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Amazon DVA-C02 Exam Syllabus Topics:

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

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최신 AWS Certified Associate DVA-C02 무료샘플문제 (Q808-Q813):

질문 # 808
A developer is debugging an issue with an application that is based on an AWS Lambda function.
The Lambda function intermittently fails during a 1-hour window. The developer needs to investigate the root cause of the intermittent failures.
The application sends logs to an Amazon CloudWatch Logs log group. The developer must be able to collect logs that relate to Lambda function failures. The developer must capture the dates and times at which the failures occurred.
Which solution will meet these requirements in the MOST operationally efficient way?

A. Use CloudWatch Insights to run a query on the log group that searches for specific patterns that match the error messages.
B. Export the CloudWatch Logs log group to an Amazon S3 bucket. Use Amazon Athena to run a SQL query against the bucket to search for the error messages.
C. Use the AWS CLI in AWS CloudShell to browse the CloudWatch Logs log group to search for the error messages.
D. Download log flies from the CloudWatch Logs log group to the developer's local machine. Use a local text search tool to search for the error messages.

정답：A

설명：
CloudWatch Logs Insights enables efficient, interactive querying of log data with minimal operational overhead. It allows filtering logs for error patterns and extracting timestamps within a specific time window, making it the most operationally efficient solution for debugging Lambda failures.

질문 # 809
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. Multi-site active-active
D. Warm standby

정답：B

설명：
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.

질문 # 810
A company wants to use AWS AppConfig to gradually deploy a new feature to 15% of users to test the feature before a full deployment.
Which solution will meet this requirement with the LEAST operational overhead?

A. Create an AWS AppConfig feature flag. Define a variant for the new feature, and create a rule to target
15% of users.
B. Set up a custom script within the application to randomly select 15% of users. Assign a flag for the new feature to the selected users.
C. Use AWS AppConfig to create a feature flag without variants. Implement a custom traffic splitting mechanism in the application code.
D. Create separate AWS AppConfig feature flags for both groups of users. Configure the flags to target
15% of users.

정답：A

설명：
Comprehensive Detailed and Lengthy Step-by-Step Explanation with All AWS Developer References:
1. Understanding the Use Case:
The company wants to gradually release a new feature to 15% of users to perform testing. AWS AppConfig is designed to manage and deploy configurations, including feature flags, allowing controlled rollouts.
2. Key AWS AppConfig Features:
* Feature Flags: Enable or disable features dynamically without redeploying code.
* Variants: Define different configurations for subsets of users.
* Targeting Rules: Specify rules for which users receive a particular variant.
3. Explanation of the Options:
* Option A:"Set up a custom script within the application to randomly select 15% of users. Assign a flag for the new feature to the selected users."While possible, this approach requires significant operational effort to manage user selection and ensure randomness. It does not leverage AWS AppConfig's built-in capabilities, which increases overhead.
* Option B:"Create separate AWS AppConfig feature flags for both groups of users. Configure the flags to target 15% of users."Creating multiple feature flags for different user groups complicates configuration management and does not optimize the use of AWS AppConfig.
* Option C:"Create an AWS AppConfig feature flag. Define a variant for the new feature, and create a rule to target 15% of users."This is the correct solution. Using AWS AppConfig feature flags with variants and targeting rules is the most efficient approach. It minimizes operational overhead by leveraging AWS AppConfig's built-in targeting and rollout capabilities.
* Option D:"Use AWS AppConfig to create a feature flag without variants. Implement a custom traffic splitting mechanism in the application code."This approach requires custom implementation within the application code, increasing complexity and operational effort.
4. Implementation Steps for Option C:
* Set Up AWS AppConfig:
* Open the AWS Systems Manager Console.
* Navigate to AppConfig.
* Create a Feature Flag:
* Define a new configuration for the feature flag.
* Add variants (e.g., "enabled" for the new feature and "disabled" for no change).
* Define a Targeting Rule:
* Use percentage-based targeting to define a rule that applies the "enabled" variant to 15% of users.
* Targeting rules can use attributes like user IDs or geographic locations.
* Deploy the Configuration:
* Deploy the configuration using a controlled rollout to ensure gradual exposure.
References:
* AWS AppConfig Documentation
* Feature Flags in AWS AppConfig
* AWS AppConfig Targeting Rules

질문 # 811
A developer is writing a web application that will run on AWS Lambda. The application will give users the ability to log in to view private documents. All pages in the application must be designed to match the company's branding. How can the developer host the sign-in pages with the LEAST amount of custom code?

A. Upload files for the sign-in pages with the required branding to an Amazon S3 bucket. Configure static website hosting for the S3 bucket.
B. Create a Lambda@Edge function to serve the sign-in pages with the required branding.
Configure Amazon CloudFront to invoke the function in response to user requests.
C. Create a Lambda function to serve the sign-in pages with the required branding. Configure Amazon API Gateway to route traffic to the function.
D. Configure an Amazon Cognito user pool with an Amazon Cognito hosted UI for the sign-in pages.Customize the pages with the required branding.

정답：D

설명：
Amazon Cognito user pools provide a fully managed hosted UI for user sign-in that requires minimal custom code. The hosted UI supports customization (such as logo, colors, and styling) so the sign-in experience can match the company's branding while avoiding building and maintaining custom authentication pages and flows.

질문 # 812
A developer is creating an AWS Lambda function that searches for items from an Amazon DynamoDB table that contains customer contact information. The DynamoDB table items have the customer's email_address as the partition key and additional properties such as customer_type, name and job_title.
The Lambda function runs whenever a user types a new character into the customer_type text input. The developer wants the search to return partial matches of all the email_address property of a particular customer_type. The developer does not want to recreate the DynamoDB table.
What should the developer do to meet these requirements?

A. Add a local secondary index (LSI) to the DynamoDB table with customer_type as the partition key and email_address as the sort key. Perform a query operation on the LSI by using the begins_with key condition expression with the email_address property.
B. Add a local secondary index (LSI) to the DynamoDB table with job_title as the partition key and email_address as the sort key. Perform a query operation on the LSI by using the begins_with key condition expression with the email_address property.
C. Add a global secondary index (GSI) to the DynamoDB table with customer_type as the partition key and email_address as the sort key. Perform a query operation on the GSI by using the begins_with key condition expression with the email_address property.
D. Add a global secondary index (GSI) to the DynamoDB table with email_address as the partition key and customer_type as the sort key. Perform a query operation on the GSI by using the begins_with key condition expression with the email_address property.

정답：C

설명：
By adding a global secondary index (GSI) to the DynamoDB table with customer_type as the partition key and email_address as the sort key, the developer can perform a query operation on the GSI using the Begins_with key condition expression with the email_address property. This will return partial matches of all email_address properties of a specific customer_type.

질문 # 813
......

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