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NEW QUESTION: 1
Which of the following holders of unregistered stock is precluded from selling shares under Rule 144?
A. an institutional investor
B. a holder of more than 10% of the outstanding stock
C. a broker/dealer firm
D. an officer of the issuing corporation
Answer: C
Explanation:
a broker/dealer firm. A broker/dealer cannot use Rule 144 when selling stock.

NEW QUESTION: 2
The R3TA WHERE file was spitted and the result were
A. Were copied into */exp/DATA*
B. Put directly into */exp/DATA*
Answer: A

NEW QUESTION: 3
Contoso.com에서 관리자로 일합니다. Contoso.com 네트워크는 Contoso.com이라는 단일 도메인으로 구성됩니다. 도메인 컨트롤러를 포함하여 Contoso.com 도메인의 모든 서버에는 Windows Server가 있습니다.
2012 R2가 설치되었습니다.
Contoso.com 사용자가 Windows 스토어 응용 프로그램을 설치할 수 없는지 확인하라는 지시를 받았습니다. 그런 다음 패키지 앱에 대한 규칙을 만듭니다.
다음 중 규칙을 기반으로 하는것은 무엇입니까? (해당 사항을 모두 선택하십시오.)
A. 패키지 게시자.
B. 응용 프로그램 버전.
C. 패키지 이름
D. 패키지 버전.
E. 응용 프로그램 이름
F. 응용 프로그램의 게시자.
Answer: A,C,D
Explanation:
설명
패키지 된 앱 (Windows 8 앱이라고도 함)은 Windows Server 2012 R2 및 Windows 8의 새로운 기능입니다.
앱 패키지 내의 모든 파일이 동일한 ID를 공유하도록하는 새로운 앱 모델을 기반으로 합니다. 따라서 앱 내의 각 파일이 고유 한 ID를 가질 수있는 패키지되지 않은 앱과 달리 단일 AppLocker 규칙을 사용하여 전체 애플리케이션을 제어 할 수 있습니다. Windows는 서명되지 않은 패키지 앱을 지원하지 않으므로 모든 패키지 앱에 서명해야 합니다.
AppLocker는 패키지 앱의 게시자 규칙만 지원합니다.
패키지 앱의 게시자 규칙은 다음 정보를 기반으로 합니다.
패키지 게시자
패키지 이름
패키지 버전
패키지 및 패키지 설치 프로그램의 모든 파일은 이러한 속성을 공유합니다. 따라서 패키지 된 앱의 AppLocker 규칙은 앱의 설치 및 실행을 모두 제어합니다. 그렇지 않으면 패키지 앱의 게시자 규칙이 나머지 규칙 모음과 다르지 않습니다. 예외를 지원하고 범위를 늘리거나 줄일 수 있으며 사용자 및 그룹에 할당 할 수 있습니다.

NEW QUESTION: 4
You need to implement a model development strategy to determine a user's tendency to respond to an ad.
Which technique should you use?
A. Use a Relative Expression Split module to partition the data based on centroid distance.
B. Use a Split Rows module to partition the data based on centroid distance.
C. Use a Split Rows module to partition the data based on distance travelled to the event.
D. Use a Relative Expression Split module to partition the data based on distance travelled to the event.
Answer: A
Explanation:
Explanation
Split Data partitions the rows of a dataset into two distinct sets.
The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression.
Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date.
Scenario:
Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement.
The distribution of features across training and production data are not consistent References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data
Topic 1, 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.