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| Section | Weight | Objectives |
|---|---|---|
| SDLC Automation | 22% | - CI/CD pipeline design and implementation
|
| Incident and Event Management | 18% | - Operational response and recovery
|
| Monitoring and Logging | 15% | - Observability and metrics
|
| Security and Compliance Automation | 13% | - Security automation in CI/CD and infrastructure
|
| Resilient Cloud Solutions | 15% | - High availability and fault tolerance design
|
| Configuration Management and Infrastructure as Code | 17% | - Infrastructure provisioning and automation
|
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NEW QUESTION # 366
A company has a single developer writing code for an automated deployment pipeline. The developer is storing source code in an Amazon S3 bucket for each project. The company wants to add more developers to the team but is concerned about code conflicts and lost work The company also wants to build a test environment to deploy newer versions of code for testing and allow developers to automatically deploy to both environments when code is changed in the repository.
What is the MOST efficient way to meet these requirements?
Answer: A
Explanation:
Creating an AWS CodeCommit repository for each project, using the main branch for production code, and creating a testing branch for code deployed to testing will meet the requirements. AWS CodeCommit is a managed revision control service that hosts Git repositories and works with all Git-based tools1. By using feature branches to develop new features and pull requests to merge code to testing and main branches, the developers can avoid code conflicts and lost work, and also implement code reviews and approvals. Option B is incorrect because creating another S3 bucket for each project for testing code and using an AWS Lambda function to promote code changes between testing and production buckets will not provide the benefits of revision control, such as tracking changes, branching, merging, and collaborating. Option C is incorrect because using the main branch for production and test code with different deployment pipelines for each environment will not allow the developers to test their code changes before deploying them to production. Option D is incorrect because enabling versioning and branching on each S3 bucket will not work with Git-based tools and will not provide the same level of revision control as AWS CodeCommit. Reference:
AWS CodeCommit
Certified DevOps Engineer - Professional (DOP-C02) Study Guide (page 182)
NEW QUESTION # 367
A company has an AWS CodePipeline pipeline that is configured with an Amazon S3 bucket in the eu-west-1 Region. The pipeline deploys an AWS Lambda application to the same Region. The pipeline consists of an AWS CodeBuild project build action and an AWS CloudFormation deploy action.
The CodeBuild project uses the aws cloudformation package AWS CLI command to build an artifact that contains the Lambda function code's .zip file and the CloudFormation template. The CloudFormation deploy action references the CloudFormation template from the output artifact of the CodeBuild project's build action.
The company wants to also deploy the Lambda application to the us-east-1 Region by using the pipeline in eu-west-1. A DevOps engineer has already updated the CodeBuild project to use the aws cloudformation package command to produce an additional output artifact for us-east-1.
Which combination of additional steps should the DevOps engineer take to meet these requirements? (Choose two.)
Answer: A,D
Explanation:
A) The CloudFormation template should be modified to include a parameter that indicates the location of the .zip file containing the Lambda function's code. This allows the CloudFormation deploy action to use the correct artifact depending on the region. This is critical because Lambda functions need to reference their code artifacts from the same region they are being deployed in.
B) You would also need to create a new CloudFormation deploy action for the us-east-1 Region within the pipeline. This action should be configured to use the CloudFormation template from the artifact that was specifically created for us-east-1.
NEW QUESTION # 368
A security team wants to use AWS CloudTrail to monitor all actions and API calls in multiple accounts that are in the same organization in AWS Organizations. The security team needs to ensure that account users cannot turn off CloudTrail in the accounts.
Which solution will meet this requirement?
Answer: D
Explanation:
To ensure that CloudTrail cannot be disabled in any member account, the control must be enforced above the individual accounts, at the organization level, and must not be overridable by local administrators. AWS Service Control Policies (SCPs) provide exactly this capability: they define permission guardrails that apply to all IAM users, roles, and even the root user in accounts within an AWS Organizations OU.
By applying an SCP that explicitly denies cloudtrail:StopLogging and cloudtrail:DeleteTrail, the organization prevents any principal in the affected accounts from disabling logging or deleting the trails, regardless of their IAM permissions. This provides strong, centralized enforcement of audit logging.
Option B uses IAM policies in each account. Local administrators with sufficient privileges could modify or detach those policies, defeating the control. Option C only sends notifications and does not prevent the action. Option D with AWS Config auto-remediation still allows a window where CloudTrail is disabled and also can be disabled or modified itself.
Therefore, the most robust and least bypassable approach is to use an SCP at the OU level to deny CloudTrail stop and delete actions.
NEW QUESTION # 369
A company used a lift-and-shift strategy to migrate a workload to AWS. The company has an Auto Scaling group of Amazon EC2 instances. Each EC2 instance runs a web application, a database, and a Redis cache.
Users are experiencing large variations in the web application's response times. Requests to the web application go to a single EC2 instance that is under significant load. The company wants to separate the application components to improve availability and performance.
Which solution will meet these requirements?
Answer: B
Explanation:
The current design colocates the web tier, database, and cache on the same EC2 instances. This causes resource contention and makes it difficult to scale each layer independently. The correct solution is to separate these concerns into independently scalable managed services.
Option D uses an Application Load Balancer (ALB) in front of an Auto Scaling group for the web application. ALB is designed for HTTP/HTTPS traffic, supports path-based and host-based routing, and can spread load evenly across multiple instances and Availability Zones, eliminating the issue where one instance is overloaded.
The database is migrated to Amazon Aurora with Multi-AZ, which provides high availability, automated failover, and managed backups. Aurora offloads database management and ensures consistent performance.
For caching, Option D introduces Amazon ElastiCache for Redis, a managed in-memory cache that is purpose-built for low-latency reads, independent scaling, and high availability through replication and clustering.
Option A and C misuse NLB for HTTP traffic or for Redis exposure and introduce unnecessary complexity. Option B keeps Redis on EC2 (via NLB + ASG in a single AZ), which reduces availability and does not leverage managed caching.
Thus, Option D best separates tiers and improves performance and availability.
NEW QUESTION # 370
A company releases a new application in a new AWS account. The application includes an AWS Lambda function that processes messages from an Amazon Simple Queue Service (Amazon SOS) standard queue. The Lambda function stores the results in an Amazon S3 bucket for further downstream processing. The Lambda function needs to process the messages within a specific period of time after the messages are published. The Lambda function has a batch size of 10 messages and takes a few seconds to process a batch of messages.
As load increases on the application's first day of service, messages in the queue accumulate at a greater rate than the Lambda function can process the messages. Some messages miss the required processing timelines. The logs show that many messages in the queue have data that is not valid. The company needs to meet the timeline requirements for messages that have valid data.
Which solution will meet these requirements?
Answer: D
Explanation:
Step 1: Handling Invalid Data with Failed Batch ItemsThe Lambda function is processing batches of messages, and some messages contain invalid data, causing processing delays. Lambda provides the capability to report failed batch items, which allows valid messages to be processed while skipping invalid ones. This functionality ensures that the valid messages are processed within the required timeline.
Action: Keep the Lambda function's batch size the same and configure it to report failed batch items.
Why: By reporting failed batch items, the Lambda function can skip invalid messages and continue processing valid ones, ensuring that they meet the processing timeline.
Reference:
Step 2: Using an SQS Dead-Letter Queue (DLQ)Configuring a dead-letter queue (DLQ) for SQS will ensure that messages with invalid data, or those that cannot be processed successfully, are moved to the DLQ. This prevents such messages from clogging the queue and allows the system to focus on processing valid messages.
Action: Configure an SQS dead-letter queue for the main queue.
Why: A DLQ helps isolate problematic messages, preventing them from continuously reappearing in the queue and causing processing delays for valid messages.
Step 3: Maintaining the Lambda Function's Batch SizeKeeping the current batch size allows the Lambda function to continue processing multiple messages at once. By addressing the failed items separately, there's no need to increase or reduce the batch size.
Action: Maintain the Lambda function's current batch size.
Why: Changing the batch size is unnecessary if the invalid messages are properly handled by reporting failed items and using a DLQ.
This corresponds to Option D: Keep the Lambda function's batch size the same. Configure the Lambda function to report failed batch items. Configure an SQS dead-letter queue.
NEW QUESTION # 371
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