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NEW QUESTION: 1
SQL APIを使用するAzure Cosmos DBデータベースがあります。
データベースから古いデータを自動的に削除する必要があります。
何を使うべきですか？
A. Time to Live (TTL)
B. schema on read
C. Low Latency Analytical Processing (LLAP)
D. soft delete
Answer: A
Explanation:
Explanation
With Time to Live or TTL, Azure Cosmos DB provides the ability to delete items automatically from a container after a certain time period. By default, you can set time to live at the container level and override the value on a per-item basis. After you set the TTL at a container or at an item level, Azure Cosmos DB will automatically remove these items after the time period, since the time they were last modified.
References:
https://docs.microsoft.com/en-us/azure/cosmos-db/time-to-live

NEW QUESTION: 2
Symmetric encryption utilizes __________, while asymmetric encryption utilizes _________.
A. Private keys, session keys
B. Private keys, public keys
C. Public keys, one time
D. Shared keys, private keys
Answer: B
Explanation:
Symmetrical systems require the key to be private between the two parties. With asymmetric systems, each circuit has one key.
In more detail:
* Symmetric algorithms require both ends of an encrypted message to have the same key and processing algorithms.
Symmetric algorithms generate a secret key that must be protected. A symmetric key, sometimes referred to as a secret key or private key, is a key that isn't disclosed to people who aren't authorized to use the encryption system.
* Asymmetric algorithms use two keys to encrypt and decrypt data. These asymmetric keys are referred to as the public key and the private key. The sender uses the public key to encrypt a message, and the receiver uses the private key to decrypt the message; what one key does, the other one undoes.
Incorrect Answers:
A. Symmetric encryption uses private keys, not public keys.
B. Symmetric encryption uses private keys, not shared keys.
C. Asymmetric encryption does not use session keys, it uses a public key to encrypt data.
References:
Dulaney, Emmett and Chuck Eastton, CompTIA Security+ Study Guide, 6th Edition, Sybex, Indianapolis, 2014, pp. 251,
262

NEW QUESTION: 3
You are performing a filter based feature selection for a dataset 10 build a multi class classifies by using Azure Machine Learning Studio.
The dataset contains categorical features that are highly correlated to the output label column.
You need to select the appropriate feature scoring statistical method to identify the key predictors. Which method should you use?
A. Chi-squared
B. Spearman correlation
C. Kendall correlation
D. Person correlation
Answer: D
Explanation:
Explanation
Pearson's correlation statistic, or Pearson's correlation coefficient, is also known in statistical models as the r value. For any two variables, it returns a value that indicates the strength of the correlation Pearson's correlation coefficient is the test statistics that measures the statistical relationship, or association, between two continuous variables. It is known as the best method of measuring the association between variables of interest because it is based on the method of covariance. It gives information about the magnitude of the association, or correlation, as well as the direction of the relationship.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/filter-based-feature-selection
https://www.statisticssolutions.com/pearsons-correlation-coefficient/
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: 4
An Administrator has an Amazon EC2 instance with an IPv6 address. The Administrator needs to prevent direct access to this instance from the Internet.
The Administrator should place the EC2 instance in a:
A. Private Subnet with an egress-only Internet Gateway attached to the subnet and placed in the subnet Route Table.
B. Private subnet with an egress-only Internet Gateway attached to the VPC and placed in the subnet Route Table.
C. Public subnet and a security group that blocks inbound IPv6 traffic attached to the interface.
D. Public subnet with an egress-only Internet Gateway attached to the VPC and placed in the VPC Route Table.
Answer: D