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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: SDLC Automation | 22% | - CI/CD pipeline design and implementation
|
| Topic 2: Resilient Cloud Solutions | 15% | - High availability and fault tolerance design
|
| Topic 3: Incident and Event Management | 18% | - Operational response and recovery
|
| Topic 4: Security and Compliance Automation | 13% | - Security automation in CI/CD and infrastructure
|
| Topic 5: Configuration Management and Infrastructure as Code | 17% | - Infrastructure provisioning and automation
|
| Topic 6: Monitoring and Logging | 15% | - Observability and metrics
|
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NEW QUESTION # 81
A company uses AWS CloudFormation stacks to deploy updates to its application. The stacks consist of different resources. The resources include AWS Auto Scaling groups, Amazon EC2 instances, Application Load Balancers (ALBs), and other resources that are necessary to launch and maintain independent stacks. Changes to application resources outside of CloudFormation stack updates are not allowed.
The company recently attempted to update the application stack by using the AWS CLI. The stack failed to update and produced the following error message: "ERROR: both the deployment and the CloudFormation stack rollback failed. The deployment failed because the following resource(s) failed to update: [AutoScalingGroup]." The stack remains in a status of UPDATE_ROLLBACK_FAILED. * Which solution will resolve this issue?
Answer: C
NEW QUESTION # 82
A DevOps engineer is building a multistage pipeline with AWS CodePipeline to build, verify, stage, test, and deploy an application. A manual approval stage is required between the test stage and the deploy stage. The development team uses a custom chat tool with webhook support that requires near-real-time notifications.
How should the DevOps engineer configure status updates for pipeline activity and approval requests to post to the chat tool?
Answer: C
Explanation:
https://aws.amazon.com/premiumsupport/knowledge-center/sns-lambda-webhooks-chime-slack-teams/
NEW QUESTION # 83
A company uses AWS CloudFormation to deploy application environments. A deployment failed due to manual modifications in stack resources. The DevOps engineer wants to detect manual modifications and alert the DevOps lead with the least effort.
Which solution meets these requirements?
Answer: D
Explanation:
AWS Config offers a managed rule called CLOUDFORMATION_STACK_DRIFT_DETECTION_CHECK to detect stack drift automatically.
* Creating an SNS topic and subscribing the DevOps lead enables simple notification without custom Lambda functions.
* An EventBridge rule that triggers on NON_COMPLIANT status (when drift is detected) can send alerts directly to SNS.Option B adds complexity by requiring custom rules and Lambda. Option C incorrectly triggers on COMPLIANT, not NON_COMPLIANT. Option D needs a Lambda for email, while SNS can send email directly.Hence, Option A offers the least operational effort.
References:
AWS Config Drift Detection
AWS Config Managed Rules
NEW QUESTION # 84
A company recently launched multiple applications that use Application Load Balancers. Application response time often slows down when the applications experience problems A DevOps engineer needs to Implement a monitoring solution that alerts the company when the applications begin to perform slowly The DevOps engineer creates an Amazon Simple Notification Semce (Amazon SNS) topic and subscribe the company's email address to the topic What should the DevOps engineer do next to meet the requirements?
Answer: D
Explanation:
Option A is incorrect because creating an Amazon EventBridge rule that invokes an AWS Lambda function to query the applications on a 5-minute interval is not a valid solution. EventBridge rules can only trigger Lambda functions based on events, not on time intervals. Moreover, querying the applications on a 5-minute interval might incur unnecessary costs and network overhead, and might not detect performance issues in real time.
Option B is correct because creating an Amazon CloudWatch Synthetics canary that runs a custom script to query the applications on a 5-minute interval is a valid solution. CloudWatch Synthetics canaries are configurable scripts that monitor endpoints and APIs by simulating customer behavior. Canaries can run as often as once per minute, and can measure the latency and availability of the applications. Canaries can also send notifications to an Amazon SNS topic when they detect errors or performance issues1.
Option C is incorrect because creating an Amazon CloudWatch alarm that uses the AWS/ApplicationELB namespace RequestCountPerTarget metric is not a valid solution. The RequestCountPerTarget metric measures the number of requests completed or connections made per target in a target group2. This metric does not reflect the application response time, which is the requirement. Moreover, configuring the CloudWatch alarm to send a notification when the number of connections becomes greater than the configured number of threads that the application supports is not a valid way to measure the application performance, as it depends on the application design and implementation.
Option D is incorrect because creating an Amazon CloudWatch alarm that uses the AWS/ApplicationELB namespace RequestCountPerTarget metric is not a valid solution, for the same reason as option C. The RequestCountPerTarget metric does not reflect the application response time, which is the requirement.
Moreover, configuring the CloudWatch alarm to send a notification when the average response time becomes greater than the longest response time that the application supports is not a valid way to measure the application performance, as it does not account for variability or outliers in the response time distribution.
References:
1: Using synthetic monitoring
2: Application Load Balancer metrics
NEW QUESTION # 85
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,D
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
5: AWS Lambda event source mappings - AWS Lambda
6: Scaling Amazon Kinesis Data Streams with AWS CloudFormation - Amazon Kinesis Data Streams
NEW QUESTION # 86
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