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NEW QUESTION: 1
Which two are true? (Choose two.)
A. An interface can be extended by an abstract class.
B. An abstract class can be extended by a concrete class.
C. An abstract class CANNOT be extended by an abstract class.
D. An abstract class can implement an interface.
E. An interface CANNOT be extended by another interface.
F. An abstract class can be extended by an interface.
Answer: B,D

NEW QUESTION: 2
Azure Machine Learning Studioを使用して、2つのデータセットが互いに著しく異なるかどうかを判断しています。
1つのデータセットの推定値は、他のデータセットの参照値よりも大きい場合も小さい場合もあります。定数を持つ分布を作成する必要があります。相関の関数としてのタイプIエラー。
ディストリビューションを作成する必要があります。
どのタイプの配布を作成する必要がありますか？
A. 片側オプション付きの対応のないt検定
B. 片側オプション付きの対応のあるt検定
C. 両側オプション付きの対応のあるt検定
D. ツーテールオプションを使用した対応のないt検定
Answer: C
Explanation:
Explanation
Choose a one-tail or two-tail test. The default is a two-tailed test. This is the most common type of test, in which the expected distribution is symmetric around zero.
Example: Type I error of unpaired and paired two-sample t-tests as a function of the correlation. The simulated random numbers originate from a bivariate normal distribution with a variance of 1.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/test-hypothesis-using-t-test
https://en.wikipedia.org/wiki/Student%27s_t-test
Topic 1, Case Study 2
Case study
Overview
You are a data scientist for Fabrikam Residences, a company specializing in quality private and commercial property in the United States. Fabrikam Residences is considering expanding into Europe and has asked you to investigate prices for private residences in major European cities. You use Azure Machine Learning Studio to measure the median value of properties. You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
Datasets
There are two datasets in CSV format that contain property details for two cities, London and Paris, with the following columns:

The two datasets have been added to Azure Machine Learning Studio as separate datasets and included as the starting point of the experiment.
Dataset issues
The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.
Columns in each dataset contain missing and null values. The dataset also contains many outliers. The Age column has a high proportion of outliers. You need to remove the rows that have outliers in the Age column.
The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.
Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.
Experiment requirements
You must set up the experiment to cross-validate the Linear Regression and Bayesian Linear Regression modules to evaluate performance.
In each case, the predictor of the dataset is the column named MedianValue. An initial investigation showed that the datasets are identical in structure apart from the MedianValue column. The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the MedianValue in numerical format. You must ensure that the datatype of the MedianValue column of the Paris dataset matches the structure of the London dataset.
You must prioritize the columns of data for predicting the outcome. You must use non-parameters statistics to measure the relationships.
You must use a feature selection algorithm to analyze the relationship between the MedianValue and AvgRoomsinHouse columns.
Model training
Given a trained model and a test dataset, you need to compute the permutation feature importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the correct metric to investigate the model's accuracy and replicate the findings.
You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.
You are concerned that the model might not efficiently use compute resources in hyperparameter tuning. You also are concerned that the model might prevent an increase in the overall tuning time. Therefore, you need to implement an early stopping criterion on models that provides savings without terminating promising jobs.
Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio. You must create three equal partitions for cross-validation. You must also configure the cross-validation process so that the rows in the test and training datasets are divided evenly by properties that are near each city's main river. The data that identifies that a property is near a river is held in the column named NextToRiver. You want to complete this task before the data goes through the sampling process.
When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.
Data visualization
You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.
You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.

NEW QUESTION: 3
You have a virtual network named VNet1 as shown in the exhibit.

No devices are connected to VNet1.
You plan to peer VNet1 to another virtual network named Vnet2 in the same region. VNet2 has an address space of 10.2.0.0/16.
You need to create the peering.
What should you do first?
A. Modify the address space of VNet1.
B. Add a gateway subnet to VNet1.
C. Configure a service endpoint on VNet2
D. Create a subnet on VNet1 and VNet2.
Answer: A
Explanation:
Explanation
The virtual networks you peer must have non-overlapping IP address spaces.
References:
https://docs.microsoft.com/en-us/azure/virtual-network/virtual-network-manage-peering#requirements-and-const

NEW QUESTION: 4
Your network contains an Active Directory domain named contoso.com. The domain contains a server named Server1 that runs Windows Server 2012 R2.
You enable and configure Routing and Remote Access (RRAS) on Server1.
You create a user account named User1.
You need to ensure that User1 can establish VPN connections to Server1.
What should you do?
A. Create a network policy.
B. Create a connection request policy.
C. Add a RADIUS client.
D. Modify the members of the Remote Management Users group.
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Network policies are sets of conditions, constraints, and settings that allow you to designate who is authorized to connect to the network and the circumstances under which they can or cannot connect.
Network policies can be viewed as rules. Each rule has a set of conditions and settings. Configure your VPN server to use Network Access Protection (NAP) to enforce health requirement policies.

References:
http://technet.microsoft.com/en-us/library/hh831683.aspx
http://technet.microsoft.com/en-us/library/cc754107.aspx
http://technet.microsoft.com/en-us/windowsserver/dd448603.aspx
http://technet.microsoft.com/en-us/library/dd314165(v=ws.10).aspx
http://technet.microsoft.com/en-us/library/dd469733.aspx
http://technet.microsoft.com/en-us/library/dd469660.aspx
http://technet.microsoft.com/en-us/library/cc753603.aspx
http://technet.microsoft.com/en-us/library/cc754033.aspx
http://technet.microsoft.com/en-us/windowsserver/dd448603.aspx

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