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NEW QUESTION: 1
고객은 동일한 VPC의 서로 다른 서브넷에서 두 개의 Amazon EC2 인스턴스 인 Server1과 Server2를 실행하고 있습니다.
Server1은 Server2를 ping 할 수 있지만 Server2는 Server1을 ping 할 수 없습니다.이 동작을 설명 할 수 있습니까? 2 답 선택
A. Server1의 운영 체제 방화벽이 Server2의 트래픽을 차단하고 있습니다.
B. 두 서버가 같은 가용성 영역에 있지 않습니다.
C. Server1의 보안 그룹에 대한 수신 규칙이 ICMP 트래픽을 허용하지 않습니다.
D. Server2의 보안 그룹에 대한 수신 규칙이 ICMP 트래픽을 허용하지 않습니다.
E. 경로 표에 정의 된 Server2에서 Server1까지의 경로가 없습니다.
Answer: A,C

NEW QUESTION: 2
A customer has a SAS-based storage environment with a SAS domain and some standalone servers, each with multiple built-in SAS disks. The implementation includes direct routing, table routing and subtractive routing. What is table routing?
A. routing between devices connected to different edge expanders
B. routing between devices connected to an edge expander
C. routing along multiple parallel connections
D. routing along a physical point-to-point connection
Answer: B

NEW QUESTION: 3
Which reports can you access via the order and resource reporting transaction in advanced planning?
There are 3 correct answers to this question.
Response:
A. Production overview
B. WIP list
C. Planning file entries
D. Operation list
E. Production list
Answer: A,B,D

NEW QUESTION: 4
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are using Azure Machine Learning Studio to perform feature engineering on a dataset.
You need to normalize values to produce a feature column grouped into bins.
Solution: Apply an Entropy Minimum Description Length (MDL) binning mode.
Does the solution meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Entropy MDL binning mode: This method requires that you select the column you want to predict and the column or columns that you want to group into bins. It then makes a pass over the data and attempts to determine the number of bins that minimizes the entropy. In other words, it chooses a number of bins that allows the data column to best predict the target column. It then returns the bin number associated with each row of your data in a column named <colname>quantized.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins