DVA-C02 PDF題庫 -新版DVA-C02考古題

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Amazon DVA-C02 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Topic 1: Troubleshooting and Optimization | 18% | - Debug and resolve errors
- 1. Identify and fix code defects
- 2. Resolve integration and configuration problems
- Troubleshoot application issues
- 1. Analyze logs, metrics, and traces
- 2. Use Amazon CloudWatch, AWS X-Ray for monitoring
- Optimize application performance
- 1. Optimize resource usage and costs
- 2. Improve latency and throughput
|
| Topic 2: Development with AWS Services | 32% | - Write code for applications hosted on AWS
- 1. Implement architectural patterns
- 2. Use AWS SDKs and APIs
- 3. Develop serverless applications
- Interact with AWS data services
- 1. Implement caching and data storage strategies
- 2. Work with Amazon DynamoDB, Amazon S3
- Integrate AWS services into applications
- 1. Use AWS Lambda, API Gateway, Amazon EventBridge
- 2. Implement messaging and event-driven workflows
|
| Topic 3: Security | 26% | - Apply security best practices
- 1. Validate application security configurations
- 2. Follow AWS Well-Architected Framework security principles
- Secure application data
- 1. Implement encryption at rest and in transit
- 2. Manage secrets and credentials securely
- Implement authentication and authorization
- 1. Manage IAM roles, policies, and permissions
- 2. Use Amazon Cognito for identity management
|
| Topic 4: Deployment | 24% | - Prepare application artifacts for deployment
- 1. Use infrastructure as code (AWS CloudFormation, AWS SAM)
- 2. Package code and dependencies
- Implement CI/CD pipelines
- 1. Use AWS CodePipeline, CodeBuild, CodeDeploy
- 2. Automate deployment and testing workflows
- Deploy applications on AWS
- 1. Deploy to AWS Lambda, Amazon EC2, Elastic Beanstalk
- 2. Perform safe updates and rollbacks
|
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最新的 AWS Certified Associate DVA-C02 免費考試真題 (Q426-Q431):
問題 #426
A company's developer has deployed an application in AWS by using AWS CloudFormation The CloudFormation stack includes parameters in AWS Systems Manager Parameter Store that the application uses as configuration settings. The application can modify the parameter values When the developer updated the stack to create additional resources with tags, the developer noted that the parameter values were reset and that the values ignored the latest changes made by the application. The developer needs to change the way the company deploys the CloudFormation stack. The developer also needs to avoid resetting the parameter values outside the stack.
Which solution will meet these requirements with the LEAST development effort?
- A. Modify the CloudFormation stack policy to deny updates on Parameter Store parameters
- B. Create an Amazon DynamoDB table as a resource in the CloudFormation stack to hold configuration data for the application Migrate the parameters that the application is modifying from Parameter Store to the DynamoDB table
- C. Create an Amazon RDS DB instance as a resource in the CloudFormation stack. Create a table in the database for parameter configuration. Migrate the parameters that the application is modifying from Parameter Store to the configuration table
- D. Modify the CloudFormation stack to set the deletion policy to Retain for the Parameter Store parameters.
答案:A
解題說明:
Explanation
https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/protect-stack-resources.html#stack-policy-
問題 #427
A company has deployed an application on AWS Elastic Beanstalk. The company has configured the Auto Scaling group that is associated with the Elastic Beanstalk environment to have five Amazon EC2 instances. If the capacity is fewer than four EC2 instances during the deployment, application performance degrades. The company is using the all-at-once deployment policy.
What is the MOST cost-effective way to solve the deployment issue?
- A. Change the deployment policy to rolling. Specify a batch size of 2.
- B. Change the deployment policy to traffic splitting. Specify an evaluation time of 1 hour.
- C. Change the deployment policy to rolling with additional batch. Specify a batch size of 1.
- D. Change the Auto Scaling group to six desired instances.
答案:C
解題說明:
This solution will solve the deployment issue by deploying the new version of the application to one new EC2 instance at a time, while keeping the old version running on the existing instances. This way, there will always be at least four instances serving traffic during the deployment, and no downtime or performance degradation will occur. Option A is not optimal because it will increase the cost of running the Elastic Beanstalk environment without solving the deployment issue. Option B is not optimal because it will split the traffic between two versions of the application, which may cause inconsistency and confusion for the customers. Option D is not optimal because it will deploy the new version of the application to two existing instances at a time, which may reduce the capacity below four instances during the deployment.
問題 #428
A developer is working on an ecommerce application that stores data in an Amazon RDS for MySQL cluster. The developer needs to implement a caching layer for the application to retrieve information about the most viewed products.
Which solution will meet these requirements?
- A. Create an Amazon ElastiCache for Redis cluster. Update the application code to use the ElastiCache for Redis cluster endpoint.
- B. Edit the RDS for MySQL cluster by adding a cache node. Configure the cache endpoint instead of the cluster endpoint in the application.
- C. Configure the RDS for MySQL cluster to add a standby instance in a different Availability Zone.Configure the application to read the data from the standby instance.
- D. Create an Amazon DynamoDB Accelerator (DAX) cluster in front of the RDS for MySQL cluster.
Configure the application to connect to the DAX endpoint instead of the RDS endpoint.
答案:A
問題 #429
A developer is re-architecting a caching solution that currently uses an Amazon ElastiCache (Redis OSS) cluster that has cluster mode enabled to store product information. The existing solution experiences significant load. All cached product data expires at the same time, which results in additional pressure on the backend database and poor performance for end users. The developer must resolve the performance issues in a way that maintains data freshness.
Which solution will meet these requirements?
- A. Increase the number of replica nodes. Disable cluster mode.
- B. Increase the number of shards. Decrease the number of replica nodes in the cluster.
- C. Increase the TTL of the product data in the cache.
- D. Add a slight variance to the TTL setting by using a randomly generated time value.
答案:D
解題說明:
The described behavior is a classic cache stampede (also called the "thundering herd" problem): a large set of hot keys share the same expiration time, so they all expire simultaneously. When that happens, many requests miss the cache at the same moment and fall back to the backend database, creating a sudden spike in database load and causing increased latency and poor user experience.
The most effective fix that preserves freshness is to introduce jitter-a small, randomized variance-into the TTL values so cached objects do not all expire at the same time. With TTL jitter, each item still has a bounded lifetime (so freshness is maintained), but expirations become spread out over time. That smooths cache-miss rates and prevents synchronized regeneration, reducing bursts of database traffic. This is a common best practice in high-load caching systems precisely to avoid mass expiration events.
Option A (increase TTL) might reduce how frequently a stampede occurs, but it does not solve the root cause:
keys can still align and expire together, and a longer TTL can also reduce freshness if product data changes.
Option B (more replicas, disable cluster mode) changes topology and read scaling, but it does not prevent synchronized expiry or the resulting backend spike; disabling cluster mode could also reduce scale for large keyspaces. Option D (more shards) can improve cache throughput capacity, but it does not address the core issue that backend load spikes occur when items expire simultaneously; lowering replica count could even reduce read availability.
Therefore, C is the correct solution: keep TTL-based freshness, but add a randomized component (for example, TTL = baseTTL + random(0..jitter)) so expirations are distributed and the backend database is protected from sudden bursts.
問題 #430
A developer created reusable code that several AWS Lambda functions need to use. The developer bundled the code into a zip archive. The developer needs to deploy the code to AWS and update the Lambda functions to use the code.
Which solution will meet this requirement in the MOST operationally efficient way?
- A. Create a new Lambda function that contains and runs the shared code. Update the existing Lambda functions to invoke the new Lambda function synchronously.
- B. Upload the zip archive to Amazon S3. Configure an import path on the Lambda functions to point to the zip archive.
- C. Create a Lambda layer that contains the zip archive. Attach the Lambda layer to the Lambda functions.
- D. Create a Lambda container image that includes the shared code. Use the container image as a Lambda base image for all the functions.
答案:C
問題 #431
......
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