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NEW QUESTION: 1
You need to implement a feature engineering strategy for the crowd sentiment local models.
What should you do?
A. Apply a Spearman correlation coefficient.
B. Apply a linear discriminant analysis.
C. Apply an analysis of variance (ANOVA).
D. Apply a Pearson correlation coefficient.
Answer: B
Explanation:
Explanation/Reference:
Explanation:
The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.
Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.
Scenario:
Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
Experiments for local crowd sentiment models must combine local penalty detection data.
All shared features for local models are continuous variables.
Incorrect Answers:
B: The Pearson correlation coefficient, sometimes called Pearson's R test, is a statistical value that measures the linear relationship between two variables. By examining the coefficient values, you can infer something about the strength of the relationship between the two variables, and whether they are positively correlated or negatively correlated.
C: Spearman's correlation coefficient is designed for use with non-parametric and non-normally distributed data. Spearman's coefficient is a nonparametric measure of statistical dependence between two variables, and is sometimes denoted by the Greek letter rho. The Spearman's coefficient expresses the degree to which two variables are monotonically related. It is also called Spearman rank correlation, because it can be used with ordinal variables.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/fisher-linear- discriminant-analysis
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-linear- correlation Testlet 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
Refer to the exhibit. R4 is configured as an OSPF stub. Which action must be taken to avoid R4 from learning OSPF type 3 LSA inter-area routes from 209.165.202.130?

A. Disable sending summary LSAs by adding no-summary to the stub command on the ABR.
B. Control of inter-area route propagation is best handled with EIGRP.
C. R4 must filter the incoming OSPF updates using route maps.
D. Disable sending summary LSAs by adding no-summary to the stub command on the ASBR.
Answer: A

NEW QUESTION: 3
A company is about to release a very large patch to its customers. An administrator is required to test patch installations several times prior to distributing them to customer PCs.
Which of the following should the administrator use to test the patching process quickly and often?
A. Create a virtualized sandbox and utilize snapshots
B. Create an image of a patched PC and replicate it to servers
C. Create a full disk image to restore after each installation
D. Create an incremental backup of an unpatched PC
Answer: A
Explanation:
Sandboxing is the process of isolating a system before installing new applications or patches on it so as to restrict the software from being able to cause harm to production
systems.
Before the patch is installed, a snapshot of the system should be taken. Snapshots are
backups that can be used to quickly recover from poor updates, and errors arising from
newly installed applications.

NEW QUESTION: 4
Click to expand each objective. To connect to the Azure portal, type https://portal.azure.com in the browser address bar.






When you are finished performing all the tasks, click the 'Next' button.
Note that you cannot return to the lab once you click the 'Next' button. Scoring occur in the background while you complete the rest of the exam.
Overview
The following section of the exam is a lab. In this section, you will perform a set of tasks in a live environment. While most functionality will be available to you as it would be in a live environment, some functionality (e.g., copy and paste, ability to navigate to external websites) will not be possible by design.
Scoring is based on the outcome of performing the tasks stated in the lab. In other words, it doesn't matter how you accomplish the task, if you successfully perform it, you will earn credit for that task.
Labs are not timed separately, and this exam may have more than one lab that you must complete. You can use as much time as you would like to complete each lab. But, you should manage your time appropriately to ensure that you are able to complete the lab(s) and all other sections of the exam in the time provided.
Please note that once you submit your work by clicking the Next button within a lab, you will NOT be able to return to the lab.
To start the lab
You may start the lab by clicking the Next button.
You need to create a web app named corp8548987n2 than can be scaled horizontally. The solution must use the lowest possible pricing tier for the App Service plan.
What should you do from the Azure portal?
Answer:
Explanation:
See explanation below.
Explanation
Step 1:
In the Azure Portal, click Create a resource > Web + Mobile > Web App.
Step 2:
Use the Webb app settings as listed below.
Web App name: corp8548987n2
Hosting plan: Azure App Service plan
Pricing tier of the Pricing Tier: Standard
Change your hosting plan to Standard, you can't setup auto-scaling below standard tier.
Step 3:
Select Create to provision and deploy the Web app.
References:
https://docs.microsoft.com/en-us/azure/app-service/environment/app-service-web-how-to-create-a-web-app-in-an
https://azure.microsoft.com/en-us/pricing/details/app-service/plans/