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To become an AWS Certified DevOps Engineer - Professional, candidates must possess a strong understanding of AWS services, such as Elastic Compute Cloud (EC2), Elastic Beanstalk, and Amazon Simple Storage Service (S3), as well as proficiency in continuous integration and continuous delivery (CI/CD) practices. The DOP-C02 Exam consists of 75 multiple-choice and multiple-response questions, and candidates have 180 minutes to complete the exam. The passing score for this certification is 750 out of 1000.

The AWS Certified DevOps Engineer - Professional certification exam is a valuable credential for professionals in DevOps roles who work with AWS. It validates their understanding of key concepts and best practices for implementing and managing DevOps practices on AWS, and demonstrates their ability to design, deploy, and operate scalable and secure systems.

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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q139-Q144):

NEW QUESTION # 139
A company's application has an API that retrieves workload metrics. The company needs to audit, analyze, and visualize these metrics from the application to detect issues at scale.
Which combination of steps will meet these requirements? (Select THREE).

Answer: C,D,F

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The recommended architecture to audit, analyze, and visualize application workload metrics at scale involves:
Using Amazon EventBridge to schedule AWS Lambda invocations that call the application API and fetch metrics (Option A). The data is stored in Amazon S3, which is ideal for scalable, cost-effective storage of large datasets.
Cataloging the stored data with AWS Glue crawlers, enabling schema discovery and making data queryable via Amazon Athena (Option C).
Visualizing the data by creating Amazon QuickSight datasets from Athena views and building dashboards for analysis (Option E).
Option B and D introduce DynamoDB, which is less suitable for large-scale analytics and Athena querying. Option F suggests querying Athena via Lambda widgets in CloudWatch, which adds complexity without significant benefit over QuickSight.
Reference:
Serverless Analytics Architecture on AWS:
"Use Lambda + S3 + Glue + Athena + QuickSight for scalable data ingestion, cataloging, querying, and visualization." (AWS Analytics Reference Architecture) AWS Glue Crawlers and Athena:
"Glue crawlers catalog data stored in S3 and create Athena tables to enable SQL querying." (AWS Glue Documentation) Amazon QuickSight for Visualization:
"QuickSight enables creating interactive dashboards from Athena query results." (Amazon QuickSight Overview)


NEW QUESTION # 140
A company has an application that runs on AWS Lambda and sends logs to Amazon CloudWatch Logs. An Amazon Kinesis data stream is subscribed to the log groups in CloudWatch Logs. A single consumer Lambda function processes the logs from the data stream and stores the logs in an Amazon S3 bucket.
The company's DevOps team has noticed high latency during the processing and ingestion of some logs.
Which combination of steps will reduce the latency? (Select THREE.)

Answer: B,C,F

Explanation:
The latency in processing and ingesting logs can be caused by several factors, such as the throughput of the Kinesis data stream, the concurrency of the Lambda function, and the configuration of the event source mapping. To reduce the latency, the following steps can be taken:
Create a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer. This will allow the Lambda function to receive records from the data stream with dedicated throughput of up to 2 MB per second per shard, independent of other consumers1. This will reduce the contention and delay in accessing the data stream.
Increase the ParallelizationFactor setting in the Lambda event source mapping. This will allow the Lambda service to invoke more instances of the function concurrently to process the records from the data stream2.
This will increase the processing capacity and reduce the backlog of records in the data stream.
Configure reserved concurrency for the Lambda function that processes the logs. This will ensure that the function has enough concurrency available to handle the increased load from the data stream3. This will prevent the function from being throttled by the account-level concurrency limit.
The other options are not effective or may have negative impacts on the latency. Option D is not suitable because increasing the batch size in the Kinesis data stream will increase the amount of data that the Lambda function has to process in each invocation, which may increase the execution time and latency4. Option E is not advisable because turning off the ReportBatchItemFailures setting in the Lambda event source mapping will prevent the Lambda service from retrying the failed records, which may result in data loss. Option F is not necessary because increasing the number of shards in the Kinesis data stream will increase the throughput of the data stream, but it will not affect the processing speed of the Lambda function, which is the bottleneck in this scenario.
1: Using AWS Lambda with Amazon Kinesis Data Streams - AWS Lambda
2: AWS Lambda event source mappings - AWS Lambda
3: Managing concurrency for a Lambda function - AWS Lambda
4: AWS Lambda function scaling - AWS Lambda
AWS Lambda event source mappings - AWS Lambda
Scaling Amazon Kinesis Data Streams with AWS CloudFormation - Amazon Kinesis Data Streams


NEW QUESTION # 141
A company has an application that uses an Amazon API Gateway REST API, AWS Lambda functions, and an Amazon DynamoDB table. The application currently runs in a single AWS Region. The company wants to make the application highly available across two Regions. User traffic must be routed to the Region that provides the least latency.
Which combination of steps will meet these requirements? (Select THREE.)

Answer: B,C,E

Explanation:
To achieve multi-Region high availability for a serverless API-based application, the architecture must be duplicated in the second Region and supported by a multi-Region data layer and appropriate DNS routing.
Option A provides the required multi-Region data layer using DynamoDB global tables, which replicate data automatically and asynchronously between Regions. This allows reads and writes in either Region and prevents cross-Region latency bottlenecks.
Option C ensures that the compute components-API Gateway REST API and AWS Lambda functions-are deployed in both Regions. Each Region operates independently, providing true active-active redundancy and allowing Route 53 to route traffic directly to either Region without dependency on the other.
Option E selects Route 53 latency-based routing, which directs each user to the Region with the lowest round- trip latency. This meets the requirement to route traffic based on the Region providing the best response time rather than only providing active-passive failover.
Option D (failover routing) supports active-standby topologies, not active-active. Option B creates a global secondary index, which does not provide multi-Region replication. Option F (multivalue routing) distributes traffic randomly and does not account for latency or Region placement.
Thus, combining DynamoDB global tables, duplicated serverless architecture, and latency-based routing fully satisfies the requirements.


NEW QUESTION # 142
A DevOps engineer has implemented a Cl/CO pipeline to deploy an AWS Cloud Format ion template that provisions a web application. The web application consists of an Application Load Balancer (ALB) a target group, a launch template that uses an Amazon Linux 2 AMI an Auto Scaling group of Amazon EC2 instances, a security group and an Amazon RDS for MySQL database The launch template includes user data that specifies a script to install and start the application.
The initial deployment of the application was successful. The DevOps engineer made changes to update the version of the application with the user data. The CI/CD pipeline has deployed a new version of the template However, the health checks on the ALB are now failing The health checks have marked all targets as unhealthy.
During investigation the DevOps engineer notices that the Cloud Formation stack has a status of UPDATE_COMPLETE. However, when the DevOps engineer connects to one of the EC2 instances and checks /varar/log messages, the DevOps engineer notices that the Apache web server failed to start successfully because of a configuration error How can the DevOps engineer ensure that the CloudFormation deployment will fail if the user data fails to successfully finish running?

Answer: A

Explanation:
Explanation
https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-attribute-updatepolicy.html


NEW QUESTION # 143
A company has an application that runs on AWS Lambda and sends logs to Amazon CloudWatch Logs. An Amazon Kinesis data stream is subscribed to the log groups in CloudWatch Logs. A single consumer Lambda function processes the logs from the data stream and stores the logs in an Amazon S3 bucket.
The company's DevOps team has noticed high latency during the processing and ingestion of some logs.
Which combination of steps will reduce the latency? (Select THREE.)

Answer: A,B,C

Explanation:
The latency in processing and ingesting logs can be caused by several factors, such as the throughput of the Kinesis data stream, the concurrency of the Lambda function, and the configuration of the event source mapping. To reduce the latency, the following steps can be taken:
Create a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer. This will allow the Lambda function to receive records from the data stream with dedicated throughput of up to 2 MB per second per shard, independent of other consumers1. This will reduce the contention and delay in accessing the data stream.
Increase the ParallelizationFactor setting in the Lambda event source mapping. This will allow the Lambda service to invoke more instances of the function concurrently to process the records from the data stream2. This will increase the processing capacity and reduce the backlog of records in the data stream.
Configure reserved concurrency for the Lambda function that processes the logs. This will ensure that the function has enough concurrency available to handle the increased load from the data stream3. This will prevent the function from being throttled by the account-level concurrency limit.
The other options are not effective or may have negative impacts on the latency. Option D is not suitable because increasing the batch size in the Kinesis data stream will increase the amount of data that the Lambda function has to process in each invocation, which may increase the execution time and latency4. Option E is not advisable because turning off the ReportBatchItemFailures setting in the Lambda event source mapping will prevent the Lambda service from retrying the failed records, which may result in data loss. Option F is not necessary because increasing the number of shards in the Kinesis data stream will increase the throughput of the data stream, but it will not affect the processing speed of the Lambda function, which is the bottleneck in this scenario.
Reference:
1: Using AWS Lambda with Amazon Kinesis Data Streams - AWS Lambda
2: AWS Lambda event source mappings - AWS Lambda
3: Managing concurrency for a Lambda function - AWS Lambda
4: AWS Lambda function scaling - AWS Lambda
: AWS Lambda event source mappings - AWS Lambda
: Scaling Amazon Kinesis Data Streams with AWS CloudFormation - Amazon Kinesis Data Streams


NEW QUESTION # 144
......

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