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NEW QUESTION: 1
A company needs to move its on-premises resources to AWS. The current environment consists of 100 virtual machines (VMs) with a total of 40 TB of storage. Most of the VMs can be taken offline because they support functions during business hours only; however, some are mission critical, so downtime must be minimized.
The administrator of the on-premises network provisioned 10 Mbps of internet bandwidth for the migration.
The on-premises network throughput has reached capacity and would be costly to increase. A solutions architect must design a migration solution that can be performed within the next 3 months.
Which method would fulfill these requirements?
A. Export the VMs locally, beginning with the most mission-critical servers first. Use AWS Transfer for SFTP to securely upload each VM to Amazon S3 after they are exported. Use VM Import/Export to import the VMs
B. Migrate mission-critical VMs with AWS SMS. Export the other VMs locally and transfer them to Amazon S3 using AWS Snowball. Use VM Import/Export to import the VMs into Amazon EC2.
C. Set up a 1Gbps AWS Direct Connect connection. Then provision a private virtual interface, and use AWS Server Migration Service (SMS) to migrate the VMs into Amazon EC2.
D. Use AWS Application Discovery Service to assess each application, and determine how to refactor and optimize each using AWS services or AWS Marketplace solutions.
Answer: B

NEW QUESTION: 2
You have an Azure Databricks workspace named workspace1 in the Standard pricing tier.
You need to configure workspace1 to support autoscaling all-purpose clusters. The solution must meet the following requirements:
Automatically scale down workers when the cluster is underutilized for three minutes.
Minimize the time it takes to scale to the maximum number of workers.
Minimize costs.
What should you do first?
A. Enable container services for workspace1.
B. Upgrade workspace1 to the Premium pricing tier.
C. Set Cluster Mode to High Concurrency.
D. Create a cluster policy in workspace1.
Answer: B
Explanation:
For clusters running Databricks Runtime 6.4 and above, optimized autoscaling is used by all-purpose clusters in the Premium plan Optimized autoscaling:
Scales up from min to max in 2 steps.
Can scale down even if the cluster is not idle by looking at shuffle file state.
Scales down based on a percentage of current nodes.
On job clusters, scales down if the cluster is underutilized over the last 40 seconds.
On all-purpose clusters, scales down if the cluster is underutilized over the last 150 seconds.
The spark.databricks.aggressiveWindowDownS Spark configuration property specifies in seconds how often a cluster makes down-scaling decisions. Increasing the value causes a cluster to scale down more slowly. The maximum value is 600.
Note: Standard autoscaling
Starts with adding 8 nodes. Thereafter, scales up exponentially, but can take many steps to reach the max. You can customize the first step by setting the spark.databricks.autoscaling.standardFirstStepUp Spark configuration property.
Scales down only when the cluster is completely idle and it has been underutilized for the last 10 minutes.
Scales down exponentially, starting with 1 node.
Reference:
https://docs.databricks.com/clusters/configure.html

NEW QUESTION: 3
プロジェクトマネージャーは、12か月以内に予定されているグローバルプロジェクトの保護管理計画を完了します。 2人のチームメンバーがプロジェクトを離れるとき、プロジェクトマネージャーは何をすべきですか？
A. 通信管理計画をアップロードします
B. 組織図を確認する
C. リソーススケジュールを更新する
D. リソース管理計画を確認します
Answer: D

NEW QUESTION: 4
Refer to the exhibit.

A shopping cart checkout process consists of a web store backend sending a sequence of API invocations to an Experience API, which in turn invokes a Process API. All API invocations are over HTTPS POST. The Java web store backend executes in a Java EE application server, while all API implementations are Mule applications executing in a customer -hosted Mule runtime.
End-to-end correlation of all HTTP requests and responses belonging to each individual checkout Instance is required. This is to be done through a common correlation ID, so that all log entries written by the web store backend, Experience API implementation, and Process API implementation include the same correlation ID for all requests and responses belonging to the same checkout instance.
What is the most efficient way (using the least amount of custom coding or configuration) for the web store backend and the implementations of the Experience API and Process API to participate in end-to-end correlation of the API invocations for each checkout instance?
A)
The web store backend, being a Java EE application, automatically makes use of the thread-local correlation ID generated by the Java EE application server and automatically transmits that to the Experience API using HTTP-standard headers No special code or configuration is included in the web store backend, Experience API, and Process API implementations to generate and manage the correlation ID

B)
The web store backend generates a new correlation ID value at the start of checkout and sets it on the X-CORRELATlON-lt HTTP request header In each API invocation belonging to that checkout No special code or configuration is included in the Experience API and Process API implementations to generate and manage the correlation ID

C)
The Experience API implementation generates a correlation ID for each incoming HTTP request and passes it to the web store backend in the HTTP response, which includes it in all subsequent API invocations to the Experience API.
The Experience API implementation must be coded to also propagate the correlation ID to the Process API in a suitable HTTP request header

D)
The web store backend sends a correlation ID value in the HTTP request body In the way required by the Experience API The Experience API and Process API implementations must be coded to receive the custom correlation ID In the HTTP requests and propagate It in suitable HTTP request headers

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

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