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NEW QUESTION: 1
Click the exhibit button. After reviewing captured network traffic logs, a security auditor suspects a violation of the organization's computer use policy. Which of the following is the likely indicator of the violation?


A. Service disruption
B. Registry entries
C. New user account
D. Unauthorized programs
E. Malicious software
Answer: D
Explanation:
Explanation/Reference:
Explanation:

NEW QUESTION: 2
群衆感情モデルの評価戦略を定義する必要があります。
順番に実行する必要がある3つのアクションはどれですか？回答するには、適切なアクションをアクションのリストから回答エリアに移動し、正しい順序に並べます。

Answer:
Explanation:

Explanation:
Scenario:
Experiments for local crowd sentiment models must combine local penalty detection data.
Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.
Note: Evaluate the changed in correlation between model error rate and centroid distance In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.
References:
https://en.wikipedia.org/wiki/Nearest_centroid_classifier
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/sweep-clustering
Topic 2, Two 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
In a three system landscape from which system the Transportable change request has to be exported
A. Production System
B. Development System
C. Quality System
Answer: C

NEW QUESTION: 4
リモートアクセスVPNテクノロジを検討する場合、SSL VPNとIPSec VPNの2つの利点は何ですか？
（2つ選択）
A. 既存のネットワークインフラストラクチャを活用します
B. デスクトップサポートを最小限に抑えます
C. クライアントのカスタマイズを増やすことができます。
D. 高度にフィルタリングされた環境で動作します
E. Webブラウザー経由でアクセス可能
Answer: D,E
Explanation:
Explanation
SSL VPN has some unique features when compared with other existing VPN technologies. Most noticeably, SSL VPN uses SSL protocol and its successor, Transport Layer Security (TLS), to provide a secure connection between remote users and internal network resources. Today, this SSL/TLS function exists ubiquitously in modern web browsers. Unlike traditional IP Security (IPSec) remote-access VPN technology, which requires installation of IPSec client software on a client machine before a connection can be established, users typically do not need to install client software in order to use SSL VPN. As a result, SSL VPN is also known as "clientless VPN" or "Web VPN".
Another SSL VPN advantage over IPSec VPN is its ease of use for end users. Different IPSec VPN vendors may have different implementation and configuration requirements. SSL VPN, on the other hand, requires only a modern web browser. End users may even choose their favorite web browsers without being restricted by the operating system.
One SSL VPN advantage for end users is in the area of outbound connection security. In most environments, outbound Secure HTTP (HTTPS) traffic, which is also based on SSL, is not blocked. This means that even if a particular local environment does not permit outbound IPSec VPN sessions (such restriction is not unusual), SSL VPN is likely free of such restriction.
https://www.cisco.com/c/en/us/about/security-center/ssl-vpn-security.html

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