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NEW QUESTION: 1
You are determining if two sets of data are significantly different from one another by using Azure Machine Learning Studio.
Estimated values in one set of data may be more than or less than reference values in the other set of data. You must produce a distribution that has a constant Type I error as a function of the correlation.
You need to produce the distribution.
Which type of distribution should you produce?
A. Paired t-test with a two-tail option
B. Unpaired t-test with a one-tail option
C. Unpaired t-test with a two tail option
D. Paired t-test with a one-tail option
Answer: A
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: 2
Which two types of volumes support NDMP backup? (Choose two.)
A. Flash Pool
B. FlexGroup
C. FlexVol
D. FlexCache
Answer: B,C

NEW QUESTION: 3
Your role of Network Administrator at ABC.com includes the management of the Active Directory Domain Services (AD DS) domain named ABC.com. The network includes servers that run Windows Server 2008 R2 Service Pack 1 (SP1) and Windows Server 2012. A server named ABC-Win12Admin runs Windows Server 2012. You use ABC-Win12Admin to administer the Windows Server 2012 servers in the domain. A server named ABC-IPAM1 is configured as an IP Address Management (IPAM) Server. You want to manage ABC-IPAM1 from ABC-Win12Admin. You install the IP Address Management (IPAM) client on ABC-Win12Admin. You open Server Manager on ABCWin12Admin but ABC-IPAM1 is not listed.
How can you manage ABC-IPAM1 from ABC-Win12Admin?
A. By adding the computer account of ABC-Win12Admin to the IPAM Multi-Server Management (MSM) administrators group on ABC-IPAM1.
B. By enabling Windows Remote Management (WinRM) on ABC-IPAM1.
C. By selecting the Add Server to Manage option in Server Manager on ABC-Win12Admin.
D. By selecting the Connect to Server option in Computer Management on ABC-Win12Admin.
Answer: C
Explanation:
Explanation/Reference:
Explanation:

NEW QUESTION: 4
In Microsoft Windows, as files are deleted the space they were allocated eventually is considered available for use by other files. This creates alternating used and unused areas of various sizes. What is this called?
A. network file storing
B. defragmentation
C. alternate data streaming
D. free space fragmentation
Answer: D
Explanation:
Free (unallocated) space fragmentation occurs when there are several unused areas of the file system where new files or meta data can be written to. Unwanted free space fragmentation is generally caused by deletion or truncation of files, but file systems may also intentionally insert fragments ("bubbles") of free space in order to facilitate extending nearby files