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NEW QUESTION: 1
Your company has a private cloud that contains 200 virtual machines. The network contains a server named Server1 that has the Microsoft Server Application Virtualization (Server App-V) Sequencer installed.
You plan to sequence, and then deploy a line-of-business web application named App1. App1 has a Windows Installer package named Install.msi. App1 must be able to store temporary files.
You need to identify which task must be performed on Server1 before you deploy App1.
What task should you identify?
A. Modify the environment variables.
B. Compress Install.msi.
C. Install the Web Server (IIS) server role.
D. Add a script to the OSD file.
Answer: C
Explanation:
http://blogs.technet.com/b/serverappv/archive/2012/05/09/server-app-v-recipe-ws-ftp-server.aspx
Before sequencing, prepare the OS by installing the following prerequisites:
1.IIS Web Server role (ensure IIS management scripts and tools are installed)
2.WebDeploy 2.0

NEW QUESTION: 2
会社にはニューヨークとロサンゼルスにオフィスがあります。
VNet1という名前のAzure仮想ネットワークを含むAzureサブスクリプションがあります。各オフィスには、VNet1へのサイト間VPN接続があります。
各ネットワークは、次の表に示すアドレススペースを使用します。

VNet1からのすべてのインターネット接続トラフィックがニューヨークオフィスを経由するようにする必要があります。
あなたは何をするべきか？回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation

Box 1: Set-AzureRmVirtualNetworkGatewayDefaultSite
The Set-AzureRmVirtualNetworkGatewayDefaultSite cmdlet assigns a forced tunneling default site to a virtual network gateway. Forced tunneling provides a way for you to redirect Internet-bound traffic from Azure virtual machines to your on-premises network; this enables you to inspect and audit traffic before releasing it. Forced tunneling is carried out by using a virtual private network (VPN) tunnel; this tunnel requires a default site, a local gateway where all the Azure Internet-bound traffic is redirected.
Set-AzureRmVirtualNetworkGatewayDefaultSite provides a way to change the default site assigned to a gateway.

NEW QUESTION: 3
You are a data scientist creating a linear regression model.
You need to determine how closely the data fits the regression line.
Which metric should you review?
A. Mean absolute error
B. Recall
C. Root Mean Square Error
D. Coefficient of determination
E. Precision
Answer: A
Explanation:
Topic 2, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events. Models will be global and local market data to meet the following business goals:
*Understand sentiment of mobile device users at sporting events based on audio from crowd reactions.
*Access a user's tendency to respond to an advertisement.
*Customize styles of ads served on mobile devices.
*Use video to detect penalty events.
Current environment
Requirements
* Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and snared using social media. The images and videos will have varying sizes and formats.
* The data available for model building comprises of seven years of sporting event media. The sporting event media includes: recorded videos, transcripts of radio commentary, and logs from related social media feeds feeds captured during the sporting events.
*Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo Formats.
Advertisements
* Ad response models must be trained at the beginning of each event and applied during the sporting event.
* Market segmentation nxxlels must optimize for similar ad resporr.r history.
* Sampling must guarantee mutual and collective exclusivity local and global segmentation models that share the same features.
* Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement.
* Data scientists must be able to detect model degradation and decay.
* Ad response models must support non linear boundaries features.
* The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviates from 0.1
+/-5%.
* The ad propensity model uses cost factors shown in the following diagram:

Penalty detection and sentiment
Findings
*Data scientists must build an intelligent solution by using multiple machine learning models for penalty event detection.
*Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
*Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
*Notebooks must execute with the same code on new Spark instances to recode only the source of the data.
*Global penalty detection models must be trained by using dynamic runtime graph computation during training.
*Local penalty detection models must be written by using BrainScript.
* 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.
* All shared features for local models are continuous variables.
* Shared features must use double precision. Subsequent layers must have aggregate running mean and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
*Ad response rates declined.
*Drops were not consistent across ad styles.
*The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that come from location sources are being used as raw features. A suggested experiment to remedy the bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
*Initial data discovery shows a wide range of densities of target states in training data used for crowd sentiment models.
*All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too stow.
*Audio samples show that the length of a catch phrase varies between 25%-47%, depending on region.
*The performance of the global penalty detection models show lower variance but higher bias when comparing training and validation sets. Before implementing any feature changes, you must confirm the bias and variance using all training and validation cases.

NEW QUESTION: 4
Refer to the exhibit.

What are two facts about the interface that you can determine from the given output? (Choose two.)
A. Cisco Application Visibility and Control limits throughput on the interface.
B. A Cisco Flexible NetFlow monitor is attached to the interface.
C. Feature activation array is active on the interface.
D. A quality of service policy is attached to the interface.
Answer: B,D