Free Amazon DOP-C02 Exam Dumps and DOP-C02 Exam Questions

Question No: 1

MultipleChoice

A company that uses electronic health records is running a fleet of Amazon EC2 instances with an Amazon Linux operating system. As part of patient privacy requirements, the company must ensure continuous compliance for patches for operating system and applications running on the EC2 instances.

How can the deployments of the operating system and application patches be automated using a default and custom repository?

Options

AUse AWS Systems Manager to create a new patch baseline including the custom repository. Run the AWS-RunPatchBaseline document using the run command to verify and install patches.

BUse AWS Direct Connect to integrate the corporate repository and deploy the patches using Amazon CloudWatch scheduled events, then use the CloudWatch dashboard to create reports.

CUse yum-config-manager to add the custom repository under /etc/yum.repos.d and run yum-config-manager-enable to activate the repository.

DUse AWS Systems Manager to create a new patch baseline including the corporate repository. Run the AWS-AmazonLinuxDefaultPatchBaseline document using the run command to verify and install patches.

Question No: 2

MultipleChoice

A company has 20 service learns Each service team is responsible for its own microservice. Each service team uses a separate AWS account for its microservice and a VPC with the 192 168 0 0/22 CIDR block. The company manages the AWS accounts with AWS Organizations.

Each service team hosts its microservice on multiple Amazon EC2 instances behind an Application Load Balancer. The microservices communicate with each other across the public internet. The company's security team has issued a new guideline that all communication between microservices must use HTTPS over private network connections and cannot traverse the public internet.

A DevOps engineer must implement a solution that fulfills these obligations and minimizes the number of changes for each service team

Which solution will meet these requirements?

Options

ACreate a new AWS account in AWS Organizations Create a VPC in this account and use AWS Resource Access Manager to share the private subnets of this VPC with the organization Instruct the service teams to launch a new. Network Load Balancer (NLB) and EC2 instances that use the shared private subnets Use the NLB DNS names for communication between microservices.

BCreate a Network Load Balancer (NLB) in each of the microservice VPCs Use AWS PrivateLink to create VPC endpoints in each AWS account for the NLBs Create subscriptions to each VPC endpoint in each of the other AWS accounts Use the VPC endpoint DNS names for communication between microservices.

CCreate a Network Load Balancer (NLB) in each of the microservice VPCs Create VPC peering connections between each of the microservice VPCs Update the route tables for each VPC to use the peering links Use the NLB DNS names for communication between microservices.

DCreate a new AWS account in AWS Organizations Create a transit gateway in this account and use AWS Resource Access Manager to share the transit gateway with the organization. In each of the microservice VPCs. create a transit gateway attachment to the shared transit gateway Update the route tables of each VPC to use the transit gateway Create a Network Load Balancer (NLB) in each of the microservice VPCs Use the NLB DNS names for communication between microservices.

Question No: 3

MultipleChoice

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 companfs existing Amazon CloudWatch dashboards

Which solution Will meet these requirements With the MOST operational effictency?

Options

AUpdate 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.

BUpdate 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

CUpdate 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

DUpdate 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.

Answer
AExplanation

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 CloudWatch dashboard 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.

CloudWatch Logs Insights

Question No: 4

MultipleChoice

A company hosts applications in its AWS account Each application logs to an individual Amazon CloudWatch log group. The companys CloudWatch costs for ingestion are increasing

A DevOps engineer needs to Identify which applications are the source of the increased logging costs.

Which solution Will meet these requirements?

Options

AUse CloudWatch metrics to create a custom expression that Identifies the CloudWatch log groups that have the most data being written to them.

BUse CloudWatch Logs Insights to create a set of queries for the application log groups to Identify the number of logs written for a period of time

CUse AWS Cost Explorer to generate a cost report that details the cost for CloudWatch usage

DUse AWS CloudTrail to filter for CreateLogStream events for each application

Answer
CExplanation

The correct answer is C)

A comprehensive and detailed explanation is:

Option A is incorrect because using CloudWatch metrics to create a custom expression that identifies the CloudWatch log groups that have the most data being written to them is not a valid solution. CloudWatch metrics do not provide information about the size or volume of data being ingested by CloudWatch logs. CloudWatch metrics only provide information about the number of events, bytes, and errors that occur within a log group or stream. Moreover, creating a custom expression with CloudWatch metrics would require using the search_web tool, which is not necessary for this use case.

Option B is incorrect because using CloudWatch Logs Insights to create a set of queries for the application log groups to identify the number of logs written for a period of time is not a valid solution. CloudWatch Logs Insights can help analyze and filter log events based on patterns and expressions, but it does not provide information about the cost or billing of CloudWatch logs. CloudWatch Logs Insights also charges based on the amount of data scanned by each query, which could increase the logging costs further.

Option C is correct because using AWS Cost Explorer to generate a cost report that details the cost for CloudWatch usage is a valid solution. AWS Cost Explorer is a tool that helps visualize, understand, and manage AWS costs and usage over time. AWS Cost Explorer can generate custom reports that show the breakdown of costs by service, region, account, tag, or any other dimension. AWS Cost Explorer can also filter and group costs by usage type, which can help identify the specific CloudWatch log groups that are the source of the increased logging costs.

Option D is incorrect because using AWS CloudTrail to filter for CreateLogStream events for each application is not a valid solution. AWS CloudTrail is a service that records API calls and account activity for AWS services, including CloudWatch logs. However, AWS CloudTrail does not provide information about the cost or billing of CloudWatch logs. Filtering for CreateLogStream events would only show when a new log stream was created within a log group, but not how much data was ingested or stored by that log stream.

CloudWatch Metrics

CloudWatch Logs Insights

AWS Cost Explorer

AWS CloudTrail

Question No: 5

MultipleChoice

A video-sharing company stores its videos in Amazon S3. The company has observed a sudden increase in video access requests, but the company does not know which videos are most popular. The company needs to identify the general access pattern for the video files. This pattern includes the number of users who access a certain file on a given day, as well as the number of pull requests for certain files.

How can the company meet these requirements with the LEAST amount of effort?

Options

AActivate S3 server access logging. Import the access logs into an Amazon Aurora database. Use an Aurora SQL query to analyze the access patterns.

BActivate S3 server access logging. Use Amazon Athena to create an external table with the log files. Use Athena to create a SQL query to analyze the access patterns.

CInvoke an AWS Lambda function for every S3 object access event. Configure the Lambda function to write the file access information, such as user. S3 bucket, and file key, to an Amazon Aurora database. Use an Aurora SQL query to analyze the access patterns.

DRecord an Amazon CloudWatch Logs log message for every S3 object access event. Configure a CloudWatch Logs log stream to write the file access information, such as user, S3 bucket, and file key, to an Amazon Kinesis Data Analytics for SQL application. Perform a sliding window analysis.