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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q148-Q153):

NEW QUESTION # 148
A company is using AWS Organizations to centrally manage its AWS accounts. The company has turned on AWS Config in each member account by using AWS Cloud Formation StackSets The company has configured trusted access in Organizations for AWS Config and has configured a member account as a delegated administrator account for AWS Config A DevOps engineer needs to implement a new security policy The policy must require all current and future AWS member accounts to use a common baseline of AWS Config rules that contain remediation actions that are managed from a central account Non-administrator users who can access member accounts must not be able to modify this common baseline of AWS Config rules that are deployed into each member account Which solution will meet these requirements?

A. Create a CloudFormation template that contains the AWS Config rules and remediation actions. Deploy the template from the Organizations management account by using CloudFormation StackSets.
B. Create an AWS Config conformance pack that contains the AWS Config rules and remediation actions Deploy the pack from the Organizations management account by using CloudFormation StackSets.
C. Create an AWS Config conformance pack that contains the AWS Config rules and remediation actions.Deploy the pack from the delegated administrator account by using AWS Config.
D. Create a CloudFormation template that contains the AWS Config rules and remediation actions Deploy the template from the delegated administrator account by using AWS Config.

Answer: C

Explanation:
The correct answer is D. Creating an AWS Config conformance pack that contains the AWS Config rules and remediation actions and deploying it from the delegated administrator account by using AWS Config will meet the requirements. A conformance pack is a collection of AWS Config rules and remediation actions that can be easily deployed as a single entity in an account and a region or across an organization in AWS Organizations1. By using the delegated administrator account, the DevOps engineer can centrally manage the conformance pack and prevent non-administrator users from modifying it in the member accounts. Option A is incorrect because creating a CloudFormation template that contains the AWS Config rules and remediation actions and deploying it from the Organizations management account by using CloudFormation StackSets will not prevent non-administrator users from modifying the AWS Config rules in the member accounts.
Option B is incorrect because deploying the conformance pack from the Organizations management account by using CloudFormation StackSets will not use the trusted access feature of AWS Config and will require additional permissions and resources. Option C is incorrect because creating a CloudFormation template that contains the AWS Config rules and remediation actions and deploying it from the delegated administrator account by using AWS Config will not leverage the benefits of conformance packs, such as simplified deployment and management. References:
* Conformance Packs - AWS Config
* Certified DevOps Engineer - Professional (DOP-C02) Study Guide (page 176)

NEW QUESTION # 149
A company's application uses a fleet of Amazon EC2 On-Demand Instances to analyze and process data. The EC2 instances are in an Auto Scaling group. The Auto Scaling group is a target group for an Application Load Balancer (ALB). The application analyzes critical data that cannot tolerate interruption. The application also analyzes noncritical data that can withstand interruption.
The critical data analysis requires quick scalability in response to real-time application demand. The noncritical data analysis involves memory consumption. A DevOps engineer must implement a solution that reduces scale-out latency for the critical data. The solution also must process the noncritical data.
Which combination of steps will meet these requirements? (Select TWO.)

A. For the critical data, modify the existing Auto Scaling group. Create a warm pool instance in the stopped state. Define the warm pool size. Create a new version of the launch template that has detailed monitoring enabled. Use On-Demand Instances.
B. For the critical data. modify the existing Auto Scaling group. Create a lifecycle hook to ensure that bootstrap scripts are completed successfully. Ensure that the application on the instances is ready to accept traffic before the instances are registered. Create a new version of the launch template that has detailed monitoring enabled.
C. For the noncritical data, create a second Auto Scaling group. Choose the predefined memory utilization metric type for the target tracking scaling policy. Use Spot Instances. Add the new Auto Scaling group as the target group for the ALB. Modify the application to use two target groups for critical data and noncritical data.
D. For the noncritical data, create a second Auto Scaling group that uses a launch template. Configure the launch template to install the unified Amazon CloudWatch agent and to configure the CloudWatch agent with a custom memory utilization metric. Use Spot Instances. Add the new Auto Scaling group as the target group for the ALB. Modify the application to use two target groups for critical data and noncritical data.
E. For the critical data, modify the existing Auto Scaling group. Create a warm pool instance in the stopped state. Define the warm pool size. Create a new version of the launch template that has detailed monitoring enabled. use Spot Instances.

Answer: A,D

Explanation:
Explanation
For the critical data, using a warm pool1 can reduce the scale-out latency by having pre-initialized EC2 instances ready to serve the application traffic. Using On-Demand Instances can ensure that the instances are always available and not interrupted by Spot interruptions2.
For the noncritical data, using a second Auto Scaling group with Spot Instances can reduce the cost and leverage the unused capacity of EC23. Using a launch template with the CloudWatch agent4 can enable the collection of memory utilization metrics, which can be used to scale the group based on the memory demand. Adding the second group as a target group for the ALB and modifying the application to use two target groups can enable routing the traffic based on the data type.
References: 1: Warm pools for Amazon EC2 Auto Scaling 2: Amazon EC2 On-Demand Capacity Reservations 3: Amazon EC2 Spot Instances 4: Metrics collected by the CloudWatch agent

NEW QUESTION # 150
A company uses AWS Secrets Manager to store a set of sensitive API keys that an AWS Lambda function uses. When the Lambda function is invoked, the Lambda function retrieves the API keys and makes an API call to an external service. The Secrets Manager secret is encrypted with the default AWS Key Management Service (AWS KMS) key.
A DevOps engineer needs to update the infrastructure to ensure that only the Lambda function's execution role can access the values in Secrets Manager. The solution must apply the principle of least privilege.
Which combination of steps will meet these requirements? (Select TWO.)

A. Create a KMS customer managed key that trusts Secrets Manager and allows the Lambda function's execution role to decrypt. Update Secrets Manager to use the new customer managed key.
B. Ensure that the Lambda function's execution role has the KMS permissions scoped on the resource level. Configure the permissions so that the KMS key can encrypt the Secrets Manager secret.
C. Update the default KMS key for Secrets Manager to allow only the Lambda function's execution role to decrypt.
D. Remove all KMS permissions from the Lambda function's execution role.
E. Create a KMS customer managed key that trusts Secrets Manager and allows the account's :root principal to decrypt. Update Secrets Manager to use the new customer managed key.

Answer: A,B

Explanation:
The requirement is to update the infrastructure to ensure that only the Lambda function's execution role can access the values in Secrets Manager. The solution must apply the principle of least privilege, which means granting the minimum permissions necessary to perform a task.
To do this, the DevOps engineer needs to use the following steps:
Create a KMS customer managed key that trusts Secrets Manager and allows the Lambda function's execution role to decrypt. A customer managed key is a symmetric encryption key that is fully managed by the customer. The customer can define the key policy, which specifies who can use and manage the key. By creating a customer managed key, the DevOps engineer can restrict the decryption permission to only the Lambda function's execution role, and prevent other principals from accessing the secret values. The customer managed key also needs to trust Secrets Manager, which means allowing Secrets Manager to use the key to encrypt and decrypt secrets on behalf of the customer.
Update Secrets Manager to use the new customer managed key. Secrets Manager allows customers to choose which KMS key to use for encrypting each secret. By default, Secrets Manager uses the default KMS key for Secrets Manager, which is a service-managed key that is shared by all customers in the same AWS Region. By updating Secrets Manager to use the new customer managed key, the DevOps engineer can ensure that only the Lambda function's execution role can decrypt the secret values using that key.
Ensure that the Lambda function's execution role has the KMS permissions scoped on the resource level. The Lambda function's execution role is an IAM role that grants permissions to the Lambda function to access AWS services and resources. The role needs to have KMS permissions to use the customer managed key for decryption. However, to apply the principle of least privilege, the role should have the permissions scoped on the resource level, which means specifying the ARN of the customer managed key as a condition in the IAM policy statement. This way, the role can only use that specific key and not any other KMS keys in the account.

NEW QUESTION # 151
A company has an application and a CI/CD pipeline. The CI/CD pipeline consists of an AWS CodePipeline pipeline and an AWS CodeBuild project. The CodeBuild project runs tests against the application as part of the build process and outputs a test report. The company must keep the test reports for 90 days.
Which solution will meet these requirements?

A. Add a report group in the CodeBuild project buildspec file with the appropriate path and format for the reports. Create an Amazon S3 bucket to store the reports. Configure the report group as an artifact in the CodeBuild project buildspec file. Configure the S3 bucket as the artifact destination. Set the object expiration to 90 days.
B. Add a report group in the CodeBuild project buildspec file with the appropriate path and format for the reports. Create an Amazon S3 bucket to store the reports. Configure an Amazon EventBridge rule that invokes an AWS Lambda function to copy the reports to the S3 bucket when a build is completed.
Create an S3 Lifecycle rule to expire the objects after 90 days.
C. Add a new stage in the CodePipeline pipeline. Configure a test action type with the appropriate path and format for the reports. Configure the report expiration time to be 90 days in the CodeBuild project buildspec file.
D. Add a new stage in the CodePipeline pipeline after the stage that contains the CodeBuild project. Create an Amazon S3 bucket to store the reports. Configure an S3 deploy action type in the new CodePipeline stage with the appropriate path and format for the reports.

Answer: B

Explanation:
Explanation
The correct solution is to add a report group in the AWS CodeBuild project buildspec file with the appropriate path and format for the reports. Then, create an Amazon S3 bucket to store the reports. You should configure an Amazon EventBridge rule that invokes an AWS Lambda function to copy the reports to the S3 bucket when a build is completed. Finally, create an S3 Lifecycle rule to expire the objects after 90 days. This approach allows for the automated transfer of reports to long-term storage and ensures they are retained for the required duration without manual intervention1.
References:
* AWS CodeBuild User Guide on test reporting1.
* AWS CodeBuild User Guide on working with report groups2.
* AWS Documentation on using AWS CodePipeline with AWS CodeBuild3.

NEW QUESTION # 152
A company has an application that runs on AWS Lambda and sends logs to Amazon CloudWatch Logs. An Amazon Kinesis data stream is subscribed to the log groups in CloudWatch Logs. A single consumer Lambda function processes the logs from the data stream and stores the logs in an Amazon S3 bucket.
The company's DevOps team has noticed high latency during the processing and ingestion of some logs.
Which combination of steps will reduce the latency? (Select THREE.)

A. Configure reserved concurrency for the Lambda function that processes the logs.
Increase the batch size in the Kinesis data stream.
B. Increase the ParallelizationFactor setting in the Lambda event source mapping.
C. Turn off the ReportBatchltemFailures setting in the Lambda event source mapping.Increase the number of shards in the Kinesis data stream.
D. Create a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer.

Answer: A,B,D

Explanation:
The latency in processing and ingesting logs can be caused by several factors, such as the throughput of the Kinesis data stream, the concurrency of the Lambda function, and the configuration of the event source mapping. To reduce the latency, the following steps can be taken:
Create a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer. This will allow the Lambda function to receive records from the data stream with dedicated throughput of up to 2 MB per second per shard, independent of other consumers1. This will reduce the contention and delay in accessing the data stream.
Increase the ParallelizationFactor setting in the Lambda event source mapping. This will allow the Lambda service to invoke more instances of the function concurrently to process the records from the data stream2. This will increase the processing capacity and reduce the backlog of records in the data stream.
Configure reserved concurrency for the Lambda function that processes the logs. This will ensure that the function has enough concurrency available to handle the increased load from the data stream3. This will prevent the function from being throttled by the account-level concurrency limit.
The other options are not effective or may have negative impacts on the latency. Option D is not suitable because increasing the batch size in the Kinesis data stream will increase the amount of data that the Lambda function has to process in each invocation, which may increase the execution time and latency4. Option E is not advisable because turning off the ReportBatchItemFailures setting in the Lambda event source mapping will prevent the Lambda service from retrying the failed records, which may result in data loss. Option F is not necessary because increasing the number of shards in the Kinesis data stream will increase the throughput of the data stream, but it will not affect the processing speed of the Lambda function, which is the bottleneck in this scenario.
Reference:
1: Using AWS Lambda with Amazon Kinesis Data Streams - AWS Lambda
2: AWS Lambda event source mappings - AWS Lambda
3: Managing concurrency for a Lambda function - AWS Lambda
4: AWS Lambda function scaling - AWS Lambda
5: AWS Lambda event source mappings - AWS Lambda
6: Scaling Amazon Kinesis Data Streams with AWS CloudFormation - Amazon Kinesis Data Streams

NEW QUESTION # 153
......

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