Pass Guaranteed Quiz 2026 Amazon DOP-C02: AWS Certified DevOps Engineer - Professional First-grade Study Guide

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Amazon DOP-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Resilient Cloud Solutions	15%	- Implement disaster recovery 1. Test and validate recovery plans 2. Pilot light, warm standby, and active-active 3. Backup and restore strategies - Ensure reliability and scalability 1. Manage capacity and elasticity 2. Optimize performance and cost 3. Design for fault tolerance - Design high availability architectures 1. Multi-AZ and multi-region deployments 2. Use Route 53 for routing and failover 3. Implement auto scaling and load balancing
Topic 2: Security, Governance, and Compliance	17%	- Implement security controls 1. Secure secrets and credentials 2. Manage IAM for pipelines and environments 3. Encrypt data at rest and in transit - Automate security and compliance 1. Integrate security into CI/CD pipelines 2. Detect and remediate violations automatically 3. Use AWS Security Hub and AWS Shield - Enforce governance and compliance 1. Manage access and permissions at scale 2. Implement compliance rules and audits 3. Use AWS Config and AWS Organizations
Topic 3: Monitoring and Logging	15%	- Automate incident response 1. Automate remediation and scaling 2. Implement alerting and notification workflows 3. Set up event-driven actions with Amazon EventBridge - Implement logging and analysis 1. Use AWS X-Ray for tracing 2. Centralize logs with CloudWatch Logs 3. Analyze logs for troubleshooting and security - Implement monitoring solutions 1. Create dashboards and insights 2. Use Amazon CloudWatch for metrics and alarms 3. Monitor infrastructure, applications, and costs
Topic 4: Incident and Event Response	14%	- Improve operational processes 1. Update runbooks and playbooks 2. Optimize response time and efficiency 3. Implement feedback loops - Manage incidents and outages 1. Troubleshoot complex issues 2. Coordinate response and recovery 3. Document and learn from events - Automate event management 1. Integrate with ticketing and workflow systems 2. Classify and prioritize events 3. Automate common operational tasks
Topic 5: Configuration Management and Infrastructure as Code	17%	- Implement infrastructure as code 1. Manage templates and stacks 2. Enforce configuration standards 3. Use AWS CloudFormation, Terraform, or AWS CDK - Manage infrastructure deployments 1. Implement version control for configurations 2. Provision and update resources automatically 3. Manage multi-account and multi-region environments - Automate configuration management 1. Use AWS Systems Manager, Ansible, or Puppet 2. Detect and remediate configuration drift 3. Maintain consistent configurations
Topic 6: SDLC Automation	22%	- Automate testing and validation 1. Security and compliance testing 2. Unit, integration, and performance testing 3. Automate quality gates - Implement CI/CD pipelines 1. Integrate version control systems 2. Use AWS CodePipeline, CodeBuild, CodeDeploy 3. Automate build, test, and deployment stages - Automate software release processes 1. Implement deployment strategies 2. Automate rollback procedures 3. Manage artifacts and dependencies

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Amazon DOP-C02 Dumps Questions, DOP-C02 Reliable Test Questions

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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q73-Q78):

NEW QUESTION # 73
A company is developing an application that will generate log events. The log events consist of five distinct metrics every one tenth of a second and produce a large amount of data The company needs to configure the application to write the logs to Amazon Time stream The company will configure a daily query against the Timestream table.
Which combination of steps will meet these requirements with the FASTEST query performance? (Select THREE.)

A. Use batch writes to write multiple log events in a Single write operation
B. Treat each log as a single-measure record
C. Treat each log as a multi-measure record
D. Configure the memory store retention period to be shorter than the magnetic store retention period
E. Configure the memory store retention period to be longer than the magnetic store retention period
F. Write each log event as a single write operation

Answer: A,C,D

Explanation:
A comprehensive and detailed explanation is:
* Option A is correct because using batch writes to write multiple log events in a single write operation is a recommended practice for optimizing the performance and cost of data ingestion in Timestream.
Batch writes can reduce the number of network round trips and API calls, and can also take advantage of parallel processing by Timestream.Batch writes can also improve the compression ratio of data in the memory store and the magnetic store, which can reduce the storage costs and improve the query performance1.
* Option B is incorrect because writing each log event as a single write operation is not a recommended practice for optimizing the performance and cost of data ingestion in Timestream. Writing each log event as a single write operation would increase the number of network round trips and API calls, and would also reduce the compression ratio of data in the memory store and the magneticstore.This would increase the storage costs and degrade the query performance1.
* Option C is incorrect because treating each log as a single-measure record is not a recommended practice for optimizing the query performance in Timestream. Treating each log as a single-measure record would result in creating multiple records for each timestamp, which would increase the storage size and the query latency.Moreover, treating each log as a single-measure record would require using joins to query multiple measures for the same timestamp, whichwould add complexity and overhead to the query processing2.
* Option D is correct because treating each log as a multi-measure record is a recommended practice for optimizing the query performance in Timestream. Treating each log as a multi-measure record would result in creating a single record for each timestamp, which would reduce the storage size and the query latency.Moreover, treating each log as a multi-measure record would allow querying multiple measures for the same timestamp without using joins, which would simplify and speed up the query processing2.
* Option E is incorrect because configuring the memory store retention period to be longer than the magnetic store retention period is not a valid option in Timestream. The memory store retention period must always be shorter than or equal to the magnetic store retention period.This ensures that data is moved from the memory store to the magnetic store before it expires out of the memory store3.
* Option F is correct because configuring the memory store retention period to be shorter than the magnetic store retention period is a valid option in Timestream. The memory store retention period determines how long data is kept in the memory store, which is optimized for fast point-in-time queries.
The magnetic store retention period determines how long data is kept in the magnetic store, which is optimized for fast analytical queries.By configuring these retentionperiods appropriately, you can balance your storage costs and query performance according to yourapplication needs3.
References:
* 1:Batch writes
* 2:Multi-measure records vs. single-measure records
* 3:Storage

NEW QUESTION # 74
A company has a single developer writing code for an automated deployment pipeline. The developer is storing source code in an Amazon S3 bucket for each project. The company wants to add more developers to the team but is concerned about code conflicts and lost work The company also wants to build a test environment to deploy newer versions of code for testing and allow developers to automatically deploy to both environments when code is changed in the repository.
What is the MOST efficient way to meet these requirements?

A. Enable versioning and branching on each S3 bucket, use the main branch for production code, and create a testing branch for code deployed to testing. Have developers use each branch for developing in each environment.
B. Create an AWS CodeCommit repository for each project, and use the main branch for production and test code with different deployment pipelines for each environment Use feature branches to develop new features.
C. Create another S3 bucket for each project for testing code, and use an AWS Lambda function to promote code changes between testing and production buckets Enable versioning on all buckets to prevent code conflicts.
D. Create an AWS CodeCommit repository tor each project, use the mam branch for production code: and create a testing branch for code deployed to testing Use feature branches to develop new features and pull requests to merge code to testing and main branches.

Answer: D

Explanation:
Explanation
Creating an AWS CodeCommit repository for each project, using the main branch for production code, and creating a testing branch for code deployed to testing will meet the requirements. AWS CodeCommit is a managed revision control service that hosts Git repositories and works with all Git-based tools1. By using feature branches to develop new features and pull requests to merge code to testing and main branches, the developers can avoid code conflicts and lost work, and also implement code reviews and approvals. Option B is incorrect because creating another S3 bucket for each project for testing code and using an AWS Lambda function to promote code changes between testing and production buckets will not provide the benefits of revision control, such as tracking changes, branching, merging, and collaborating. Option C is incorrect because using the main branch for production and test code with different deployment pipelines for each environment will not allow the developers to test their code changes before deploying them to production.
Option D is incorrect because enabling versioning and branching on each S3 bucket will not work with Git-based tools and will not provide the same level of revision control as AWS CodeCommit. References:
* AWS CodeCommit
* Certified DevOps Engineer - Professional (DOP-C02) Study Guide (page 182)

NEW QUESTION # 75
A DevOps administrator is responsible for managing the security of a company's Amazon CloudWatch Logs log groups. The company's security policy states that employee IDs must not be visible in logs except by authorized personnel. Employee IDs follow the pattern of Emp-XXXXXX, where each X is a digit.
An audit discovered that employee IDs are found in a single log file. The log file is available to engineers, but the engineers are not authorized to view employee IDs. Engineers currently have an AWS IAM Identity Center permission that allows logs:* on all resources in the account.
The administrator must mask the employee ID so that new log entries that contain the employee ID are not visible to unauthorized personnel.
Which solution will meet these requirements with the MOST operational efficiency?

A. Create a new data protection policy on the log group. Add managed data identifiers for the personal data category. Create an IAM policy that has a Deny action for the "NotAction":"logs:Unmask" permission on the resource. Attach the policy to the engineering accounts.
B. Create an AWS Lambda function to parse a log file entry, remove the employee ID, and write the results to a new log file. Create a Lambda subscription filter on the log group and select the Lambda function. Grant the lambda:InvokeFunction permission to the log group.
C. Create an Amazon Data Firehose delivery stream that has an Amazon S3 bucket as the destination.Create a Firehose subscription filter on the log group that uses the Firehose delivery stream. Remove the "logs:*" permission on the engineering accounts. Create an Amazon Macie job on the S3 bucket that has an Emp-\d{6} custom identifier.
D. Create a new data protection policy on the log group. Add an Emp-\d{6} custom data identifier configuration. Create an IAM policy that has a Deny action for the "Action":"logs:Unmask" permission on the resource. Attach the policy to the engineering accounts.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Amazon CloudWatch Logs now supportsdata protection policiesthat can mask sensitive data in logs at ingestion time usingcustom data identifiers. By creating a custom data identifier matching the employee ID pattern (Emp-\d{6}), the administrator can mask those patterns in new log entries automatically.
Assigning an IAM policy that denies the logs:Unmask action to engineers prevents them from seeing unmasked sensitive data, enforcing least privilege access.
This approach is operationally efficient because it uses built-in CloudWatch Logs data protection capabilities without the need for complex custom code or manual log filtering.
Option C requires writing and managing Lambda functions for log transformation, increasing operational overhead. Option D is more complex and reactive rather than preventing the sensitive data exposure. Option B incorrectly references NotAction and managed identifiers that may not match the custom pattern.
Reference:
Amazon CloudWatch Logs Data Protection Policies:"You can create data protection policies with custom data identifiers to mask sensitive information such as employee IDs in log data."(AWS Documentation on CloudWatch Logs Data Protection)

NEW QUESTION # 76
A DevOps engineer manages a company's Amazon Elastic Container Service (Amazon ECS) cluster. The cluster runs on several Amazon EC2 instances that are in an Auto Scaling group. The DevOps engineer must implement a solution that logs and reviews all stopped tasks for errors.
Which solution will meet these requirements?

A. Configure an EC2 Auto Scaling lifecycle hook for the EC2_INSTANCE_TERMINATING scale-in event. Write the SystemEventLog file to Amazon S3. Use Amazon Athena to query the log file for errors.
B. Configure the EC2 instances to store logs in Amazon CloudWatch Logs. Create a CloudWatch Contributor Insights rule that uses the EC2 instance log data. Use the Contributor Insights rule to investigate stopped tasks.
C. Configure tasks to write log data in the embedded metric format. Store the logs in Amazon CloudWatch Logs. Monitor the ContainerInstanceCount metric for changes.
D. Create an Amazon EventBridge rule to capture task state changes. Send the event to Amazon CloudWatch Logs. Use CloudWatch Logs Insights to investigate stopped tasks.

Answer: D

Explanation:
The best solution to log and review all stopped tasks for errors is to use Amazon EventBridge and Amazon CloudWatch Logs. Amazon EventBridge allows the DevOps engineer to create a rule that matches task state change events from Amazon ECS. The rule can then send the event data to Amazon CloudWatch Logs as the target. Amazon CloudWatch Logs can store and monitor the log data, and also provide CloudWatch Logs Insights, a feature that enables the DevOps engineer to interactively search and analyze the log data. Using CloudWatch Logs Insights, the DevOps engineer can filter and aggregate the log data based on various fields, such as cluster, task, container, and reason. This way, the DevOps engineer can easily identify and investigate the stopped tasks and their errors.
The other options are not as effective or efficient as the solution in option A. Option B is not suitable because the embedded metric format is designed for custom metrics, not for logging task state changes. Option C is not feasible because the EC2 instances do not store the task state change events in their logs. Option D is not relevant because the EC2_INSTANCE_TERMINATING lifecycle hook is triggered when an EC2 instance is terminated by the Auto Scaling group, not when a task is stopped by Amazon ECS.
Creating a CloudWatch Events Rule That Triggers on an Event - Amazon Elastic Container Service Sending and Receiving Events Between AWS Accounts - Amazon EventBridge Working with Log Data - Amazon CloudWatch Logs Analyzing Log Data with CloudWatch Logs Insights - Amazon CloudWatch Logs Embedded Metric Format - Amazon CloudWatch Amazon EC2 Auto Scaling Lifecycle Hooks - Amazon EC2 Auto Scaling

NEW QUESTION # 77
A company is migrating its container-based workloads to an AWS Organizations multi-account environment.
The environment consists of application workload accounts that the company uses to deploy and run the containerized workloads. The company has also provisioned a shared services account tor shared workloads in the organization.
The company must follow strict compliance regulations. All container images must receive security scanning before they are deployed to any environment. Images can be consumed by downstream deployment mechanisms after the images pass a scan with no critical vulnerabilities. Pre-scan and post-scan images must be isolated from one another so that a deployment can never use pre-scan images.
A DevOps engineer needs to create a strategy to centralize this process.
Which combination of steps will meet these requirements with the LEAST administrative overhead? (Select TWO.)

A. Create pre-scan Amazon Elastic Container Registry (Amazon ECR) repositories in each account that publishes container images. Create repositories for post-scan images in the shared services account.
Configure Amazon ECR image scanning to run on new image pushes to the pre-scan repositories. Use resource-based policies to grant the organization read access to the post-scan repositories.
B. Create Amazon Elastic Container Registry (Amazon ECR) repositories in the shared services account:
one repository for each pre-scan image and one repository for each post-scan image. Configure Amazon ECR image scanning to run on new image pushes to the pre-scan repositories. Use resource-based policies to grant the organization write access to the pre-scan repositories and read access to the post- scan repositories.
C. Create a pipeline in AWS CodePipeline for each pre-scan repository. Create a source stage that runs when new images are pushed to the pre-scan repositories. Create a stage that uses AWS CodeBuild as the action provider. Write a buildspec.yaml definition that determines the image scanning status and pushes images without critical vulnerabilities lo the post-scan repositories.
D. Configure image replication for each image from the image's pre-scan repository to the image's post- scan repository.
E. Create an AWS Lambda function. Create an Amazon EventBridge rule that reacts to image scanning completed events and invokes the Lambda function. Write function code that determines the image scanning status and pushes images without critical vulnerabilities to the post-scan repositories.

Answer: B,D

NEW QUESTION # 78
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