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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.
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Amazon DOP-C02 exam is one of the most sought-after certifications for professionals in the field of DevOps. It is a professional-level certification that is intended for individuals who are already working in the field of DevOps and have extensive experience in deploying, operating, and managing AWS environments. The DOP-C02 exam is designed to test the candidate's knowledge and skills in designing, managing, and operating AWS environments at a professional level.
Amazon DOP-C02 exam is a professional-level certification for those who want to validate their expertise in the field of DevOps. AWS Certified DevOps Engineer - Professional certification is intended for experienced DevOps engineers, developers, and system administrators who want to demonstrate their proficiency in designing, deploying, and managing highly available, scalable, and fault-tolerant systems on the AWS platform. DOP-C02 Exam measures the candidate's ability to design and manage continuous delivery systems and methodologies on AWS, implement and manage highly available and scalable systems, and automate operational processes.
Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q171-Q176):
NEW QUESTION # 171
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?
- A. Create a resource policy on the Lambda function to grant Amazon S3 the permission to invoke the Lambda function for the S3 bucket
- B. Provision space in the /tmp folder of the Lambda function to give the function the ability to process large files from the S3 bucket
- C. Increase the memory of the Lambda function to give the function the ability to process large files from the S3 bucket.
- D. Configure an Amazon Simple Queue Service (Amazon SQS) queue as an OnFailure destination for the Lambda function
Answer: A
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 underwhat 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 # 172
A company has multiple AWS accounts. The company uses AWS IAM Identity Center (AWS Single Sign-On) that is integrated with AWS Toolkit for Microsoft Azure DevOps. The attributes for access control feature is enabled in IAM Identity Center.
The attribute mapping list contains two entries. The department key is mapped to
${path:enterprise.department}. The costCenter key is mapped to ${path:enterprise.costCenter}.
All existing Amazon EC2 instances have a department tag that corresponds to three company departments (d1, d2, d3). A DevOps engineer must create policies based on the matching attributes. The policies must minimize administrative effort and must grant each Azure AD user access to only the EC2 instances that are tagged with the user's respective department name.
Which condition key should the DevOps engineer include in the custom permissions policies to meet these requirements?
- A.
- B. <e ip="img_142.jpg"></e>b
- C.
- D.
Answer: B
Explanation:
Explanation
https://docs.aws.amazon.com/singlesignon/latest/userguide/configure-abac.html
NEW QUESTION # 173
A company is using an AWS CodeBuild project to build and package an application. The packages are copied to a shared Amazon S3 bucket before being deployed across multiple AWS accounts.
The buildspec.yml file contains the following:
The DevOps engineer has noticed that anybody with an AWS account is able to download the artifacts.
What steps should the DevOps engineer take to stop this?
- A. Modify the post_build command to use --acl public-read and configure a bucket policy that grants read access to the relevant AWS accounts only.
- B. Create an S3 bucket policy that grants read access to the relevant AWS accounts and denies read access to the principal "*".
- C. Modify the post_build command to remove --acl authenticated-read and configure a bucket policy that allows read access to the relevant AWS accounts only.
- D. Configure a default ACL for the S3 bucket that defines the set of authenticated users as the relevant AWS accounts only and grants read-only access.
Answer: C
Explanation:
When setting the flag authenticated-read in the command line, the owner gets FULL_CONTROL. The AuthenticatedUsers group (Anyone with an AWS account) gets READ access. Reference: https://docs.aws.
amazon.com/AmazonS3/latest/userguide/acl-overview.html
NEW QUESTION # 174
A company's production environment uses an AWS CodeDeploy blue/green deployment to deploy an application. The deployment incudes Amazon EC2 Auto Scaling groups that launch instances that run Amazon Linux 2.
A working appspec. ymi file exists in the code repository and contains the following text.
A DevOps engineer needs to ensure that a script downloads and installs a license file onto the instances before the replacement instances start to handle request traffic. The DevOps engineer adds a hooks section to the appspec. yml file.
Which hook should the DevOps engineer use to run the script that downloads and installs the license file?
- A. BeforeBlockTraffic
- B. AfterBlockTraffic
- C. Beforelnstall
- D. Down load Bundle
Answer: C
Explanation:
This hook runs before the new application version is installed on the replacement instances. This is the best place to run the script because it ensures that the license file is downloaded and installed before the replacement instances start to handle request traffic. If you use any other hook, you may encounter errors or inconsistencies in your application.
NEW QUESTION # 175
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. Configure the memory store retention period to be shorter than the magnetic store retention period
- C. Write each log event as a single write operation
- D. Configure the memory store retention period to be longer than the magnetic store retention period
- E. Treat each log as a single-measure record
- F. Treat each log as a multi-measure record
Answer: A,B,F
Explanation:
The correct answer is A, D, and F)
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.
Reference:
1: Batch writes
2: Multi-measure records vs. single-measure records
3: Storage
NEW QUESTION # 176
......
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