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| Section | Weight | Objectives |
|---|
| Development with AWS Services | 32% | - Use data stores in application development
- 1. Amazon DynamoDB
- 2. Amazon EFS
- 3. Amazon S3
- 4. Amazon RDS
- 5. Amazon ElastiCache
- Develop code for AWS serverless applications
- 1. Amazon EventBridge
- 2. AWS Step Functions
- 3. Amazon API Gateway
- 4. AWS AppSync
- Develop code for applications hosted on AWS
- 1. Amazon Cognito
- 2. AWS CLI
- 3. Amazon DynamoDB
- 4. Amazon SNS
- 5. AWS CloudFormation
- 6. Amazon API Gateway
- 7. Amazon S3
- 8. AWS SDKs
- 9. Amazon SQS
- 10. AWS Lambda
|
| Security | 26% | - Manage sensitive data in application code
- 1. AWS Systems Manager Parameter Store
- 2. Environment variables
- 3. Encryption of data at rest and in transit
- 4. AWS Secrets Manager
- Implement encryption by using AWS services
- 1. Amazon S3 encryption
- 2. AWS CloudHSM
- 3. AWS Certificate Manager
- 4. AWS KMS
- 5. AWS Secrets Manager
- Implement authentication and/or authorization for applications and AWS services
- 1. Bearer tokens (JWT, OAuth, AWS STS)
- 2. User pools and identity pools in Amazon Cognito
- 3. Resource-based policies, service policies, and principal policies
- 4. Identity federation (SAML, OpenID Connect, Amazon Cognito)
- 5. Role-based access control (RBAC)
|
| Troubleshooting and Optimization | 18% | - Instrument code for observability
- 1. Amazon CloudWatch alarms
- 2. Custom metrics
- 3. AWS X-Ray
- 4. Structured logging
- 5. Amazon CloudWatch metrics
- Optimize applications by using AWS services and features
- 1. Caching strategies (ElastiCache, CloudFront)
- 2. Data storage optimization
- 3. Cost optimization
- 4. Memory and compute optimization
- 5. Concurrency and throttling
- Assist in a root cause analysis
- 1. Error handling and retries
- 2. AWS CloudTrail
- 3. Amazon CloudWatch Logs
- 4. AWS X-Ray
- 5. Application logging
|
| Deployment | 24% | - Prepare application artifacts to be deployed to AWS
- 1. AWS SAM
- 2. Docker containers
- 3. AWS CloudFormation
- 4. AWS CDK
- 5. Lambda deployment packages
- Deploy code by using AWS CI/CD services
- 1. AWS CodeBuild
- 2. AWS CodePipeline
- 3. GitHub integration
- 4. Canary deployments
- 5. AWS CodeDeploy
- 6. Blue/green deployments
- 7. AWS CodeCommit
- 8. Rolling deployments
- Test applications in development environments
- 1. AWS SAM local
- 2. Mocking AWS services
- 3. Local testing of Lambda functions
- Automate deployment testing
- 1. AWS CodeBuild
- 2. AWS CodePipeline
- 3. AWS CodeDeploy
- 4. AWS CodeCommit
|
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Amazon AWS Certified Developer - Associate Sample Questions (Q658-Q663):
NEW QUESTION # 658
A developer is creating a script to automate the deployment process for a serverless application.
The developer wants to use an existing AWS Serverless Application Model (AWS SAM) template for the application.
What should the developer use for the project? (Choose two.)
- A. Call sam package to create the deployment package. Call sam deploy to deploy the package afterward.
- B. Create a ZIP package and upload it to Amazon S3. Call aws cloudformation create-stack to create the application.
- C. Call aws cloudformation package to create the deployment package. Call aws cloudformation deploy to deploy the package afterward.
- D. Create a ZIP package locally and call aws serverlessrepo create-applicatiion to create the application.
- E. Call aws s3 cp to upload the AWS SAM template to Amazon S3. Call aws lambda update- function-code to create the application.
Answer: A,C
NEW QUESTION # 659
A company has an analytics application that uses an AWS Lambda function to process transaction data asynchronously. A developer notices that asynchronous invocations of the Lambda function sometimes fail. When failed Lambda function invocations occur, the developer wants to invoke a second Lambda function to handle errors and log details.
Which solution will meet these requirements?
- A. Configure a Lambda function trigger with a failure condition. Specify Lambda function as the destination type. Specify the error-handling Lambda function's Amazon Resource Name (ARN) as the resource.
- B. Configure a Lambda function destination with a failure condition. Specify Lambda function as the destination type. Specify the error-handling Lambda function's Amazon Resource Name (ARN) as the resource.
- C. Enable AWS X-Ray active tracing on the initial Lambda function. Configure X-Ray to capture stack traces of the failed invocations. Invoke the error-handling Lambda function by including the stack traces in the event object.
- D. Create a status check alarm on the initial Lambda function. Configure the alarm to invoke the error-handling Lambda function when the alarm is initiated. Ensure that the alarm passes the stack trace in the event object.
Answer: D
NEW QUESTION # 660
A developer is using AWS Step Functions to automate a workflow The workflow defines each step as an AWS Lambda function task The developer notices that runs of the Step Functions state machine fail in the GetResource task with either an UlegalArgumentException error or a TooManyRequestsException error The developer wants the state machine to stop running when the state machine encounters an UlegalArgumentException error. The state machine needs to retry the GetResource task one additional time after 10 seconds if the state machine encounters a TooManyRequestsException error. If the second attempt fails, the developer wants the state machine to stop running.
How can the developer implement the Lambda retry functionality without adding unnecessary complexity to the state machine'?
- A. Duplicate the GetResource task Rename the new GetResource task to TryAgain Add a catcher to the original GetResource task Configure the catcher with an error type of TooManyRequestsException.
Configure the next step to be TryAgain. - B. Add a catcher to the GetResource task Configure the catcher with an error type of TooManyRequestsException. an interval of 10 seconds, and a maximum attempts value of 1. Configure the next step to be the GetResource task.
- C. Add a retrier to the GetResource task Configure the retrier with an error type of TooManyRequestsException, an interval of 10 seconds, and a maximum attempts value of 1.
- D. Add a Delay task after the GetResource task. Add a catcher to the GetResource task. Configure the catcher with an error type of TooManyRequestsException. Configure the next step to be the Delay task Configure the Delay task to wait for an interval of 10 seconds Configure the next step to be the GetResource task.
Answer: C
Explanation:
* Step Functions Retriers: Retriers provide a built-in way to gracefully handle transient errors within State Machines. Here's how to use them:
* Directly attach a retrier to the problematic 'GetResource' task.
* Configure the retrier:
* ErrorEquals: Set this to ['TooManyRequestsException'] to target the specific error.
* IntervalSeconds: Set to 10 for the desired retry delay.
* MaxAttempts: Set to 1, as you want only one retry attempt.
* Error Handling:
* Upon 'TooManyRequestsException', the retrier triggers the task again after 10 seconds.
* On a second failure, Step Functions moves to the next state or fails the workflow, as per your design.
* 'IllegalArgumentException' causes error propagation as intended.
References:
* Error Handling in Step Functions: https://docs.aws.amazon.com/step-functions/latest/dg/concepts- error-handling.html
NEW QUESTION # 661
A developer is receiving an intermittent ProvisionedThroughputExceededException error from an application that is based on Amazon DynamoDB. According to the Amazon CloudWatch metrics for the table, the application is not exceeding the provisioned throughput. What could be the cause of the issue?
- A. The application is exceeding capacity on a particular sort key.
- B. The DynamoDB table is exceeding the provisioned scaling operations.
- C. The DynamoDB table storage size is larger than the provisioned size.
- D. The application is exceeding capacity on a particular hash key.
Answer: D
Explanation:
DynamoDB distributes throughput across partitions based on the hash key. A hot partition (caused by high usage of a specific hash key) can result in a ProvisionedThroughputExceededException, even if overall usage is below the provisioned capacity.
* Why Option B is Correct:
* Partition-Level Limits: Each partition has a limit of 3,000 read capacity units or 1,000 write capacity units per second.
* Hot Partition: Excessive use of a single hash key can overwhelm its partition.
* Why Not Other Options:
* Option A: DynamoDB storage size does not affect throughput.
* Option C: Provisioned scaling operations are unrelated to throughput errors.
* Option D: Sort keys do not impact partition-level throughput.
DynamoDB Partition Key Design Best Practices
NEW QUESTION # 662
A company runs a web application on Amazon EC2 instances behind an Application Load Balancer. The application uses Amazon DynamoDB as its database. The company wants to ensure high performance for reads and writes.
Which solution will meet these requirements MOST cost-effectively?
- A. Use DynamoDB on-demand capacity mode for the table. Specify a maximum throughput higher than the expected peak read and write capacity units.
- B. Create an Amazon DynamoDB Accelerator (DAX) cluster. Configure the application to use the DAX endpoint.
- C. Use DynamoDB provisioned throughput mode for the table. Create an Amazon CloudWatch alarm on the ThrottledRequests metric. Invoke an AWS Lambda function to increase provisioned capacity.
- D. Configure auto-scaling for the DynamoDB table with a target utilization of 70%. Set the minimum and maximum capacity units based on the expected workload.
Answer: D
Explanation:
Comprehensive Detailed Explanation with all AWS Reference
Why Option A is Correct:
Auto-scaling with a target utilization ensures the DynamoDB table dynamically adjusts capacity based on workload, maintaining high performance while optimizing cost. Setting a reasonable target utilization minimizes overprovisioning and throttling risks.
Why Other Options are Incorrect:
Option B: On-demand capacity is costlier than provisioned throughput for predictable workloads.
Option C: Using manual CloudWatch alarms and Lambda for scaling is less efficient and adds operational overhead.
Option D: DAX accelerates read performance but does not improve write performance.
AWS Documentation Reference:
DynamoDB Auto Scaling
NEW QUESTION # 663
......
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