AWS-DevOps-Engineer-Professional Exam Question 26
You have been requested to use CloudFormation to maintain version control and achieve automation for the
applications in your organization. How can you best use CloudFormation to keep everything agile and
maintain multiple environments while keeping cost down?
applications in your organization. How can you best use CloudFormation to keep everything agile and
maintain multiple environments while keeping cost down?
AWS-DevOps-Engineer-Professional Exam Question 27
A company has microservices running in AWS Lambda that read data from Amazon DynamoDB. The Lambda code is manually deployed by Developers after successful testing. The company now needs the tests and deployments be automated and run in the cloud. Additionally, traffic to the new versions of each microservice should be incrementally shifted over time after deployment. What solution meets all the requirements, ensuring the MOST developer velocity?
AWS-DevOps-Engineer-Professional Exam Question 28
You need to perform ad-hoc analysis on log data, including searching quickly for specific error codes and reference numbers. Which should you evaluate first?
AWS-DevOps-Engineer-Professional Exam Question 29
A company runs a three-tier web application in its production environment, which is built on a single AWS CloudFormation template made up of Amazon EC2 instances behind an ELB Application Load Balancer. The instances run in an EC2 Auto Scaling group across multiple Availability Zones. Data is stored in an Amazon RDS Multi-AZ DB instance with read replicas. Amazon Route 53 manages the application's public DNS record. A DevOps Engineer must create a workflow to mitigate a failed software deployment by rolling back changes in the production environment when a software cutover occurs for new application software. What steps should the Engineer perform to meet these requirements with the LEAST amount of downtime?
AWS-DevOps-Engineer-Professional Exam Question 30
You work for a company that automatically tags photographs using artificial neural networks (ANNs), which run on GPUs using C++. You receive millions of images at a time, but only 3 times per day on average. These images are loaded into an AWS S3 bucket you control for you in a batch, and then the customer publishes a JSON-formatted manifest into another S3 bucket you control as well. Each image takes 10 milliseconds to process using a full GPU. Your neural network software requires 5 minutes to bootstrap. Image tags are JSON objects, and you must publish them to an S3 bucket. Which of these is the best system architectures for this system?
