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Amazon DVA-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Development with AWS Services	32%	- Develop code for AWS serverless applications 1. Amazon API Gateway 2. AWS Step Functions 3. AWS AppSync 4. Amazon EventBridge - Use data stores in application development 1. Amazon EFS 2. Amazon RDS 3. Amazon ElastiCache 4. Amazon DynamoDB 5. Amazon S3 - Develop code for applications hosted on AWS 1. Amazon API Gateway 2. Amazon Cognito 3. AWS CLI 4. Amazon SQS 5. AWS CloudFormation 6. AWS Lambda 7. Amazon SNS 8. Amazon DynamoDB 9. Amazon S3 10. AWS SDKs
Topic 2: Troubleshooting and Optimization	18%	- Assist in a root cause analysis 1. Amazon CloudWatch Logs 2. AWS X-Ray 3. AWS CloudTrail 4. Error handling and retries 5. Application logging - Optimize applications by using AWS services and features 1. Memory and compute optimization 2. Cost optimization 3. Data storage optimization 4. Caching strategies (ElastiCache, CloudFront) 5. Concurrency and throttling - Instrument code for observability 1. AWS X-Ray 2. Amazon CloudWatch alarms 3. Custom metrics 4. Amazon CloudWatch metrics 5. Structured logging
Topic 3: Security	26%	- Implement authentication and/or authorization for applications and AWS services 1. User pools and identity pools in Amazon Cognito 2. Resource-based policies, service policies, and principal policies 3. Role-based access control (RBAC) 4. Bearer tokens (JWT, OAuth, AWS STS) 5. Identity federation (SAML, OpenID Connect, Amazon Cognito) - Manage sensitive data in application code 1. Environment variables 2. AWS Secrets Manager 3. AWS Systems Manager Parameter Store 4. Encryption of data at rest and in transit - Implement encryption by using AWS services 1. AWS Certificate Manager 2. AWS Secrets Manager 3. AWS CloudHSM 4. Amazon S3 encryption 5. AWS KMS
Topic 4: Deployment	24%	- Deploy code by using AWS CI/CD services 1. AWS CodeBuild 2. Blue/green deployments 3. Canary deployments 4. AWS CodeDeploy 5. GitHub integration 6. Rolling deployments 7. AWS CodePipeline 8. AWS CodeCommit - Test applications in development environments 1. AWS SAM local 2. Mocking AWS services 3. Local testing of Lambda functions - Prepare application artifacts to be deployed to AWS 1. AWS CDK 2. AWS SAM 3. AWS CloudFormation 4. Lambda deployment packages 5. Docker containers - Automate deployment testing 1. AWS CodeBuild 2. AWS CodeDeploy 3. AWS CodePipeline 4. AWS CodeCommit

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Amazon AWS Certified Developer - Associate Sample Questions (Q21-Q26):

NEW QUESTION # 21
A developer wants to add request validation to a production environment Amazon API Gateway API. The developer needs to test the changes before the API is deployed to the production environment. For the test, the developer will send test requests to the API through a testing tool.
Which solution will meet these requirements with the LEAST operational overhead?

A. Export the existing API to an OpenAPI file. Create a new API. Import the OpenAPI file. Modify the new API to add request validation. Perform the tests. Modify the existing API to add request validation. Deploy the existing API to production.
B. Modify the existing API to add request validation. Deploy the updated API to a new API Gateway stage. Perform the tests. Deploy the updated API to the API Gateway production stage.
C. Clone the existing API. Modify the new API to add request validation. Perform the tests. Modify the existing API to add request validation. Deploy the existing API to production.
D. Create a new API. Add the necessary resources and methods, including new request validation.
Perform the tests. Modify the existing API to add request validation. Deploy the existing API to production

Answer: B

Explanation:
In this option, you are making changes directly to the existing API, adding request validation.
Then, you deploy the updated API to a new API Gateway stage, which allows you to test the changes without affecting the production environment. After performing the tests and ensuring everything works as expected, you can then deploy the updated API to the production stage, thus minimizing operational overhead.

NEW QUESTION # 22
A company is building an application for stock trading. The application needs sub-millisecond latency for processing trade requests. The company uses Amazon DynamoDB to store all the trading data that is used to process each trading request A development team performs load testing on the application and finds that the data retrieval time is higher than expected. The development team needs a solution that reduces the data retrieval time with the least possible effort.
Which solution meets these requirements'?

A. Add local secondary indexes (LSis) for the trading data.
B. Add retries with exponential back off for DynamoDB queries.
C. Use DynamoDB Accelerator (DAX) to cache the trading data.
D. Store the trading data m Amazon S3 and use S3 Transfer Acceleration.

Answer: C

Explanation:
Explanation
This solution will meet the requirements by using DynamoDB Accelerator (DAX), which is a fully managed, highly available, in-memory cache for DynamoDB that delivers up to a 10 times performance improvement - from milliseconds to microseconds - even at millions of requests per second. The developer can use DAX to cache the trading data that is used to process each trading request, which will reduce the data retrieval time with the least possible effort. Option A is not optimal because it will add local secondary indexes (LSIs) for the trading data, which may not improve the performance or reduce the latency of data retrieval, as LSIs are stored on the same partition as the base table and share the same provisioned throughput. Option B is not optimal because it will store the trading data in Amazon S3 and use S3 Transfer Acceleration, which is a feature that enables fast, easy, and secure transfers of files over long distances between S3 buckets and clients, not between DynamoDB and clients. Option C is not optimal because it will add retries with exponential backoff for DynamoDB queries, which is a strategy to handle transient errors by retrying failed requests with increasing delays, not by reducing data retrieval time.
References: [DynamoDB Accelerator (DAX)], [Local Secondary Indexes]

NEW QUESTION # 23
A company has a web application that is hosted on AWS. The application is behind an Amazon CloudFront distribution. A developer needs a dashboard to monitor error rates and anomalies of the CloudFront distribution as frequently as possible.
Which combination of steps should the developer take to meet these requirements? (Choose two.)

A. Set up Amazon Kinesis Data Streams to send the logs to Amazon OpenSearch Service by using an AWS Lambda function. Make a dashboard in OpenSearch Dashboards.
B. Stream the CloudFront distribution logs to an Amazon S3 bucket. Detect anomalies and error rates by using Amazon Athena.
C. Enable real-time logs on the CloudFront distribution. Create a data stream in Amazon Kinesis Data Streams.
D. Set up Amazon Kinesis Data Firehose to send the logs to AWS CloudTrail. Create CloudTrail metrics, alarms, and dashboards.
E. Stream the CloudFront distribution logs to Amazon Kinesis Data Firehose.

Answer: A,C

NEW QUESTION # 24
A developer supports an application that accesses data in an Amazon DynamoDB table. One of the item attributes is expirationDate in the timestamp format. The application uses this attribute to find items, archive them, and remove them from the table based on the timestamp value The application will be decommissioned soon, and the developer must find another way to implement this functionality. The developer needs a solution that will require the least amount of code to write.
Which solution will meet these requirements?

A. Create two AWS Lambda functions one to delete the items and one to process the items Create a DynamoDB stream Use the Deleteltem API operation to delete the items based on the expirationDate attribute Use the GetRecords API operation to get the items from the DynamoDB stream and process them
B. Enable TTL on the expirationDate attribute in the table Specify an Amazon Simple Queue Service (Amazon SQS> dead-letter queue as the target to delete the items Create an AWS Lambda function to process the items
C. Enable TTL on the expirationDate attribute in the table. Create a DynamoDB stream. Create an AWS Lambda function to process the deleted items. Create a DynamoDB trigger for the Lambda function.
D. Create two AWS Lambda functions, one to delete the items and one to process the items. Create an Amazon EventBndge scheduled rule to invoke the Lambda Functions Use the Deleteltem API operation to delete the items based on the expirationDate attribute. Use the GetRecords API operation to get the items from the DynamoDB table and process them.

Answer: C

Explanation:
* TTL for Automatic Deletion: DynamoDB's Time-to-Live effortlessly deletes expired items without manual intervention.
* DynamoDB Stream: Captures changes to the table, including deletions of expired items, triggering downstream actions.
* Lambda for Processing: A Lambda function connected to the stream provides custom logic for handling the deleted items.
* Code Efficiency: This solution leverages native DynamoDB features and stream-based processing, minimizing the need for custom code.
References:
* DynamoDB TTL Documentation: https://docs.aws.amazon.com/amazondynamodb/latest
/developerguide/TTL.html
* DynamoDB Streams Documentation: https://docs.aws.amazon.com/amazondynamodb/latest
/developerguide/Streams.html

NEW QUESTION # 25
A company hosts a stateless web application with low data storage in a single AWS Region. The company wants to increase the resiliency of the application to include a multi-Region presence. The company wants to set the recovery time objective (RTO) and recovery point objective (RPO) to hours. The company needs a low-cost and low-complexity disaster recovery (DR) strategy.
Which DR strategy should the company use?

A. Backup and restore
B. Pilot light
C. Warm standby
D. Multi-site active-active

Answer: B

Explanation:
* Why Option B is Correct:The pilot light strategy keeps a minimal version of the environment in another Region and scales up during a disaster. It achieves an RTO and RPO of hours at a low cost and complexity.
* Why Other Options are Incorrect:
* Option A: Warm standby is more expensive as it keeps a scaled-down, fully functioning version running in another Region.
* Option C: Backup and restore has a longer RTO and RPO than hours.
* Option D: Multi-site active-active is costly and more complex than required.
* AWS Documentation References:
* Disaster Recovery Strategies on AWS

NEW QUESTION # 26
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