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NEW QUESTION: 1
웹 사이트를 개발합니다. Azure에서 웹 사이트를 호스팅 할 계획입니다. 웹 사이트가 게시 된 후 트래픽이 많을 것으로 예상합니다. 비용을 최소화하면서 웹 사이트의 가용성과 응답 성을 유지해야합니다. 웹 사이트를 배포해야합니다. 어떻게해야합니까?
A. 표준 서비스 계층을 사용하는 App Service에 웹 사이트를 배포하십시오. CPU로드가 높을 때 자동으로 확장되도록 App Service 계획을 구성하십시오.
B. 웹 사이트를 가상 머신에 배포하십시오. CPU로드가 높을 때 가상 머신이 자동으로 확장되도록 구성하십시오.
C. 공유 서비스 계층을 사용하는 App Service에 웹 사이트를 배포하십시오. CPU로드가 높을 때 자동으로 확장되도록 App Service 계획을 구성하십시오.
D. 웹 사이트를 가상 머신에 배포하십시오. CPU로드시 가상 머신 인스턴스 수를 늘리도록 스케일 세트 구성
Answer: A
Explanation:
설명:
WAWS (Windows Azure 웹 사이트)는 표준, 무료 및 공유의 3 가지 모드를 제공합니다.
표준 모드는 인스턴스가 하나 뿐인 사이트의 경우에도 월 99.9 %의 엔터프라이즈 급 SLA (서비스 수준 계약)를 제공합니다.
표준 모드는 전용 인스턴스에서 실행되므로 Windows Azure 웹 사이트를 구매하는 다른 방법과 다릅니다.

NEW QUESTION: 2
You are building a regression model tot estimating the number of calls during an event.
You need to determine whether the feature values achieve the conditions to build a Poisson regression model.
Which two conditions must the feature set contain? I ach correct answer presents part of the solution. NOTE:
Each correct selection is worth one point.
A. The label data must be a negative value.
B. The label data can be positive or negative,
C. The label data must be a positive value
D. The data must be whole numbers.
E. The label data must be non discrete.
Answer: C,D
Explanation:
Explanation
Poisson regression is intended for use in regression models that are used to predict numeric values, typically counts. Therefore, you should use this module to create your regression model only if the values you are trying to predict fit the following conditions:
* The response variable has a Poisson distribution.
* Counts cannot be negative. The method will fail outright if you attempt to use it with negative labels.
* A Poisson distribution is a discrete distribution; therefore, it is not meaningful to use this method with non-whole numbers.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/poisson-regression
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:

The ad propensity model uses proposed cost factors shown in the following diagram:

Performance curves of current and proposed cost factor scenarios are 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: 3
企業は、「プライベート」と「パブリック」の定義を持つデータ分類システムを持っています」同社のセキュリティポリシーでは、データがどのようにタイプに基づいて保護されるべきかが概説されています。
"専有"。
次のうち、このデータタイプを追加した理由はどれですか？
A. より検索可能なデータ
B. データ分類の改善
C. プライバシー担当者の権限の拡大
D. コスト削減
Answer: B