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NEW QUESTION: 1
You have an Azure subscription that contains the virtual machines shown in the following table.

Subnet1 and Subnet2 have a Microsoft.Storage service endpoint configured.
You have an Azure Storage account named storageacc1 that is configured as shown in the following exhibit.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Answer:
Explanation:

Explanation:
Box 1: Yes
The public IP of VM1 is allowed through the firewall.
Box 2: No
The allowed virtual network list is empty so VM2 cannot access storageacc1 directly. The public IP address of VM2 is not in the allowed IP list so VM2 cannot access storageacc1 over the Internet.
Box 3: No
The allowed virtual network list is empty so VM3 cannot access storageacc1 directly. VM3 does not have a public IP address so it cannot access storageacc1 over the Internet.
Reference:
https://docs.microsoft.com/en-gb/azure/storage/common/storage-network-security

NEW QUESTION: 2
DynamoDB 테이블을 모니터링 할 수 있는 모든 CloudWatch 메트릭을 선택하십시오.
4 가지 정답을 선택하십시오.
A. ReadThrottleEvents
B. ConsumedWriteCapacityUnits
C. ConsumedReadCapacityUnits
D. ThrottledRequests
Answer: A,B,C,D
Explanation:
설명:
Amazon CloudWatch는 1 분 간격으로 다음 DynamoDB 메트릭을 집계합니다.
ConditionalCheckFailedRequests ConsumedReadCapacityUnits ConsumedWriteCapacityUnits ReadThrottleEvents 반환 된 바이트 반환 된 항목 수 반환 된 레코드 수 SuccessfulRequestLatency SystemErrors ThrottledRequests UserErrors WriteThrottleEvents 참조 :
http://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/dynamo-metricscollected.html

NEW QUESTION: 3
The CUSTOM tier for Cloud Machine Learning Engine allows you to specify the number of which types of cluster nodes?
A. Parameter servers
B. Masters, workers, and parameter servers
C. Workers
D. Workers and parameter servers
Answer: D
Explanation:
The CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines:
You must set TrainingInput.masterType to specify the type of machine to use for your master node.
You may set TrainingInput.workerCount to specify the number of workers to use.
You may set TrainingInput.parameterServerCount to specify the number of parameter servers to use.
You can specify the type of machine for the master node, but you can't specify more than one master node.
Reference: https://cloud.google.com/ml-engine/docs/training-overview#job_configuration_parameters