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| Section | Weight | Objectives |
|---|
| Development with AWS Services | 32% | - Write code for applications hosted on AWS
- 1. Implement architectural patterns
- 2. Develop serverless applications
- 3. Use AWS SDKs and APIs
- Integrate AWS services into applications
- 1. Use AWS Lambda, API Gateway, Amazon EventBridge
- 2. Implement messaging and event-driven workflows
- Interact with AWS data services
- 1. Implement caching and data storage strategies
- 2. Work with Amazon DynamoDB, Amazon S3
|
| Security | 26% | - Secure application data
- 1. Manage secrets and credentials securely
- 2. Implement encryption at rest and in transit
- Implement authentication and authorization
- 1. Manage IAM roles, policies, and permissions
- 2. Use Amazon Cognito for identity management
- Apply security best practices
- 1. Follow AWS Well-Architected Framework security principles
- 2. Validate application security configurations
|
| Troubleshooting and Optimization | 18% | - Troubleshoot application issues
- 1. Analyze logs, metrics, and traces
- 2. Use Amazon CloudWatch, AWS X-Ray for monitoring
- Debug and resolve errors
- 1. Identify and fix code defects
- 2. Resolve integration and configuration problems
- Optimize application performance
- 1. Optimize resource usage and costs
- 2. Improve latency and throughput
|
| Deployment | 24% | - Prepare application artifacts for deployment
- 1. Package code and dependencies
- 2. Use infrastructure as code (AWS CloudFormation, AWS SAM)
- Deploy applications on AWS
- 1. Perform safe updates and rollbacks
- 2. Deploy to AWS Lambda, Amazon EC2, Elastic Beanstalk
- Implement CI/CD pipelines
- 1. Automate deployment and testing workflows
- 2. Use AWS CodePipeline, CodeBuild, CodeDeploy
|
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Amazon AWS Certified Developer - Associate Sample Questions (Q203-Q208):
NEW QUESTION # 203
A company needs to distribute firmware updates to its customers around the world.
Which service will allow easy and secure control of the access to the downloads at the lowest cost?
- A. Use Amazon CloudFront with AWS Lambda@Edge.
- B. Create a dedicated Amazon CloudFront Distribution for each customer.
- C. Use Amazon API Gateway and AWS Lambda to control access to an S3 bucket.
- D. Use Amazon CloudFront with signed URLs for Amazon S3.
Answer: D
Explanation:
https://aws.amazon.com/blogs/networking-and-content-delivery/amazon-s3-amazon-cloudfront-a- match-made-in-the-cloud/
NEW QUESTION # 204
A developer needs to add additional monitoring to an application that runs in an Amazon ECS cluster. The solution must publish and store custom metrics for the application. The developer must be able to save metadata about the custom metrics and store data with a granularity of 1 second. Which solution will meet these requirements?
- A. Monitor the containers by using Amazon ECS Exec. Use Amazon EventBridge Scheduler to run the ECS execute-command CLI command on the cluster to publish custom metrics on a schedule.
- B. Use the Amazon CloudWatch PutMetricData API to publish the custom metrics. Set the StorageResolution field to 1. Use the dimensions parameter to assign name value pairs to the metrics.
- C. Instrument the application to use AWS X-Ray by installing and running the X-Ray daemon. Use the X-Ray SDK to upload custom metrics and metadata through traces. Segment and subsegment data.
- D. Use Amazon ECS Container Insights to publish custom metrics from within the application.
Specify high resolution and use the metadata feature to store more data.
Answer: B
Explanation:
Amazon CloudWatch custom metrics support high-resolution data by using the PutMetricData API with StorageResolution set to 1, which stores metrics at 1-second granularity. Dimensions provide name-value metadata that helps categorize and filter the custom metrics for analysis and dashboards.
NEW QUESTION # 205
A developer is modifying an existing AWS Lambda function. While checking the code, the developer notices hardcoded parameter values for an Amazon RDS for SQL Server user name, password, database, host, and port. There are also hardcoded parameter values for an Amazon DynamoDB 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 in 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 SecureString parameters in AWS Systems Manager Parameter Store 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 the 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 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.
- D. 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.
Answer: A
Explanation:
https://docs.aws.amazon.com/systems-manager/latest/userguide/integration-ps- secretsmanager.html
NEW QUESTION # 206
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 Serveruser 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 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.
- D. 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.
Answer: D
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 SystemsManager 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.
NEW QUESTION # 207
A company runs a processing job every night to calculate customer loyalty scores. The job processes customer records sequentially. The job loads a customer's data from an Amazon DynamoDB table and runs a machine learning (ML) script to calculate a loyalty score for the customer. The job writes the output to a second DynamoDB table. The ML script takes 20 seconds to run.
A developer observes an increase in the job duration. Some output results are not available the morning after the job runs. The developer does not see errors in the logs for DynamoDB.
The developer needs to speed up the processing job.
Which solution will meet this requirement with the LEAST amount of development effort?
- A. Migrate the ML script to Amazon SageMaker AI. Ref actor the processing job to query the SageMaker AI endpoint.
- B. Refactor the processing job to read and write records by using the BatchGetItem and BatchWriteItem operations.
- C. Run multiple processing jobs in parallel. Use internal code logic to determine which records should be processed by each job.
- D. Provision read and write capacity units for both DynamoDB tables.
Answer: C
Explanation:
The primary bottleneck is the 20-second ML computation per customer, which makes sequential processing slow and increasingly unable to finish overnight. Running multiple jobs in parallel reduces total elapsed time by distributing customers across concurrent workers while leaving the existing DynamoDB access pattern largely unchanged. This achieves the speedup with minimal refactoring compared to redesigning the data access pattern or rebuilding the ML execution path.
NEW QUESTION # 208
......
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