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Planning and Administering Microsoft Azure for SAP Workloads

  • Exam Number/Code : AZ-120
  • Exam Name : Planning and Administering Microsoft Azure for SAP Workloads
  • Questions and Answers : 213 Q&As
  • Update Time: 2019-01-10
  • Price: $ 99.00 $ 39.00

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NEW QUESTION: 1
Two administrators Dave and Jon both manage R80 Management as administrators for ABC Corp. Jon logged into the R80 Management and then shortly after Dave logged in to the same server. They are both in the Security Policies view. From the screenshots below, why does Dave not have the rule no.6 in his SmartConsole view even though Jon has it his in his SmartConsole view?

A. Dave is currently editing rule no.6 and has marked this rule for deletion.
B. Dave is currently editing rule no.6 and has deleted it from his Rule Base.
C. Jon is currently editing rule no.6 but has Published part of his changes.
D. Jon is currently editing rule no.6 but has not yet Published his changes.
Answer: D
Explanation:
When an administrator logs in to the Security Management Server through SmartConsole, a new editing session starts. The changes that the administrator makes during the session are only available to that administrator. Other administrators see a lock icon on object and rules that are being edited. To make changes available to all administrators, and to unlock the objects and rules that are being edited, the administrator must publish the session.

NEW QUESTION: 2

A. Option D
B. Option B
C. Option A
D. Option C
Answer: D

NEW QUESTION: 3
A Solutions Architect is responsible for redesigning a legacy Java application to improve its availability, data durability, and scalability. Currently, the application runs on a single high-memory Amazon EC2 instance. It accepts HTTP requests from upstream clients, adds them to an in-memory queue, and responds with a 200 status. A separate application thread reads items from the queue, processes them, and persists the results to an Amazon RDS MySQL instance. The processing time for each item takes 90 seconds on average., most of which is spent waiting on external service calls, but the application is written to process multiple items in parallel.
Traffic to this service is unpredictable. During periods of high load, items may sit in the internal queue for over an hour while the application processes the backing. In addition, the current system has issues with availability and data if the single application node fails.
Clients that access this service cannot be modified. They expect to receive a response to each HTTP request they send within 10 seconds before they will time out and retry the request.
Which approach would improve the availability and durability of the system while decreasing the processing latency and minimizing costs?
A. Update the application to use a Redis task queue instead of the in-memory queue. Build a Docker container image for the application. Create an Amazon ECS task definition that includes the application container and a separate container to host Redis. Deploy the new task definition as an ECS service using AWS Fargate and enable Auto Scaling.
B. Modify the application to use Amazon DynamoDB instead of Amazon RDS. Configure Auto Scaling for the DynamoDB table. Deploy the application within an Auto Scaling group with a scaling policy based on CPU utilization. Back the in-memory queue with a memory-mapped file to an instance store volume and periodically write that file to Amazon S3.
C. Create an Amazon API Gateway REST API that uses Lambda proxy integration to pass requests to an AWS Lambda function. Migrate the core processing code to a Lambda function and write a wrapper class that provides a handler method that converts the proxy events to the internal application data model and invokes the processing module.
D. Create an Amazon API Gateway REST API that uses a service proxy to put items in an Amazon SQS queue. Extract the core processing code from the existing application and update it to pull items from Amazon SQS queue. Extract the core processing code from the existing application and update it to pull items from Amazon SQS instead of an in-memory queue. Deploy the new processing application to smaller EC2 instances within an Auto Scaling group that scales dynamically based on the approximate number of messages in the Amazon SQS queue.
Answer: B