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>> DOP-C02的中関連問題 <<

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Amazon AWS Certified DevOps Engineer - Professional 認定 DOP-C02 試験問題 (Q18-Q23):

質問 # 18
A DevOps engineer needs to implement a CI/CD pipeline in an AWS account. The pipeline must consume sensitive database credentials that are stored in an AWS Systems Manager Parameter Store parameter. The Parameter Store parameter is in a separate central account. The DevOps engineer needs to create and integrate the parameter with the CI/CD account.
Which combination of steps will meet these requirements? (Select THREE.)

A. Use a customer managed AWS KMS key to encrypt the parameter. Grant decrypt permissions for the customer managed key to the AWS account that hosts the CI/CD pipeline.
B. Use an advanced tier Parameter Store parameter to store the database credentials in the central AWS account.
C. Use a standard tier Parameter Store parameter to store the database credentials in the central AWS account.
D. Use an AWS KMS managed key to encrypt the parameter. Grant decrypt permissions for the KMS key to the AWS account that hosts the CI/CD pipeline.
E. Create an IAM role in the AWS account that hosts the CI/CD pipeline. Add the full ARN of the parameter to the IAM policy that is associated with the IAM role.
F. Create an AWS Resource Access Manager (AWS RAM) resource share in the central AWS account. Share the parameter with the account that hosts the CI/CD pipeline.

正解：A、B、F

解説：
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The requirement is to securely consume cross-account sensitive parameters from AWS Systems Manager Parameter Store in a CI/CD pipeline. AWS imposes specific constraints and features for cross-account parameter access, and the correct solution must align with those constraints.
First, cross-account sharing of Parameter Store parameters is supported only for Advanced tier parameters. Standard tier parameters cannot be shared across accounts. Therefore, Option A is required, and Option C is invalid.
Second, cross-account access to Parameter Store parameters is implemented using AWS Resource Access Manager (AWS RAM). RAM allows the central account to explicitly share the parameter resource with the CI/CD account. Without RAM, the parameter is not visible or accessible across accounts. This makes Option F mandatory.
Third, because the parameter stores sensitive credentials, encryption must be handled securely. When sharing encrypted parameters across accounts, AWS requires the use of a customer managed AWS KMS key, not an AWS managed key. The key policy must explicitly grant `kms:Decrypt` permission to the consuming account. AWS managed keys cannot be shared across accounts, which makes Option D invalid and Option E correct.
Although the CI/CD pipeline's IAM role must ultimately have permission to read the parameter, that permission is implicit in the consuming account once the parameter is shared and the KMS key policy allows decryption. The critical cross-account enablers are Advanced tier, RAM sharing, and a customer managed KMS key.
Therefore, the correct combination is A, E, and F.

質問 # 19
A company has an application that runs on Amazon EC2 instances in an Auto Scaling group. The application processes a high volume of messages from an Amazon Simple Queue Service (Amazon SQS) queue.
A DevOps engineer noticed that the application took several hours to process a group of messages from the SQS queue. The average CPU utilization of the Auto Scaling group did not cross the threshold of a target tracking scaling policy when processing the messages. The application that processes the SQS queue publishes logs to Amazon CloudWatch Logs.
The DevOps engineer needs to ensure that the queue is processed quickly.
Which solution meets these requirements with the LEAST operational overhead?

A. Create an AWS Lambda function that logs the ApproximateNumberOfMessagesVisible attribute of the SQS queue to a CloudWatch Logs log group. Schedule an Amazon EventBridge rule to run the Lambda function every 5 minutes. Create a metric filter to count the number of log events from a CloudWatch logs group. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scale in and out.
B. Create an AWS Lambda function. Configure the Lambda function to publish a custom metric by using the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupIn-ServiceInstances Auto Scaling group attribute to publish the queue messages for each instance. Create a CloudWatch subscription filter for the application logs with the Lambda function as the target. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scale in and out.
C. Create an AWS Lambda function. Configure the Lambda function to publish a custom metric by using the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupIn-ServiceInstances Auto Scaling group attribute to publish the queue messages for each instance. Schedule an Amazon EventBridge rule to run the Lambda function every hour. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scale in and out.
D. Create a target tracking scaling policy for the Auto Scaling group. In the target tracking policy, use the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupIn-ServiceInstances Auto Scaling group attribute to calculate how many messages are in the queue for each number of instances by using metric math. Use the calculated attribute to scale in and out.

正解：D

解説：
Comprehensive and Detailed Explanation From Exact Extract:
The default CPU utilization metric does not reflect the processing backlog in the SQS queue, so the Auto Scaling group is not scaling properly to handle the workload.
To scale the Auto Scaling group based on queue length, you can create a target tracking scaling policy that uses a custom metric that combines the SQS queue's ApproximateNumberOfMessagesVisible and the number of instances (GroupIn-ServiceInstances) metric using CloudWatch metric math. This allows the scaling policy to calculate the average number of messages per instance and scale accordingly.
This approach requires no additional Lambda functions or log processing, thus minimizing operational overhead.
Option A and B require Lambda functions to publish custom metrics, which increases operational complexity. Option D also adds complexity with logging and metric filters.
Reference:
Scaling based on SQS queue length using metric math:
"You can create CloudWatch metric math expressions combining SQS and Auto Scaling group metrics to enable target tracking scaling policies that respond to queue backlog." (AWS Auto Scaling with SQS) Target Tracking Scaling Policies:
"Target tracking policies can use metric math expressions as a source to make scaling decisions." (AWS Auto Scaling Target Tracking)

質問 # 20
A company has deployed an application in a single AWS Region. The application backend uses Amazon DynamoDB tables and Amazon S3 buckets.
The company wants to deploy the application in a secondary Region. The company must ensure that the data in the DynamoDB tables and the S3 buckets persists across both Regions. The data must also immediately propagate across Regions.
Which solution will meet these requirements with the MOST operational efficiency?

A. Implement two-way S3 bucket replication between the primary Region's S3 buckets and the secondary Region's S3 buckets. Convert the DynamoDB tables into global tables. Set the secondary Region as the additional Region.
B. Implement S3 Batch Operations copy jobs between the primary Region and the secondary Region for all S3 buckets. Enable DynamoDB streams on the DynamoDB tables in both Regions. In each Region, create an AWS Lambda function that subscribes to the DynamoDB streams. Configure the Lambda function to copy new records to the DynamoDB tables in the other Region.
C. Implement two-way S3 bucket replication between the primary Region's S3 buckets and the secondary Region's S3 buckets. Enable DynamoDB streams on the DynamoDB tables in both Regions. In each Region, create an AWS Lambda function that subscribes to the DynamoDB streams. Configure the Lambda function to copy new records to the DynamoDB tables in the other Region.
D. Implement S3 Batch Operations copy jobs between the primary Region and the secondary Region for all S3 buckets. Convert the DynamoDB tables into global tables. Set the secondary Region as the additional Region.

正解：A

解説：
The company needs multi-Region data persistence with immediate propagation and minimal operational overhead. For S3, the correct mechanism is S3 replication (Cross-Region Replication or Same-Region Replication), which continuously and asynchronously replicates new objects as they are written. Configuring two-way replication between the primary and secondary Region buckets ensures that objects written in either Region are replicated to the other automatically without custom code.
For DynamoDB, the native solution for multi-Region replication is DynamoDB global tables. Global tables provide multi-master, multi-Region replication with low-latency reads and writes in each Region and automatic propagation of changes. Converting existing tables into global tables and adding the secondary Region as a replica gives immediate, managed cross-Region replication with minimal maintenance.
Option A combines these two fully managed features: two-way S3 replication and DynamoDB global tables.
This yields the highest operational efficiency.
Options B, C, and D rely on S3 Batch Operations or DynamoDB Streams + Lambda to manually copy data cross-Region. These approaches add complexity, custom code, and operational risk, and they are less suitable when AWS provides managed replication mechanisms specifically designed for this purpose.
Therefore, Option A is the correct and most efficient solution.

質問 # 21
A company has many AWS accounts. During AWS account creation the company uses automation to create an Amazon CloudWatch Logs log group in every AWS Region that the company operates in. The automaton configures new resources in the accounts to publish logs to the provisioned log groups in their Region.
The company has created a logging account to centralize the logging from all the other accounts. A DevOps engineer needs to aggregate the log groups from all the accounts to an existing Amazon S3 bucket in the logging account.
Which solution will meet these requirements in the MOST operationally efficient manner?

A. In the logging account create a CloudWatch Logs destination with a destination policy for each Region. For each new account subscribe the CloudWatch Logs log groups to the destination. Configure a single Amazon Kinesis data stream and a single Amazon Kinesis Data Firehose delivery stream to deliver the logs from all the CloudWatch Logs destinations to the S3 bucket.
B. In the logging account create a CloudWatch Logs destination with a destination policy for each Region. For each new account subscribe the CloudWatch Logs log groups to the destination Configure an Amazon Kinesis data stream and an Amazon Kinesis Data Firehose delivery stream for each Region to deliver the logs from the CloudWatch Logs destinations to the S3 bucket.
C. In the logging account create a CloudWatch Logs destination with a destination policy. For each new account subscribe the CloudWatch Logs log groups to the destination. Configure a single Amazon Kinesis data stream to deliver the logs from the CloudWatch Logs destination to the S3 bucket.
D. In the logging account create a CloudWatch Logs destination with a destination policy. For each new account subscribe the CloudWatch Logs log groups to the. Destination Configure a single Amazon Kinesis data stream and a single Amazon Kinesis Data Firehose delivery stream to deliver the logs from the CloudWatch Logs destination to the S3 bucket.

正解：B

解説：
This solution will meet the requirements in the most operationally efficient manner because it will use CloudWatch Logs destination to aggregate the log groups from all the accounts to a single S3 bucket in the logging account. However, unlike option A, this solution will create a CloudWatch Logs destination for each region, instead of a single destination for all regions. This will improve the performance and reliability of the log delivery, as it will avoid cross-region data transfer and latency issues. Moreover, this solution will use an Amazon Kinesis data stream and an Amazon Kinesis Data Firehose delivery stream for each region, instead of a single stream for all regions. This will also improve the scalability and throughput of the log delivery, as it will avoid bottlenecks and throttling issues that may occur with a single stream.

質問 # 22
A company has a single AWS account that runs hundreds of Amazon EC2 instances in a single AWS Region.
New EC2 instances are launched and terminated each hour in the account. The account also includes existing EC2 instances that have been running for longer than a week.
The company's security policy requires all running EC2 instances to use an EC2 instance profile. If an EC2 instance does not have an instance profile attached, the EC2 instance must use a default instance profile that has no IAM permissions assigned.
A DevOps engineer reviews the account and discovers EC2 instances that are running without an instance profile. During the review, the DevOps engineer also observes that new EC2 instances are being launched without an instance profile.
Which solution will ensure that an instance profile is attached to all existing and future EC2 instances in the Region?

A. Configure an Amazon EventBridge rule that reacts to EC2 StartInstances API calls. Configure the rule to invoke an AWS Systems Manager Automation runbook to attach the default instance profile to the EC2 instances.
B. Configure the iam-role-managed-policy-check AWS Config managed rule with a trigger type of configuration changes. Configure an automatic remediation action that invokes an AWS Lambda function to attach the default instance profile to the EC2 instances.
C. Configure the ec2-instance-profile-attached AWS Config managed rule with a trigger type of configuration changes. Configure an automatic remediation action that invokes an AWS Systems Manager Automation runbook to attach the default instance profile to the EC2 instances.
D. Configure an Amazon EventBridge rule that reacts to EC2 RunInstances API calls. Configure the rule to invoke an AWS Lambda function to attach the default instance profile to the EC2 instances.

正解：C

解説：
Explanation
https://docs.aws.amazon.com/config/latest/developerguide/ec2-instance-profile-attached.html

質問 # 23
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