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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified DevOps Engineer - Professional |
| Exam Number: | DOP-C02 |
| Passing Score: | 720 (on a 100-1000 point scale, 72%) |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 180 minutes |
| Exam Format: | Multiple-choice (single response), Multiple-choice (multiple response) |
| Real Exam Qty: | 75 |
| Related Certifications: | AWS Certified SysOps Administrator - Associate AWS Certified Solutions Architect - Professional AWS Certified Developer - Associate |
| Exam Price: | USD 300 |
| Available Languages: | Simplified Chinese, Japanese, English, Korean |
| Sample Questions: | Amazon DOP-C02 Sample Questions |
| Exam Way: | In-person at Pearson VUE testing centers or online proctored exam |
| Pre Condition: | Recommended: One of the following Associate-level certifications or equivalent experience:- AWS Certified Solutions Architect - Associate- AWS Certified Developer - Associate- AWS Certified SysOps Administrator - AssociateRecommended work experience: 2+ years of experience in provisioning, operating, and managing AWS environments |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-devops-engineer-professional/ |
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Amazon DOP-C02 Certification Exam is intended for experienced DevOps engineers, as well as other IT professionals who work in a DevOps environment. DOP-C02 exam is designed to be challenging, and candidates are advised to have at least two years of hands-on experience working in a DevOps role before attempting the certification. Candidates who pass the exam will be certified as AWS Certified DevOps Engineers - Professionals, and will have the skills and knowledge necessary to design, manage, and maintain DevOps systems on the AWS platform.
NEW QUESTION # 320
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: A
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 # 321
A DevOps engineer is working on a project that is hosted on Amazon Linux and has failed a security review. The DevOps manager has been asked to review the company buildspec. yaml die for an AWS CodeBuild project and provide recommendations. The buildspec. yaml file is configured as follows:
What changes should be recommended to comply with AWS security best practices? (Select THREE.)
Answer: A,D,E
Explanation:
B . Update the CodeBuild project role with the necessary permissions and then remove the AWS credentials from the environment variable. C. Store the DB_PASSWORD as a SecureString value in AWS Systems Manager Parameter Store and then remove the DB_PASSWORD from the environment variables. E. Use AWS Systems Manager run command versus scp and ssh commands directly to the instance.
NEW QUESTION # 322
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?
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 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.
References:
CloudWatch Logs Insights
Amazon Athena
AWS X-Ray
AWS Lambda
Amazon DynamoDB
NEW QUESTION # 323
A DevOps engineer has developed an AWS Lambda function The Lambda function starts an AWS CloudFormation drift detection operation on all supported resources for a specific CloudFormation stack The Lambda function then exits Its invocation The DevOps engineer has created an Amazon EventBrdge scheduled rule that Invokes the Lambda function every hour. An Amazon Simple Notification Service (Amazon SNS) topic already exists In the AWS account. The DevOps engineer has subscribed to the SNS topic to receive notifications The DevOps engineer needs to receive a notification as soon as possible when drift is detected in this specific stack configuration.
Which solution Will meet these requirements?
Answer: A
Explanation:
A comprehensive and detailed explanation is:
* Option A is incorrect because EventBridge rules cannot filter events based on the message body or attributes of the target service. Therefore, configuring an SNS subscription filter policy to match the CloudFormation stack will not work. The SNS topic will receive all events from the EventBridge rule, regardless of the stack name or drift status.
* Option B is incorrect because it introduces unnecessary complexity and cost. Creating a second Lambda function to query the CloudFormation API for the drift detection results is redundant, since CloudFormation already publishes drift detection events to EventBridge. Moreover, invoking two Lambda functions every hour will incur more charges than invoking one.
* Option C is incorrect because GuardDuty does not provide drift detection for CloudFormation stacks.
GuardDuty is a threat detection service that monitors for malicious activity and unauthorized behavior in AWS accounts and workloads. Itdoes not monitor or report on configuration changes or drifts in CloudFormation stacks.
* Option D is correct because it leverages AWS Config and its managed rule for drift detection. AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources. It can detect configuration changes and drifts in CloudFormation stacks using the cloudformation-stack-drift-detection-check managed rule. This rule triggers an AWS Config event when a stack drifts from its expected template configuration. By creating a second EventBridge rule that reacts to this event for the specific stack, the DevOps engineer can configure the SNS topic as a target and receive a notification as soon as possible when drift is detected.
References:
* AWS Config
* Amazon SNS subscription filter policies
* Amazon EventBridge rules
NEW QUESTION # 324
A company detects unusual login attempts in many of its AWS accounts. A DevOps engineer must implement a solution that sends a notification to the company's security team when multiple failed login attempts occur. The DevOps engineer has already created an Amazon Simple Notification Service (Amazon SNS) topic and has subscribed the security team to the SNS topic.
Which solution will provide the notification with the LEAST operational effort?
Answer: B
Explanation:
Failed interactive logins such as ConsoleLogin are part of CloudTrail management events, not data events. The most direct, low-overhead design is to stream management events to CloudWatch Logs, then create a metric filter that matches failed ConsoleLogin events (for example, $.eventName = "ConsoleLogin" and $.responseElements.ConsoleLogin = "Failure" or $.errorMessage). That metric is then used as the basis for a CloudWatch alarm. When the alarm threshold (e.g., N failures in a period) is breached, the alarm triggers and sends a notification to the already created SNS topic, which alerts the security team.
Option A follows this recommended pattern: CloudTrail → CloudWatch Logs → Metric Filter → Alarm → SNS. It is fully managed, near real-time, and requires minimal custom logic.
Option B uses Athena with EventBridge to periodically query CloudTrail logs in S3. This introduces more moving parts, more configuration, higher latency, and more operational overhead.
Options C and D incorrectly reference CloudTrail data events and S3 event notifications, which are not the right mechanisms to detect ConsoleLogin failures. Console logins are not S3 events, and using data events would miss these management actions.
Therefore, Option A is the correct and simplest solution.
NEW QUESTION # 325
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