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NEW QUESTION: 1
You are designing a Windows Communication Foundation (WCF) service that uses the
Product class.
You need to update the class to meet the storage requirement.
What should you do? (Each correct answer presents part of the solution. Choose all that apply.)
A. Mark the Product class with the CollectionDataContract attribute.
B. Mark the public members of the Product class with the DataMember attribute.
C. Mark the public members of the Product class with the DataContractFormat attribute.
D. Mark the Product class with the DataContract attribute.
Answer: B,D
Explanation:
So as of .NET 3.5 SP1, you don't have to add data contract or data member attributes anymore - if you don't then the data contract serializer will serialize all public properties on your class, just like the XML serializer would.
HOWEVER: by not adding those attributes, you lose a lot of useful capabilities:
* without [DataContract], you cannot define an XML namespace for your data to live in
* without [DataMember], you cannot serialize non-public properties or fields
* without [DataMember], you cannot define an order of serialization (Order=) and the
DCS will serialize all properties alphabetically
* without [DataMember], you cannot define a different name for your property
(Name=)
* without [DataMember], you cannot define things like IsRequired= or other useful attributes
* without [DataMember], you cannot leave out certain public properties - all public properties will be serialized by the DCS

NEW QUESTION: 2
Your network contains an Active Directory domain named contoso.com. The domain contains more than
100 Group Policy objects (GPOs). Currently, there are no enforced GPOs.
You need to prevent all of the GPOs at the site level and at the domain level from being Applied to users and computers in an organizational unit (OU) named OU1.
You want to achieve this goal by using the minimum amount of administrative effort.
What should you use?
A. Gptedit.msc
B. Gpresult
C. Gpupdate
D. Dcgpofix
E. Get-GPOReport
F. Add-ADGroupMember
G. Restore-GPO
H. Gpfixup
I. Set-GPLink
J. Import-GPO
K. Set-GPInheritance
L. Set-GPPermission
Answer: K
Explanation:
Explanation/Reference:
Explanation:
http://technet.microsoft.com/en-us/library/ee461032.aspx

NEW QUESTION: 3
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 data must be whole numbers.
B. The label data must be a negative value.
C. The label data must be a positive value
D. The label data must be non discrete.
E. The label data can be positive or negative,
Answer: A,C
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: 4
Events are defined in
Please choose the correct answer. Choose one:
A. Picklists
B. Manager Pay, organization and Job structures
C. Data models
D. Employee files
Answer: A

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