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Achieving the DOP-C02 Certification demonstrates a candidate's ability to design, deploy, and operate scalable, highly available, and fault-tolerant systems on AWS. It also highlights their proficiency in implementing and automating security and compliance controls, as well as their expertise in optimizing and improving the performance of applications and infrastructure.
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The AWS Certified DevOps Engineer - Professional certification exam covers a range of topics related to DevOps, including continuous integration and continuous delivery, monitoring and logging, security, and automation. Candidates must demonstrate a deep understanding of these topics, as well as the ability to apply their knowledge to real-world scenarios. DOP-C02 Exam also tests candidates' ability to work collaboratively with cross-functional teams, communicate effectively, and troubleshoot problems.
NEW QUESTION # 247
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?
Answer: B
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 under what 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 # 248
A company uses an organization in AWS Organizations to manage multiple AWS accounts in multiple OUs.
The company is planning to implement a comprehensive account management solution and wants to ensure consistent baseline configurations.
A DevOps engineer is developing a solution to automatically deploy AWS CloudFormation templates to new AWS accounts. The specific CloudFormation template that the solution deploys must vary based on which organizational unit (OU) each new account is placed in.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
The requirement is to automatically apply different baseline CloudFormation templates based on OU placement when new AWS accounts are created, while keeping operational overhead as low as possible.
Because the company is already using AWS Organizations and is planning a comprehensive account management strategy, the most AWS-native and efficient solution is AWS Control Tower with Customizations for AWS Control Tower (CfCT) .
CfCT is specifically designed to extend Control Tower's baseline by allowing administrators to deploy OU- scoped CloudFormation templates automatically. The solution uses a manifest file to map CloudFormation templates to specific OUs, ensuring that each new account receives the correct baseline configuration immediately after provisioning. Templates and configuration are stored in a version-controlled Git repository, providing auditability, change tracking, and rollback capabilities.
Option B adds unnecessary operational complexity by introducing a custom CodePipeline that must be manually triggered and maintained. This duplicates functionality that CfCT already provides natively. Options C and D rely on custom Lambda logic and EventBridge rules, which increase maintenance burden, reduce transparency, and lack built-in OU-aware governance features.
AWS documentation explicitly recommends Customizations for AWS Control Tower for OU-based, scalable, and automated baseline deployments. Therefore, Option A delivers the required functionality with the least operational overhead and aligns with AWS best practices for multi-account governance.
NEW QUESTION # 249
A company is building a new pipeline by using AWS CodePipeline and AWS CodeBuild in a build account.
The pipeline consists of two stages. The first stage is a CodeBuild job to build and package an AWS Lambda function. The second stage consists of deployment actions that operate on two different AWS accounts a development environment account and a production environment account. The deployment stages use the AWS Cloud Format ion action that CodePipeline invokes to deploy the infrastructure that the Lambda function requires.
A DevOps engineer creates the CodePipeline pipeline and configures the pipeline to encrypt build artifacts by using the AWS Key Management Service (AWS KMS) AWS managed key for Amazon S3 (the aws/s3 key).
The artifacts are stored in an S3 bucket When the pipeline runs, the Cloud Formation actions fail with an access denied error.
Which combination of actions must the DevOps engineer perform to resolve this error? (Select TWO.)
Answer: A,E
NEW QUESTION # 250
A DevOps team manages infrastructure for an application. The application uses long-running processes to process items from an Amazon Simple Queue Service (Amazon SQS) queue. The application is deployed to an Auto Scaling group.
The application recently experienced an issue where items were taking significantly longer to process. The queue exceeded the expected size, which prevented various business processes from functioning properly. The application records all logs to a third-party tool.
The team is currently subscribed to an Amazon Simple Notification Service (Amazon SNS) topic that the team uses for alerts. The team needs to be alerted if the queue exceeds the expected size.
Which solution will meet these requirements with the MOST operational efficiency?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The Amazon SQS service publishes several standard CloudWatch metrics by default, including ApproximateNumberOfMessagesVisible, which represents the number of messages available for retrieval from the queue (i.e., the number of messages waiting to be processed). This is the primary metric to monitor queue backlog and processing delays.
Using ApproximateNumberOfMessagesVisible to monitor the visible messages in the queue gives a direct and near real-time indication of processing delays or backlog.
CloudWatch metrics are automatically collected and available without the need to create custom metrics or Lambda functions, which increases operational efficiency by reducing complexity and maintenance overhead.
Setting a CloudWatch alarm on this metric with a static threshold and a reasonable evaluation period (such as 1 hour) is sufficient to alert the team when the queue grows beyond an expected size.
The alarm can be directly configured to send notifications to an existing SNS topic, maintaining seamless integration with the team's alerting mechanisms.
The ApproximateNumberOfMessagesDelayed metric refers to messages that are delayed and not available for processing yet due to delay settings, which is not the direct backlog that causes business process delays.
Options C and D introduce unnecessary complexity by requiring Lambda functions and custom metrics or scheduled invocations, which reduce operational efficiency compared to using built-in CloudWatch metrics and alarms.
Reference from AWS Official Documentation and Study Guide:
Amazon SQS Monitoring with CloudWatch Metrics:
"Amazon SQS automatically sends metrics to CloudWatch, including ApproximateNumberOfMessagesVisible, which shows the number of messages available for retrieval." (Source: Amazon SQS Monitoring - AWS Documentation) Setting CloudWatch Alarms for SQS Queues:
"You can create CloudWatch alarms on SQS metrics such as ApproximateNumberOfMessagesVisible to receive notifications when the queue size exceeds a threshold." (Source: Amazon CloudWatch Alarms - AWS Documentation) AWS DevOps Engineer Professional Exam Guide:
"Efficient alerting for queue backlogs should leverage native CloudWatch metrics to minimize operational overhead." (Source: Official AWS Certified DevOps Engineer Professional Study Guide)
NEW QUESTION # 251
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.)
Answer: A,E,F
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 magnetic store. 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, which would 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 retention periods appropriately, you can balance your storage costs and query performance according to your application needs3.
References:
1: Batch writes
2: Multi-measure records vs. single-measure records
3: Storage
NEW QUESTION # 252
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