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NEW QUESTION: 1
What are two ways to create a customized schema? (Choose two.)
A. use ClearQuest API and run query to customize the schema
B. import a portion of the schema from another schema and modify appropriately
C. start with Blank schema and add the respective packages
D. alter tables in the schema database using SQL Statement and change relation as needed
Answer: B,C

NEW QUESTION: 2
기존 호스팅 환경에서 웹 애플리케이션 실행 비용을 AWS에서 실행하는 비용을 비교하는 데 어떤 도구를 사용할 수 있습니까?
A. AWS 총 소유 비용 (TCO) 계산기
B. AWS 예산
C. AWS 비용 탐색기
D. AWS 비용 및 사용 보고서
Answer: A
Explanation:
Reference: https://aws.amazon.com/tco-calculator/

NEW QUESTION: 3
Scenario: An administrator has three servers in a pool each containing three physical network interface cards (NICs). Currently NIC 0 is used for management, NIC 1 for virtual machine traffic, and NIC 2 for storage traffic.
Which network type must the administrator create to access multiple VLANs for virtual machine traffic?
A. Cross-server private
B. Bonded
C. Single-server private
D. External
Answer: D

NEW QUESTION: 4
You need to identify the methods for dividing the data according to the testing requirements.
Which properties should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Scenario: Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.
Box 1: Assign to folds
Use Assign to folds option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.
Not Head: Use Head mode to get only the first n rows. This option is useful if you want to test a pipeline on a small number of rows, and don't need the data to be balanced or sampled in any way.
Not Sampling: The Sampling option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.
Box 2: Partition evenly
Specify the partitioner method: Indicate how you want data to be apportioned to each partition, using these options:
* Partition evenly: Use this option to place an equal number of rows in each partition. To specify the number of output partitions, type a whole number in the Specify number of folds to split evenly into text box.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/partition-and-sample