HOTTEST AMAZON DOP-C02 CERTIFICATION, NEW DOP-C02 BRAINDUMPS FREE

Hottest Amazon DOP-C02 Certification, New DOP-C02 Braindumps Free

Hottest Amazon DOP-C02 Certification, New DOP-C02 Braindumps Free

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The DOP-C02 certification exam consists of 75 multiple-choice and multiple-response questions, which must be completed within 180 minutes. DOP-C02 exam is designed to test the candidate's knowledge across several domains, including Configuration Management and Infrastructure as Code, Monitoring and Logging, Security, Compliance, and Deployment and Provisioning. DOP-C02 Exam is computer-based and can be taken at an AWS test center or remotely.

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Today is the best time to become competitive and updated in the market. You can do this easily. Just enroll in the DOP-C02 exam and start DOP-C02 exam preparation with AWS Certified DevOps Engineer - Professional exam dumps. Download the Exams. Solutions Amazon DOP-C02 Exam Dumps after paying an affordable DOP-C02 exam questions charge and start this journey without wasting further time.

Amazon DOP-C02 (AWS Certified DevOps Engineer - Professional) certification exam is a highly sought after certification that validates the skills and knowledge required to manage and deploy applications on the AWS platform. AWS Certified DevOps Engineer - Professional certification is designed for DevOps engineers who have experience in developing, provisioning, operating and managing applications on the AWS platform. DOP-C02 exam tests the candidate's ability to design, deploy, manage, and maintain AWS-based applications using DevOps practices and principles.

The AWS Certified DevOps Engineer - Professional (DOP-C02) exam is designed to validate the skills and knowledge required to work with AWS in a DevOps engineering role. AWS Certified DevOps Engineer - Professional certification exam is intended for professionals who have experience working with AWS services and are responsible for managing and deploying applications on the AWS platform. DOP-C02 Exam is a comprehensive assessment of a candidate's ability to design, deploy, and manage scalable and highly available systems on AWS.

Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q65-Q70):

NEW QUESTION # 65
A company's application runs on Amazon EC2 instances. The application writes to a log file that records the username, date, time: and source IP address of the login. The log is published to a log group in Amazon CloudWatch Logs The company is performing a root cause analysis for an event that occurred on the previous day The company needs to know the number of logins for a specific user from the past 7 days Which solution will provide this information'?

  • A. Create a CloudWatch dashboard. Add a number widget that has a filter pattern that counts the number of logins for the username over the past 7 days directly from the log group
  • B. Create a CloudWatch Logs Insights query that uses an aggregation function to count the number of logins for the username over the past 7 days. Run the query against the log group
  • C. Create a CloudWatch Logs metric filter on the log group Use a filter pattern that matches the username. Publish a CloudWatch metric that sums the number of logins over the past 7 days.
  • D. Create a CloudWatch Logs subscription on the log group Use a filter pattern that matches the username Publish a CloudWatch metric that sums the number of logins over the past 7 days

Answer: B

Explanation:
To analyze and find the number of logins for a specific user from the past 7 days, a CloudWatch Logs Insights query is the most suitable solution. CloudWatch Logs Insights enables you to interactively search and analyze your log data in Amazon CloudWatch Logs. You can use the query language to perform queries that contain multiple commands, including aggregation functions, which can count the occurrences of logins for a specific username over a specified time period. This approach is more direct and efficient than creating a metric filter or subscription, which would require additional steps to publish and sum a metric. Reference: AWS Certified DevOps Engineer - Professional, CloudWatch Logs Insights query syntax, Tutorial: Run a query with an aggregation function, Add or remove a number widget from a CloudWatch dashboard.


NEW QUESTION # 66
A company has a mobile application that makes HTTP API calls to an Application Load Balancer (ALB). The ALB routes requests to an AWS Lambda function. Many different versions of the application are in use at any given time, including versions that are in testing by a subset of users. The version of the application is defined in the user-agent header that is sent with all requests to the API.
After a series of recent changes to the API, the company has observed issues with the application. The company needs to gather a metric for each API operation by response code for each version of the application that is in use. A DevOps engineer has modified the Lambda function to extract the API operation name, version information from the user-agent header and response code.
Which additional set of actions should the DevOps engineer take to gather the required metrics?

  • A. Modify the Lambda function to write the API operation name, response code, and version number as a log line to an Amazon CloudWatch Logs log group. Configure a CloudWatch Logs metric filter that increments a metric for each API operation name. Specify response code and application version as dimensions for the metric.
  • B. Configure AWS X-Ray integration on the Lambda function. Modify the Lambda function to create an X-Ray subsegment with the API operation name, response code, and version number. Configure X-Ray insights to extract an aggregated metric for each API operation name and to publish the metric to Amazon CloudWatch. Specify response code and application version as dimensions for the metric.
  • C. Configure the ALB access logs to write to an Amazon CloudWatch Logs log group. Modify the Lambda function to respond to the ALB with the API operation name, response code, and version number as response metadata. Configure a CloudWatch Logs metric filter that increments a metric for each API operation name. Specify response code and application version as dimensions for the metric.
  • D. Modify the Lambda function to write the API operation name, response code, and version number as a log line to an Amazon CloudWatch Logs log group. Configure a CloudWatch Logs Insights query to populate CloudWatch metrics from the log lines. Specify response code and application version as dimensions for the metric.

Answer: A

Explanation:
Explanation
"Note that the metric filter is different from a log insights query, where the experience is interactive and provides immediate search results for the user to investigate. No automatic action can be invoked from an insights query. Metric filters, on the other hand, will generate metric data in the form of a time series. This lets you create alarms that integrate into your ITSM processes, execute AWS Lambda functions, or even create anomaly detection models."
https://aws.amazon.com/blogs/mt/quantify-custom-application-metrics-with-amazon-cloudwatch-logs-and-metri


NEW QUESTION # 67
A company uses AWS Directory Service for Microsoft Active Directory as its identity provider (IdP). The company requires all infrastructure to be defined and deployed by AWS CloudFormation.
A DevOps engineer needs to create a fleet of Windows-based Amazon EC2 instances to host an application.
The DevOps engineer has created a
CloudFormation template that contains an EC2 launch template, IAM role, EC2 security group, and EC2 Auto Scaling group. The DevOps engineer must implement a solution that joins all EC2 instances to the domain of the AWS Managed Microsoft AD directory.
Which solution will meet these requirements with the MOST operational efficiency?

  • A. Store the existing AWS Managed Microsoft AD domain connection details in AWS Secrets Manager. In the CloudFormation template, create an AWS::SSM::Association resource to associate the AWS-CreateManagedWindowslnstanceWithApproval Automation runbook with the EC2 Auto Scaling group. Pass the ARNs for the parameters from Secrets Manager to join the domain. Attach the AmazonSSMDirectoryServiceAccess and SecretsManagerReadWrite AWS managed policies to the IAM role that the EC2 instances use.
  • B. In the CloudFormation template, create an AWS::SSM::Document resource that joins the EC2 instance to the AWS Managed Microsoft AD domain by using the parameters for the existing directory. Update the launch template to include the SSMAssociation property to use the new SSM document. Attach the AmazonSSMManagedlnstanceCore and AmazonSSMDirectoryServiceAccess AWS managed policies to the IAM role that the EC2 instances use.
  • C. In the CloudFormation template, update the launch template to include specific tags that propagate on launch. Create an AWS::SSM::Association resource to associate the AWS-JoinDirectoryServiceDomain Automation runbook with the EC2 instances that have the specified tags. Define the required parameters to join the AWS Managed Microsoft AD directory. Attach the AmazonSSMManagedlnstanceCore and AmazonSSMDirectoryServiceAccess AWS managed policies to the IAM role that the EC2 instances use.
  • D. Store the existing AWS Managed Microsoft AD domain administrator credentials in AWS Secrets Manager. In the CloudFormation template, update the EC2 launch template to include user data.Configure the user data to pull the administrator credentials from Secrets Manager and to join the AWS Managed Microsoft AD domain. Attach the AmazonSSMManagedlnstanceCore and SecretsManagerReadWrite AWS managed policies to the IAM role that the EC2 instances use.

Answer: C

Explanation:
Explanation
To meet the requirements, the DevOps engineer needs to create a solution that joins all EC2 instances to the domain of the AWS Managed Microsoft AD directory with the most operational efficiency. The DevOps engineer can use AWS Systems Manager Automation to automate the domain join process using an existing runbook called AWS-JoinDirectoryServiceDomain. This runbook can join Windows instances to an AWS Managed Microsoft AD or Simple AD directory by using PowerShell commands. The DevOps engineer can create an AWS::SSM::Association resource in the CloudFormation template to associate the runbook with the EC2 instances that have specific tags. The tags can be defined in the launch template and propagated on launch to the EC2 instances. The DevOps engineer can also define the required parameters for the runbook, such as the directory ID, directory name, and organizational unit. The DevOps engineer can attach the AmazonSSMManagedlnstanceCore and AmazonSSMDirectoryServiceAccess AWS managed policies to the IAM role that the EC2 instances use. These policies grant the necessary permissions for Systems Manager and Directory Service operations.


NEW QUESTION # 68
A company has configured an Amazon S3 event source on an AWS Lambda function The company needs the Lambda function to run when a new object is created or an existing object IS modified In a particular S3 bucket The Lambda function will use the S3 bucket name and the S3 object key of the incoming event to read the contents of the created or modified S3 object The Lambda function will parse the contents and save the parsed contents to an Amazon DynamoDB table.
The Lambda function's execution role has permissions to read from the S3 bucket and to write to the DynamoDB table, During testing, a DevOps engineer discovers that the Lambda function does not run when objects are added to the S3 bucket or when existing objects are modified.
Which solution will resolve this problem?

  • A. Configure an Amazon Simple Queue Service (Amazon SQS) queue as an OnFailure destination for the Lambda function
  • B. Increase the memory of the Lambda function to give the function the ability to process large files from the S3 bucket.
  • C. Create a resource policy on the Lambda function to grant Amazon S3 the permission to invoke the Lambda function for the S3 bucket
  • D. Provision space in the /tmp folder of the Lambda function to give the function the ability to process large files from the S3 bucket

Answer: C

Explanation:
* Option A is incorrect because increasing the memory of the Lambda function does not address the root cause of the problem, which is that the Lambda function is not triggered by the S3 event source.
Increasing the memory of the Lambda function might improve its performance or reduce its execution time, but it does not affect its invocation. Moreover, increasing the memory of the Lambda function might incur higher costs, as Lambda charges based on the amount of memory allocated to the function.
* Option B is correct because creating a resource policy on the Lambda function to grant Amazon S3 the permission to invoke the Lambda function for the S3 bucket is a necessary step to configure an S3 event source. A resource policy is a JSON document that defines who can access a Lambda resource and underwhat conditions. By granting Amazon S3 permission to invoke the Lambda function, the company ensures that the Lambda function runs when a new object is created or an existing object is modified in the S3 bucket1.
* Option C is incorrect because configuring an Amazon Simple Queue Service (Amazon SQS) queue as an On-Failure destination for the Lambda function does not help with triggering the Lambda function.
An On-Failure destination is a feature that allows Lambda to send events to another service, such as SQS or Amazon Simple Notification Service (Amazon SNS), when a function invocation fails.
However, this feature only applies to asynchronous invocations, and S3 event sources use synchronous invocations. Therefore, configuring an SQS queue as an On-Failure destination would have no effect on the problem.
* Option D is incorrect because provisioning space in the /tmp folder of the Lambda function does not address the root cause of the problem, which is that the Lambda function is not triggered by the S3 event source. Provisioning space in the /tmp folder of the Lambda function might help with processing large files from the S3 bucket, as it provides temporary storage for up to 512 MB of data. However, it does not affect the invocation of the Lambda function.
References:
* Using AWS Lambda with Amazon S3
* Lambda resource access permissions
* AWS Lambda destinations
* [AWS Lambda file system]


NEW QUESTION # 69
A company sells products through an ecommerce web application The company wants a dashboard that shows a pie chart of product transaction details. The company wants to integrate the dashboard With the company's existing Amazon CloudWatch dashboards Which solution Will meet these requirements With the MOST operational efficiency?

  • A. Update the ecommerce application to emit a JSON object to a CloudWatch log group for each processed transaction. Use CloudWatch Logs Insights to query the log group and to visualize the results in a pie chart format Attach the results to the desired CloudWatch dashboard.
  • B. Update the ecommerce application to emit a JSON object to a CloudWatch log group for each processed transaction_ Create an AWS Lambda function to aggregate and write the results to Amazon DynamoDB. Create a Lambda subscription filter for the log file. Attach the results to the desired CloudWatch dashboard.
  • C. Update the ecommerce application to use AWS X-Ray for instrumentation. Create a new X-Ray subsegment Add an annotation for each processed transaction. Use X-Ray traces to query the data and to visualize the results in a pie chart format Attach the results to the desired CloudWatch dashboard
  • D. Update the ecommerce application to emit a JSON object to an Amazon S3 bucket for each processed transaction. Use Amazon Athena to query the S3 bucket and to visualize the results In a Pie chart format. Export the results from Athena Attach the results to the desired CloudWatch dashboard

Answer: A

Explanation:
The correct answer is A.
A comprehensive and detailed explanation is:
* Option A is correct because it meets the requirements with the most operational efficiency. Updating the ecommerce application to emit a JSON object to a CloudWatch log group for each processed transaction is a simple and cost-effective way to collect the data needed for the dashboard. Using CloudWatch Logs Insights to query the log group and to visualize the results in a pie chart format is also a convenient and integrated solution that leverages the existing CloudWatch dashboards. Attaching the results to the desired CloudWatchdashboard is straightforward and does not require any additional steps or services.
* Option B is incorrect because it introduces unnecessary complexity and cost. Updating the ecommerce application to emit a JSON object to an Amazon S3 bucket for each processed transaction is a valid way to store the data, but it requires creating and managing an S3 bucket and its permissions. Using Amazon Athena to query the S3 bucket and to visualize the results in a pie chart format is also a valid way to analyze the data, but it incurs charges based on the amount of data scanned by each query. Exporting the results from Athena and attaching them to the desired CloudWatch dashboard is also an extra step that adds more overhead and latency.
* Option C is incorrect because it uses AWS X-Ray for an inappropriate purpose. Updating the ecommerce application to use AWS X-Ray for instrumentation is a good practice for monitoring and tracing distributed applications, but it is not designed for aggregating product transaction details.
Creating a new X-Ray subsegment and adding an annotation for each processed transaction is possible, but it would clutter the X-Ray service map and make it harder to debug performance issues. Using X- Ray traces to query the data and to visualize the results in a pie chart format is also possible, but it would require custom code and logic that are not supported by X-Ray natively. Attaching the results to the desired CloudWatch dashboard is also not supported by X-Ray directly, and would require additional steps or services.
* Option D is incorrect because it introduces unnecessary complexity and cost. Updating the ecommerce application to emit a JSON object to a CloudWatch log group for each processed transaction is a simple and cost-effective way to collect the data needed for the dashboard, as in option A. However, creating an AWS Lambda function to aggregate and write the results to Amazon DynamoDB is redundant, as CloudWatch Logs Insights can already perform aggregation queries on log data. Creating a Lambda subscription filter for the log file is also redundant, as CloudWatch Logs Insights can already access log data directly. Attaching the results to the desired CloudWatch dashboard would also require additional steps or services, as DynamoDB does not support native integration with CloudWatch dashboards.
References:
* CloudWatch Logs Insights
* Amazon Athena
* AWS X-Ray
* AWS Lambda
* Amazon DynamoDB


NEW QUESTION # 70
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