for large models, but adds latency during cold start as model loads from EFS
* Requires EFS setup, VPC, and has added network I/O overhead
* Still slower than bundling in container image
C: Split into five Lambda layers
* # Still violates the total layer size limit of 250 MB (unzipped)
* You cannot exceed that even with multiple layers
# D: Use Docker container image
* # Allows bundling up to 10 GB of dependencies and models
* # High portability and performance
* # Avoids latency of downloading models at runtime
* # Ideal for scientific/NLP models
* Lambda container image support: https://docs.aws.amazon.com/lambda/latest/dg/images-create.html
* Lambda limits: https://docs.aws.amazon.com/lamb ... started-limits.html
* Using large models with Lambda: https://aws.amazon.com/blogs/machine-learning/deploying-large- machine-learning-models-on-aws-lambda-with-container-images/
NEW QUESTION # 296
What are the steps to using the AWS CLI to launch a templatized serverless application?
A. Use AWS CloudFormation package then CloudFormation deploy.
B. Use AWS CloudFormation validate-template then CloudFormation create-change-set.
C. Use AWS CloudFormation create-stack then CloudFormation update-stack.
D. Use AWS CloudFormation get-template then CloudFormation execute-change-set.
NEW QUESTION # 297
A developer is modifying an existing AWS Lambda function White checking the code the developer notices hardcoded parameter various for an Amazon RDS for SQL Server user name password database host and port. There also are hardcoded parameter values for an Amazon DynamoOB table. an Amazon S3 bucket, and an Amazon Simple Notification Service (Amazon SNS) topic.
The developer wants to securely store the parameter values outside the code m an encrypted format and wants to turn on rotation for the credentials. The developer also wants to be able to reuse the parameter values from other applications and to update the parameter values without modifying code.
Which solution will meet these requirements with the LEAST operational overhead?
A. Create an RDS database secret in AWS Secrets Manager. Set the user name password, database, host and port. Turn on secret rotation. Create encrypted Lambda environment variables for the DynamoDB table, S3 bucket and SNS topic.
B. Create RDS database parameters in AWS Systems Manager Parameter. Store for the user name password, database, host and port. Create encrypted Lambda environment variables for me DynamoDB table, S3 bucket, and SNS topic. Create a Lambda function and set the logic for the credentials rotation task Schedule the credentials rotation task in Amazon EventBridge.
C. Create an RDS database secret in AWS Secrets Manager. Set the user name password, database, host and port. Turn on secret rotation. Create Secure String parameters in AWS Systems Manager Parameter Store for the DynamoDB table, S3 bucket and SNS topic.
D. Create RDS database parameters in AWS Systems Manager Parameter. Store for the user name password database, host, and port. Store the DynamoDB table. S3 bucket, and SNS topic in Amazon S3 Create a Lambda function and set the logic for the credentials rotation Invoke the Lambda function on a schedule.
Answer: C
Explanation:
This solution will meet the requirements by using AWS Secrets Manager and AWS Systems Manager Parameter Store to securely store the parameter values outside the code in an encrypted format. AWS Secrets Manager is a service that helps protect secrets such as database credentials by encrypting them with AWS Key Management Service (AWS KMS) and enabling automatic rotation of secrets. The developer can create an RDS database secret in AWS Secrets Manager and set the user name, password, database, host, and port for accessing the RDS database. The developer can also turn on secret rotation, which will change the database credentials periodically according to a specified schedule or event. AWS Systems Manager Parameter Store is a service that provides secure and scalable storage for configuration data and secrets. The developer can create Secure String parameters in AWS Systems Manager Parameter Store for the DynamoDB table, S3 bucket, and SNS topic, which will encrypt them with AWS KMS. The developer can also reuse the parameter values from other applications and update them without modifying code. Option A is not optimal because it will create encrypted Lambda environment variables for the DynamoDB table, S3 bucket, and SNS topic, which may not be reusable or updatable without modifying code. Option C is not optimal because it will create RDS database parameters in AWS Systems Manager Parameter Store, which does not support automatic rotation of secrets. Option D is not optimal because it will store the DynamoDB table, S3 bucket, and SNS topic in Amazon S3, which may introduce additional costs and complexity for accessing configuration data.
References: AWS Secrets Manager, [AWS Systems Manager Parameter Store]
NEW QUESTION # 298
An application needs to encrypt data that is written to Amazon S3 where the keys are managed in an on- premises data center, and the encryption is handled by S3.
Which type of encryption should be used?
A. Use server-side encryption with Amazon S3-managed keys
B. Use server-side encryption with customer-provided keys
C. Use client-side encryption with customer master keys
D. Use server-side encryption with AWS KMS-managed keys
NEW QUESTION # 299
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