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Amazon DOP-C02 certification exam is an excellent opportunity for professionals to validate their skills and knowledge in AWS DevOps engineering. AWS Certified DevOps Engineer - Professional certification is highly valued in the industry and provides many opportunities for career growth. DOP-C02 exam tests the candidate's ability to design, manage, and implement AWS solutions using various DevOps tools and practices. AWS Certified DevOps Engineer - Professional certification is valid for three years, and professionals can renew it by passing the recertification exam or completing the required continuing education credits.
Amazon DOP-C02 Certification Exam is designed to test an individual's ability to implement and manage a DevOps environment on the AWS platform. This includes designing and implementing continuous delivery systems, continuous integration, and continuous deployment systems. It also measures an individual's knowledge of monitoring, logging, and metrics systems on the AWS platform, as well as their ability to implement and manage security and compliance policies.
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The DOP-C02 Certification Exam is intended for professionals who have already achieved the AWS Certified Developer - Associate or AWS Certified SysOps Administrator - Associate certification. To be eligible for the exam, candidates must have at least two years of experience in deploying and managing AWS-based applications using DevOps practices.
NEW QUESTION # 197
A company recently deployed its web application on AWS. The company is preparing for a large-scale sales event and must ensure that the web application can scale to meet the demand The application's frontend infrastructure includes an Amazon CloudFront distribution that has an Amazon S3 bucket as an origin. The backend infrastructure includes an Amazon API Gateway API. several AWS Lambda functions, and an Amazon Aurora DB cluster The company's DevOps engineer conducts a load test and identifies that the Lambda functions can fulfill the peak number of requests However, the DevOps engineer notices request latency during the initial burst of requests Most of the requests to the Lambda functions produce queries to the database A large portion of the invocation time is used to establish database connections Which combination of steps will provide the application with the required scalability? (Select TWO)
Answer: B,C
Explanation:
Explanation
The correct answer is B and E. Configuring a higher provisioned concurrency for the Lambda functions will ensure that the functions are ready to respond to the initial burst of requests without any cold start latency.
Using Amazon RDS Proxy to create a proxy for the Aurora database will enable the Lambda functions to reuse existing database connections and reduce the overhead of establishing new ones. This will also improve the scalability and availability of the database by managing the connection pool size and handling failovers.
Option A is incorrect because reserved concurrency only limits the number of concurrent executions for a function, not pre-warms them. Option C is incorrect because converting the DB cluster to an Aurora global database will not address the issue of database connection latency, and may introduce additional costs and complexity. Option D is incorrect because moving the code blocks that initialize database connections into the function handlers will not improve the performance or scalability of the Lambda functions, and may actually worsen the cold start latency. References:
* AWS Lambda Provisioned Concurrency
* Using Amazon RDS Proxy with AWS Lambda
* Certified DevOps Engineer - Professional (DOP-C02) Study Guide (page 173)
NEW QUESTION # 198
A company deploys a web application on Amazon EC2 instances that are behind an Application Load Balancer (ALB). The company stores the application code in an AWS CodeConnections compatible Git repository.
When the company merges code to the main branch, an AWS CodeBuild project is initiated. The CodeBuild project compiles the code, stores the packaged code in AWS CodeArtifact, and invokes AWS Systems Manager Run Command to deploy the packaged code to the EC2 instances.
Previous deployments have resulted in defects, EC2 instances that were not running the latest version of the packaged code, and inconsistencies between instances. A DevOps engineer needs to improve the reliability of the deployment solution.
Which combination of actions will meet this requirement? (Select TWO.)
Answer: D,E
Explanation:
The core problem described is deployment inconsistency and lack of reliability caused by using AWS Systems Manager Run Command for application deployment. Run Command executes ad hoc commands and does not provide deployment orchestration, version tracking, lifecycle hooks, or health-based traffic control, which commonly leads to drift between EC2 instances.
The most reliable AWS-native solution is to adopt AWS CodePipeline combined with AWS CodeDeploy.
Option B introduces a structured CI/CD pipeline with a clear build stage followed by a test stage, ensuring that only tested artifacts progress to deployment. Sequential build and test stages are preferred for reliability and deterministic behavior, especially when test results must gate deployments.
Option C is essential because AWS CodeDeploy is the service specifically designed to deploy application revisions consistently across EC2 fleets. By creating a CodeDeploy application and deployment group and integrating it with the ALB, deployments gain support for lifecycle events, health checks, instance synchronization, and automatic rollback. This ensures that all EC2 instances receive the same application version and that traffic is managed safely during deployments.
Option A introduces unnecessary parallelism that does not address the core issue. Option D adds excessive complexity with Lambda orchestration. Option E incorrectly replaces CodeArtifact with S3 without addressing deployment reliability.
Therefore, combining CodePipeline (B) with CodeDeploy and ALB integration (C) provides consistent, repeatable, and reliable deployments aligned with AWS best practices.
NEW QUESTION # 199
A DevOps engineer is working on a member account in an organization in AWS Organizations with all features enabled. The account has sensitive data stored in Amazon S3 buckets.
The DevOps engineer must ensure that all public access to S3 buckets in the account is blocked. If the account-level S3 Block Public Access settings change in the future, the changes must be reverted automatically so that all public access is blocked again.
Which solution meets these requirements?
Answer: A
Explanation:
Option B is the only choice that directly satisfies both requirements:
Continuously evaluate the account-level S3 Block Public Access setting
AWS Config is designed to record configuration state and evaluate resources/settings against rules over time.
A Config rule (managed rule) can check whether the account-level "S3 Block Public Access" settings are configured as required (i.e., blocking public access).
Automatically revert drift (auto-remediate) if someone changes the setting later AWS Config Remediation can automatically trigger an AWS Systems Manager Automation runbook when the rule becomes NON_COMPLIANT.
Using an SSM Automation document/runbook that sets S3 account-level Block Public Access back to the required "blocked" configuration ensures that any future change is corrected automatically, restoring compliance without manual intervention.
Why the other options don't fully meet the requirement:
A (Security Hub): Security Hub primarily aggregates findings and checks controls. While it can integrate with automation, AWS Config is the standard service for configuration drift detection + automatic remediation loops for account-level posture settings. Security Hub is not the most direct "detect config drift and auto-fix" mechanism for an account setting in the way Config remediation is.
C (SCP): An SCP can restrict API actions, but it doesn't "revert" a changed S3 Block Public Access configuration; it only prevents/limits what actions can be called. Also, "deny S3 actions from outside the account" is not the same as enforcing Block Public Access settings at the account level.
NEW QUESTION # 200
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?
Answer: C
Explanation:
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)
NEW QUESTION # 201
A company uses an organization in AWS Organizations to manage multiple AWS accounts. The company needs a solution to detect sensitive information in Amazon S3 buckets in all the company's accounts. When the solution detects sensitive data, the solution must collect all the findings and make them available to the company's security officer in a single location. The solution must move S3 objects that contain sensitive information to a quarantine S3 bucket.
Which solutions will meet these requirements with the LEAST operational overhead? (Select TWO.)
Answer: C,E
Explanation:
The company requires an organization-wide, centralized, and automated solution to detect sensitive data in Amazon S3, aggregate findings in one location, and quarantine affected objects with minimal operational overhead. AWS provides a native service specifically designed for this purpose: Amazon Macie.
Option A is essential because Amazon Macie automatically discovers and classifies sensitive data such as PII in S3 buckets using managed machine learning models. When enabled at the organization level, Macie scans buckets across all accounts and Regions. Integrating Macie with AWS Security Hub centralizes all findings in a single dashboard, allowing the company's security officer to review and manage sensitive data alerts across the organization without building custom aggregation pipelines.
Detection alone is not sufficient; remediation is also required. Option C completes the solution by using Amazon EventBridge, which natively receives Macie findings in near real time. An EventBridge rule can trigger a Lambda function whenever Macie identifies sensitive data. The Lambda function can then copy the affected object to a quarantine S3 bucket and delete the original object, meeting the remediation requirement automatically and consistently.
Option D requires custom sensitive data detection logic, which is complex, error-prone, and unnecessary given Macie's capabilities. Option E is invalid because SCPs cannot inspect or move data. Option B does not detect sensitive information.
Therefore, A and C together provide the most efficient, scalable, and AWS-recommended solution.
NEW QUESTION # 202
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