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NEW QUESTION: 1
Network interconnection devices are often located below and below the OSI reference model, implemented in a combination of hardware and software.
A. One
B. Three
C. Four
D. Five
Answer: B

NEW QUESTION: 2
MySecureData 회사는 전세계에 5 개의 지점을 가지고 있습니다. 웹 서버가 AWS에 있고 각 지점이 로컬 데이터 센터에 자체 데이터베이스를 갖도록 데이터 센터를 확장하려고 합니다. 회사는 사용자 로그인을 기반으로 데이터 센터에 연결하려고 합니다.
MySecureData 회사는 어떻게 AWS VPC로 이 시나리오를 구현할 수 있습니까?
A. AWS CloudGateway를 사용하여 여러 VPN 연결과 통신하십시오.
B. 단일 VPC에서 다른 데이터 센터를 연결할 수 없습니다.
C. 앱 서버의 퍼블릭 서브넷이있는 5 개의 VPC를 생성하고 각 VPN의 VPN 게이트웨이를 설정하여 개별적으로 연결하십시오.
D. AWS VPN CloudHub를 사용하여 여러 VPN 연결과 통신하십시오.
Answer: D
Explanation:
설명:
VPC (Virtual Private Cloud)는 사용자의 AWS 계정 전용 가상 네트워크입니다. 사용자는 VPC 내 요구 사항에 따라 서브넷을 생성 할 수 있습니다. 사용자가 자신의 데이터 센터에서 VPC를 연결하려는 경우 하드웨어 VPN 액세스를 사용하여 데이터 센터에 연결하는 퍼블릭 및 VPN 전용 서브넷을 설정할 수 있습니다. 조직에 여러 개의 VPN 연결이있는 경우 AWS VPN CloudHub를 사용하여 사이트간에 보안 통신을 제공 할 수 있습니다.
VPN CloudHub는 사용자가 VPC와 함께 또는 VPC없이 사용할 수있는 간단한 허브 앤 스포크 모델에서 작동합니다. 이 설계는 원격 지사 간의 기본 또는 백업 연결을 위해 편리하고 저렴한 허브 및 스포크 모델을 구현하려는 여러 지사 및 기존 인터넷 연결을 보유한 고객에게 적합합니다.
http://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPN_CloudHub.html

NEW QUESTION: 3
ワークスペース内のデータと実験を操作するには、Azure Machine Learning SDKを使用する必要があります。
Python環境からワークスペースに接続するには、config.jsonファイルを構成する必要があります。
ワークスペースに接続するために、config.jsonファイルに追加する必要がある2つの追加パラメーターはどれですか。それぞれの正解がソリューションの一部を示しています。
注：それぞれの正しい選択は1ポイントの価値があります。
A. resource_group
B. ログイン
C. subscription_Id
D. キー
E. 地域
Answer: C,E
Explanation:
Topic 1, Case Study
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: 4
The delivery team wants to ensure stakeholders contribute value on an ongoing basis to delivering the solution.
This is an example of the following agile analysis principle:
A. Retrospective input and observations
B. Stimulate collaboration and continuous improvement
C. Avoiding waste and unused functionality
D. Value of assessing problems in a timely manner
Answer: B

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