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NEW QUESTION: 1
You develop an entertainment application where users can buy and trade virtual real estate. The application must scale to support thousands of users.
The current architecture includes five Azure virtual machines (VM) that connect to an Azure SQL Database for account information and Azure Table Storage for backend services. A user interacts with these components in the cloud at any given time.
Routing Service - Routes a request to the appropriate service and must not persist data across

sessions.
Account Service - Stores and manages all account information and authentication and requires data to

persist across sessions
User Service - Stores and manages all user information and requires data to persist across sessions.

Housing Network Service - Stores and manages the current real-estate economy and requires data to

persist across sessions.
Trade Service - Stores and manages virtual trade between accounts and requires data to persist

across sessions.
Due to volatile user traffic, a microservices solution is selected for scale agility.
You need to migrate to a distributed microservices solution on Azure Service Fabric.
Solution: Create a Service Fabric Cluster with a stateless Reliable Service for Routing Service. Create stateful Reliable Services for all other components.
Does the solution meet the goal?
A. No
B. Yes
Answer: B

NEW QUESTION: 2
You have an application running on Amazon EC2 in an Auto Scaling group. Instances are being bootstrapped dynamically, and the bootstrapping takes over 15 minutes to complete. You find that instances are reported by Auto Scaling as being In Service before bootstrapping has completed. You are receiving application alarms related to new instances before they have completed bootstrapping, which is causing confusion. You find the cause: your application monitoring tool is polling the Auto Scaling Service API for instances that are In Service, and creating alarms for new previously unknown instances. Which of the following will ensure that new instances are not added to your application monitoring tool before bootstrapping is completed?
A. Use the default Amazon Cloud Watch application metrics to monitor your application's health.
Configure an Amazon SNS topic to send these Cloud Watch alarms to the correct recipients.
B. Create an Auto Scaling group lifecycle hook to hold the instance in a pending: wait state until your bootstrapping is complete. Once bootstrapping is complete, notify Auto Scaling to complete the lifecycle hook and move the instance into a pending:proceed state.
C. Tag all instances on launch to identify that they are in a pending state. Change your application monitoring tool to look for this tag before adding new instances, and the use the Amazon API to set the instance state to 'pending' until bootstrapping is complete.
D. Increase the desired number of instances in your Auto Scaling group configuration to reduce the time it takes to bootstrap future instances.
Answer: B
Explanation:
Explanation
Auto Scaling lifecycle hooks enable you to perform custom actions as Auto Scaling launches or terminates instances. For example, you could install or configure software on newly launched instances, or download log files from an instance before it terminates.
After you add lifecycle hooks to your Auto Scaling group, they work as follows:
1. Auto Scaling responds to scale out events by launching instances and scale in events by terminating instances.
2. Auto Scaling puts the instance into a wait state (Pending:Wait orTerminating:Wait). The instance remains in this state until either you tell Auto Scaling to continue or the timeout period ends.
For more information on rolling updates, please visit the below link:
* http://docs.aws.amazon.com/autoscaling/latest/userguide/lifecycle-hooks.html

NEW QUESTION: 3

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