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NEW QUESTION: 1
What protocol allows for centralized management of multiple wireless access points?
A. LWAPP
B. ad hoc
C. WEP
D. WPA
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Lightweight access point protocol (LWAPP) is a protocol used to allow centralized management of access points (APs). The management components are removed from the APs and centralized into a wireless LAN controller. This controller can coordinate WLAN access, managing the load on the APs and user movement between APs. A lightweight AP receives control and configuration from the WLAN controller.
LWAPP defines the following activities:
Packet encapsulation, fragmentation, and formatting
Access point certification and software control
Access point discovery, information exchange, and configuration
The processing of 802.11 data and the handling of management protocols and access point capabilities is distributed between the lightweight access point and the WLAN controller. For example, the AP handles the transmission of beacon frames and responses to probe request frames and the controller handles authentication. The WLC enhances:
Mobility
Authentication
Security management
When lightweight APs are used, the data path from one wireless station to another includes the AP and its controller.
Wi-Fi protected access (WPA) is an encryption and authentication protocol for wireless access. It supports
802.1x authentication and EAP on a wireless client. The AP would function as the authenticator.
WEP is a wireless encryption protocol that uses static keys and no authentication.
Ad hoc is a WLAN mode used for peer-to-peer connectivity. Ad hoc allows wireless-enabled computers to communicate with each other without having an AP involved.
Objective:
Layer 2 Technologies
Sub-Objective:
Configure and verify other LAN switching technologies
References:
Cisco > Support > Product Support > Wireless > Cisco Aironet 1200 Series > Product Literature > Solution Overviews > Cisco Unified Wireless Network Overview
NEW QUESTION: 2
You need to meet the technical requirements for setting the storage limit.
Which three actions 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
Topic 2, Litware, inc Case Study:
Overview
Existing Environment
Litware, Inc. is a design and manufacturing company that has 4,500 users. The company has sales, marketing, design research, field test, and human resources (HR) departments.
Litware has a main office in California, three branches offices in the United States, and five branches offices in Europe.
On-premises Infrastructure
The network contains an Active Directory forest named litewareinc.com that contains a child domain for each region.
All domain controllers, run Windows Server 2012. The man office syncs identities to Microsoft Azure Active Directory (Azure AD) by using Azure Connect. All user accounts are created in the on-premises Active Directory and sync to Azure AD.
Each office contains the following servers and Client computers:
*A domain controller that runs Windows Server 2012.
*A file server that runs Windows Server 2012.
* Client computers that run Windows 10.
Currently, alt content created by users is stored locally on file servers.
Cloud Infrastructure
Litware is moving the content from the file servers to Microsoft Office 365. The company purchases 4,500 Microsoft 365 E5 licenses.
Liteware uses Microsoft Exchange Online for email.
Problem Statements
Liteware identifies the following issues:
* Finding content and people within the organization is difficult.
* Users cannot access company data outside the corporate network.
* Content recovery is slow because all the content is still on-premises.
* Data security is compromised because users can copy company content to USB drives.
* The locally stored content to USB drives.
* Users must frequently contact the HR department to find employees within the organization who have relevant skills.
* Users can delete content indiscriminately and without resource as they have full control of the content of the file servers.
Requirements
Business Goals
Liteware identifies the following strategic initiatives to remain competitive:
* All content must be stored centrally
* Access to content must be based on the user's:
* Department
* Security level
* Physical location
* Users must be able to work on content offline
* Users must be able to share content externally
* Content classifications from mobile devices
* Content classifications must include a physical location
* Content must be retained and protected based on its type
* Litware must adhere to highly confidential regulatory standards that include:
* The ability to restrict the copying of all content created internally and externally
* Including accurate time zone reporting in audit trails
* Users must be able to search for content and people across the entire organization.
* Content classification metadata must adhere to naming conventions specified by the IT department.
* Users must be able to access content quickly without having to review many pages of search results to find documents.
* Security rules must be implemented so that user access can be revoked if a user shares confidential content with external users.
Planned Changes
Liteware plans to implement the following changes:
*Move all department content to Microsoft SharePoint Online.
*Move all user content to Microsoft OneDrive for Business.
*Restrict user access based on location and device.
Technical Requirements
Liteware identifies the following technical requirements:
* All content must be stored centrally
* Access to content must be based on the user's:
* Department
* Security level
* Physical location
* Users must be able to work on content offline
* Users must be able to share content externally
* Content classifications from mobile devices
* Content classifications must include a physical location
* Content must be retained and protected based on its type
* Litware must adhere to highly confidential regulatory standards that include:
* The ability to restrict the copying of all content created internally and externally
* Including accurate time zone reporting in audit trails
* Users must be able to search for content and people across the entire organization.
* Content classification metadata must adhere to naming conventions specified by the IT department.
* Users must be able to access content quickly without having to review many pages of search results to find documents.
* Security rules must be implemented so that user access can be revoked if a user shares confidential content with external users.
NEW QUESTION: 3
다음 중 시각화 사용과 관련된 가장 큰 위험은 무엇입니까?
A. 호스트 성능 문제
B. 부적절한 구성
C. 네트워크 대역폭이 충분하지 않습니다
D. 단일 실패 지점
Answer: A
NEW QUESTION: 4
Azure Machine Learning Studioを使用して、2つのデータセットが互いに著しく異なるかどうかを判断しています。
1つのデータセットの推定値は、他のデータセットの参照値よりも大きい場合も小さい場合もあります。定数を持つ分布を作成する必要があります。相関の関数としてのタイプIエラー。
ディストリビューションを作成する必要があります。
どのタイプの配布を作成する必要がありますか？
A. 片側オプション付きの対応のないt検定
B. 片側オプション付きの対応のあるt検定
C. 両側オプション付きの対応のあるt検定
D. ツーテールオプションを使用した対応のないt検定
Answer: C
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.
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