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NEW QUESTION 26
An organization has two Amazon EC2 instances:
The first is running an ordering application and an inventory application.
The second is running a queuing system.
During certain times of the year, several thousand orders are placed per second. Some orders were lost when the queuing system was down. Also, the organization's inventory application has the incorrect quantity of products because some orders were processed twice.
What should be done to ensure that the applications can handle the increasing number of orders?

  • A. Put the ordering and inventory applications into their own Amazon EC2 instances, and create an Auto Scaling group for each application. Use Amazon SQS standard queues for the incoming orders, and implement idempotency in the inventory application.
  • B. Put the ordering and inventory applications into their own Amazon ECS containers and create an Auto Scaling group for each application. Then, deploy the message queuing server in multiple Availability Zones.
  • C. Put the ordering and inventory applications into their own Amazon EC2 instances. Write the incoming orders to an Amazon Kinesis data stream Configure AWS Lambda to poll the stream and update the inventory application.
    https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/standard-queues.html
  • D. Put the ordering and inventory applications into their own AWS Lambda functions. Have the ordering application write the messages into an Amazon SQS FIFO queue.

Answer: A

 

NEW QUESTION 27
An enterprise company's data science team wants to provide a safe, cost-effective way to provide easy access to Amazon SageMaker. The data scientists have limited AWS knowledge and need to be able to launch a Jupyter notebook instance. The notebook instance needs to have a preconfigured AWS KMS key to encrypt data at rest on the machine learning storage volume without exposing the complex setup requirements.
Which approach will allow the company to set up a self-service mechanism for the data scientists to launch Jupyter notebooks in its AWS accounts with the LEAST amount of operational overhead?

  • A. Create an AWS CLI script that the data scientists can run locally. Provide step-by-step instructions about the parameters to be provided while executing the AWS CLI script to launch a Jupyter notebook with a preconfigured KMS key. Distribute the CLI script to the data scientists using a shared Amazon S3 bucket.
  • B. Create an AWS CloudFormation template to launch a Jupyter notebook instance using the AWS::SaqeMaker::Notebooklnstance resource type with a preconfigured KMS key. Add a user-friendly name to the CloudFormation template. Display the URL to the notebook using the Outputs section. Distribute the CloudFormation template to the data scientists using a shared Amazon S3 bucket.
  • C. Create an AWS CloudFormation template to launch a Jupyter notebook instance using the AWS::SageMaker::Notebooklnstance resource type with a preconfigured KMS key. Simplify the parameter names, such as the instance size, by mapping them to Small, Large, and X-Large using the Mappings section in CloudFormation. Display the URL to the notebook using the Outputs section, then upload the template into an AWS Service Catalog product in the data scientist's portfolio, and share it with the data scientist's IAM role.
  • D. Create a serverless front end using a static Amazon S3 website to allow the data scientists to request a Jupyter notebook instance by filling out a form. Use Amazon API Gateway to receive requests from the S3 website and trigger a central AWS Lambda function to make an API call to Amazon SageMaker that launch a notebook instance with a preconfigured KMS key for the data scientists. Then call back to the front-end website to display the URL to the notebook instance.

Answer: C

Explanation:
https://aws.amazon.com/blogs/mt/enable-self-service-secured-data-science-using-amazon-sagemaker-notebooks-and-aws-service-catalog/

 

NEW QUESTION 28
A company has an application that uses Amazon EC2 instances in an Auto Scaling group. The Quality Assurance (QA) department needs to launch a large number of short-lived environments to test the application.
The application environments are currently launched by the Manager of the department using an AWS CloudFormation template. To launch the stack, the Manager uses a role with permission to use CloudFormation, EC2 and Auto Scaling APIs. The Manager wants to allow testers to launch their own environments, but does not want to grant broad permission to each user. Which set up would achieve these goals?

  • A. Create an AWS Elastic Beanstalk application from the environment template. Give users in the QA department permission to use Elastic Beanstalk permissions only. Train users to launch Elastic beanstalk environments with the Elastic Beanstalk CLI, passing the existing role to the environment as a service role.
  • B. Create an AWS Service Catalog product form the environment template. Add a launch constraint to the product with the existing role. Give users in the QA department permission to use AWS Service Catalog APIs only. Train users to launch the templates form the AWS Service Catalog console.
  • C. Upload the AWS CloudFormation template to Amazon S3. Give users in the QA department permission to use CloudFormation and S3 APIs, with conditions that restrict the permission to the template and the resources it creates. Train users to launch the template form the CloudFormation console.
  • D. Upload the AWS CloudFormation template to Amazon S3. Give users in the QA department permission to assume the Manager's role and add a policy that restricts the permissions to the template and the resources it creates. Train users to launch the template from the CloudFormation console.

Answer: B

Explanation:
Explanation
https://aws.amazon.com/blogs/mt/how-to-launch-secure-and-governed-aws-resources-with-aws-cloudformation-

 

NEW QUESTION 29
A three-tier web application runs on Amazon EC2 instances. Cron daemons are used to trigger scripts that collect the web server, application, and database logs and send them to a centralized location every hour.
Occasionally, scaling events or unplanned outages have caused the instances to stop before the latest logs were collected, and the log files were lost.
Which of the following options is the MOST reliable way of collecting and preserving the log files?

  • A. Use Amazon CloudWatch Events to trigger AWS Lambda to SSH into each running instance and invoke the log collection scripts more frequently to reduce the possibility of log messages being lost in an outage.
  • B. Use Amazon CloudWatch Events to trigger Amazon Systems Manager Run Command to invoke the log collection scripts more frequently to reduce the possibility of log messages being lost in an outage.
  • C. Update the cron jobs to run every 5 minutes instead of every hour to reduce the possibility of log messages being lost in an outage.
  • D. Use the Amazon CloudWatch Logs agent to stream log messages directly to CloudWatch Logs.
    Configure the agent with a batch count of 1 to reduce the possibility of log messages being lost in an outage.

Answer: D

 

NEW QUESTION 30
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