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NEW QUESTION: 1
An RSVP enabled router receives an RSVP reservation message for a new RSVP path. The router is unable to accommodate the reservation request.
Which action does the router take?
A. The router sends a ResvErr message to the upstream router.
B. The router sends a ResvTear message to the upstream router.
C. The router sends a ResvErr message to the downstream router.
D. The router sends a ResvTear message to the downstream router.
Answer: D

NEW QUESTION: 2
Your network contains an Active Directory domain named contoso.com. You have a Group Policy object (GPO) named GP1 that is linked to the domain. GP1 contains a software restriction policy that blocks an Application named App1.
You have a workgroup computer named Computer1 that runs Windows 8. A local Group Policy on Computer1 contains an Application control policy that allows App1.
You join Computer1 to the domain.
You need to prevent App1 from running on Computer1.
What should you do?
A. From Group Policy Management, add an Application control policy to GP1.
B. From Group Policy Management, enable the Enforced option on GP1.
C. In the local Group Policy of Computer1, configure a software restriction policy.
D. From Computer1, run gpupdate /force.
Answer: D
Explanation:
All GPO settings are processed in the following order:
1.Local Group Policy object-Each computer has exactly one Group Policy object that is stored locally. This processes for both computer and user Group Policy processing.
2.Site-Any GPOs that have been linked to the site that the computer belongs to are processed next.
3.Domain-Processing of multiple domain-linked GPOs is in the order specified by the administrator, on the Linked Group Policy Objects tab for the domain in GPMC.
4.Organizational units-GPOs that are linked to the organizational unit that is highest in the Active Directory hierarchy are processed first, then GPOs that are linked to its child organizational unit, and so on. Finally, the GPOs that are linked to the organizational unit that contains the user or computer are processed.
This order means that the local GPO is processed first, and GPOs that are linked to the organizational unit of which the computer or user is a direct member are processed last, **which overwrites settings in the earlier GPOs if there are conflicts.** (If there are no conflicts, then the earlier and later settings are merely aggregated.)
References: http://technet.microsoft.com/en-us/library/cc785665(v=ws.10).aspx

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. Precision
B. Mean absolute error
C. Coefficient of determination
D. Root Mean Square Error
E. Recall
Answer: B
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.