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NEW QUESTION: 1
Refer to the exhibit.

A kubeconfig file to manage access to clusters is provided. How many clusters are defined and which of them are accessed using username/password authentication versus certificate?
A. three clusters; scratch
B. two clusters; scratch
C. two clusters; development
D. three clusters; development
Answer: D

NEW QUESTION: 2
You are editing a SQL Server Integration Services (SSIS) package that uses checkpoints.
The package performs the following steps:
1. Download a sales transaction file by using FTP.
2. Truncate a staging table.
3. Load the contents of the file to the staging table.
4. Merge the data with another data source for loading to a data warehouse.

The checkpoints are currently working such that if any of the four steps fail, the package will restart from the failed step the next time it executes.
You need to modify the package to ensure that if either the Truncate Staging Table or the Load Sales to Staging task fails, the package will always restart from the Truncate Staging Table task the next time the package runs.
Which three steps should you perform in sequence? (To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.)

Answer:
Explanation:

Explanation
Box 1:

Box 2:

Box 3:

NEW QUESTION: 3
App1の展開環境を推奨する必要があります。
何をお勧めしますか？
A. ASE1およびI1価格帯を使用するApp Serviceプラン
B. P3v2の価格帯を使用する新しいApp Serviceプラン
C. S1価格帯を使用する新しいApp Serviceプラン
D. ASE1およびI3価格帯を使用するApp Serviceプラン
Answer: A
Explanation:
説明
参照：
https://docs.microsoft.com/en-us/azure/app-service/environment/app-service-app-service-environment-control-in

NEW QUESTION: 4
You are solving a classification task.
The dataset is imbalanced.
You need to select an Azure Machine Learning Studio module to improve the classification accuracy.
Which module should you use?
A. Synthetic Minority Oversampling Teachnique (SMOTE)
B. Filter Based Feature Selection
C. Permutation Feature Importance
D. Fisher Linear Discriminant Analysis.
Answer: D
Explanation:
Topic 1, Case Study 2
Overwiew
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 m 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.
Datasets issues
The Accessibility ToHighway 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 datasets also contain 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 columms 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 over fitting. You need to produce a more refined regression model that reduces the over fitting.
Experiment requirement
You must set up the experiment to cross-validate the Linear Regression And Bayesian Linear Regression modules to evaluate performance.
In cacti 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 Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the Median Value in numerical format. You must ensure that the data type 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-parametric statistics to measure- the relationships.
You must use a feature selection algorithm to analyze the relationship between the MedianValue and AvgRoomslnHouse 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 melt to investigate the model's accuracy and replicate the findings.
You want to configure hyper parameters the model learning process to speed the learning phase by using hyper parameters. 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 computer resources in hyper parameter 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 Azure Machine Learning Studio You must create three partitions foe cross -validation. You must also configure 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 m 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 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 and 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 Jungle modules and the Two Class Decision Jungle modules with one another.