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Amazon AWS Certified Developer - Associate 認定 DVA-C02 試験問題 (Q487-Q492):

質問 # 487
A company is expanding the compatibility of its photo-snaring mobile app to hundreds of additional devices with unique screen dimensions and resolutions. Photos are stored in Amazon S3 in their original format and resolution. The company uses an Amazon CloudFront distribution to serve the photos The app includes the dimension and resolution of the display as GET parameters with every request.
A developer needs to implement a solution that optimizes the photos that are served to each device to reduce load time and increase photo quality.
Which solution will meet these requirements MOST cost-effective?

A. Use S3 Batch Operations to invoke an AWS Lambda function to create new variants of the photos with the required dimensions and resolutions. Create a dynamic CloudFront origin that automatically maps the request of each device to the corresponding photo variant.
B. Create a Lambda@Edge function that optimizes the photos upon request and returns the photos as a response. In the same function store a copy of the processed photos on Amazon S3 for subsequent requests.
C. Use S3 Batch Operations to invoke an AWS Lambda function to create new variants of the photos with the required dimensions and resolutions. Create a Lambda@Edge function to route requests to the corresponding photo vacant by using request headers.
D. Create a Lambda@Edge function that optimizes the photos upon request and returns the photos as a response. Change the CloudFront TTL cache policy to the maximum value possible.

正解：B

解説：
This solution meets the requirements most cost-effectively because it optimizes the photos on demand and caches them for future requests. Lambda@Edge allows the developer to run Lambda functions at AWS locations closer to viewers, which can reduce latency and improve photo quality. The developer can create a Lambda@Edge function that uses the GET parameters from each request to optimize the photos with the required dimensions and resolutions and returns them as a response. The function can also store a copy of the processed photos on Amazon S3 for subsequent requests, which can reduce processing time and costs. Using S3 Batch Operations to create new variants of the photos will incur additional storage costs and may not cover all possible dimensions and resolutions. Creating a dynamic CloudFront origin or a Lambda@Edge function to route requests to corresponding photo variants will require maintaining a mapping of device types and photo variants, which can be complex and error-prone.
Reference: [Lambda@Edge Overview], [Resizing Images with Amazon CloudFront & Lambda@Edge]

質問 # 488
A company runs a payment application on Amazon EC2 instances behind an Application Load Balance. The EC2 instances run in an Auto Scaling group across multiple Availability Zones. The application needs to retrieve application secrets during the application startup and export the secrets as environment variables. These secrets must be encrypted at rest and need to be rotated every month.
Which solution will meet these requirements with the LEAST development effort?

A. Save the secrets as strings in AWS Systems Manager Parameter Store and use the default AWS Key Management Service (AWS KMS) key. Configure an Amazon EC2 user data script to retrieve the secrets during the startup and export as environment variables. Configure an AWS Lambda function to rotate the secrets in Parameter Store every month.
B. Save the secrets in a text file and store the text file in Amazon S3. Provision a customer managed key. Use the key for secret encryption in Amazon S3. Read the contents of the text file and read the export as environment variables. Configure S3 Object Lambda to rotate the text file every month.
C. Store the secrets in AWS Secrets Manager. Provision a new customer master key. Use the key to encrypt the secrets. Enable automatic rotation. Configure an Amazon EC2 user data script to programmatically retrieve the secrets during the startup and export as environment variables.
D. Save the secrets as base64 encoded environment variables in the application properties.
Retrieve the secrets during the application startup. Reference the secrets in the application code.
Write a script to rotate the secrets saved as environment variables.

正解：C

質問 # 489
A company has an application that consists of different microservices that run inside an AWS account.
The microservices are running in containers inside a single VPC. The number of microservices is constantly increasing. A developer must create a central logging solution for application logs.

A. Create an AWS CloudTrail trail to log all the API calls.
B. Use AWS Cloud Map to map the interactions of the microservices.
C. Configure VPC Flow Logs to track the communications between the microservices.
D. Create a different Amazon CloudWatch Logs stream for each microservice.

正解：D

解説：
To create a central logging solution for microservices, using Amazon CloudWatch Logs is a recommended and effective approach. Here's why:
* Amazon CloudWatch Logs Streams allow you to centralize logs from different services, which is crucial as the number of microservices increases.
* Each microservice can have its own dedicated log stream within Amazon CloudWatch Logs, providing clear segregation of logs while still allowing centralized management.
* This setup enables developers to monitor, search, and analyze logs efficiently using tools like CloudWatch Insights.
* Other options like CloudTrail (B) are designed for API activity monitoring, not application logs. VPC Flow Logs (C) focus on network traffic rather than application behavior. AWS Cloud Map (D) is for service discovery and routing, not logging.

質問 # 490
A gaming application stores scores for players in an Amazon DynamoDB table that has four attributes:
user_id, user_name, user_score, and user_rank. The users are allowed to update their names only. A user is authenticated by web identity federation.
Which set of conditions should be added in the policy attached to the role for the dynamodb:PutItem API call?

A. "Condition": {"ForAllValues:StringEquals": {"dynamodb:LeadingKeys": ["${www.amazon.com:
user_id}"],"dynamodb:Attributes": ["user_name"]}}
B. "Condition": {"ForAllValues:StringEquals": {"dynamodb:LeadingKeys": ["${www.amazon.com:user_name}"],"dynamodb:Attributes": ["username", "userid"]}}
C. "Condition": {"ForAllValues:StringEquals": {"dynamodb:LeadingKeys": ["${www.amazon.com:
user_id}"],"dynamodb:Attributes": ["user_name", "user_id"]}}
D. "Condition": {"ForAllValues:StringEquals": {"dynamodb:LeadingKeys": ["${www.amazon.com:
user_name}"],"dynamodb:Attributes": ["user_id"]}}

正解：A

解説：
The correct policy condition ensures that:
The LeadingKeys condition restricts operations to the authenticated user's user_id.
The Attributes condition limits the updatable attributes to user_name.
Explanation of Choices:
Option A: Correctly enforces both the key restriction (dynamodb:LeadingKeys) and ensures only the user_name attribute can be updated.
Option B, C, D: Use incorrect conditions, such as referencing user_name in the LeadingKeys or including other attributes like user_id in updatable fields.
Reference:AWS DynamoDB Condition Keys Documentation

質問 # 491
A company is migrating its PostgreSQL database into the AWS Cloud. The company wants to use a database that will secure and regularly rotate database credentials. The company wants a solution that does not require additional programming overhead.
Which solution will meet these requirements?

A. Use Amazon Aurora PostgreSQL tor the database. Store the database credentials in AWS Systems Manager Parameter Store Turn on rotation.
B. Use Amazon DynamoDB for the database. Store the database credentials in AWS Secrets Manager Turn on rotation.
C. Use Amazon Aurora PostgreSQL for the database. Store the database credentials in AWS Secrets Manager Turn on rotation.
D. Use Amazon DynamoDB for the database. Store the database credentials in AWS Systems Manager Parameter Store Turn on rotation.

正解：C

解説：
This solution meets the requirements because it uses a PostgreSQL-compatible database that can secure and regularly rotate database credentials without requiring additional programming overhead. Amazon Aurora PostgreSQL is a relational database service that is compatible with PostgreSQL and offers high performance, availability, and scalability. AWS Secrets Manager is a service that helps you protect secrets needed to access your applications, services, and IT resources. You can store database credentials in AWS Secrets Manager and use them to access your Aurora PostgreSQL database. You can also enable automatic rotation of your secrets according to a schedule or an event. AWS Secrets Manager handles the complexity of rotating secrets for you, such as generating new passwords and updating your database with the new credentials. Using Amazon DynamoDB for the database will not meet the requirements because it is a NoSQL database that is not compatible with PostgreSQL. Using AWS Systems Manager Parameter Store for storing and rotating database credentials will require additional programming overhead to integrate with your database.

質問 # 492
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