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NEW QUESTION: 1
You must use the Azure Machine Learning SDK to interact with data and experiments in the workspace.
You need to configure the config.json file to connect to the workspace from the Python environment.
Which two additional parameters must you add to the config.json file in order to connect to the workspace? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Key
B. subscription_Id
C. Login
D. resource_group
E. region
Answer: B,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: 2

A. Option B
B. Option C
C. Option D
D. Option A
Answer: C

NEW QUESTION: 3
You have a customized image of Windows 7 Professional.
You mount the image and modify the contents of the image.
You need to restore the image to its original state.
Which tool should you use?
A. Dism.exe
B. Pkgmgr.exe
C. Sysprep.exe
D. Ocsetup.exe
Answer: A
Explanation:
Explanation/Reference:
1083 2058
Dism
Deployment Image Servicing and Management (DISM) is a command-line tool used to service Windows images offline before deployment. You can use it to install, uninstall, configure, and update Windows features, packages, drivers, and international settings. Subsets of the DISM servicing commands are also available for servicing a running operating system.
Windows 7 introduces the DISM command-line tool. You can use DISM to service a Windows image or to
prepare a Windows PE image. DISM replaces Package Manager (Pkgmgr.exe), PEimg, and Intlcfg in
Windows Vista, and includes new features to improve the experience for offline servicing.
You can use DISM to perform the following actions:
Prepare a Windows PE image.
Enable or disable Windows features within an image.
Upgrade a Windows image to a different edition.
Add,
remove, and enumerate packages.
Add,
remove, and enumerate drivers.
Apply changes based on the offline servicing section of an unattended answer file.
Configure international settings.
Implement powerful logging features.
Service operating systems such as Windows Vista with SP1 and Windows Server 2008.
Service a 32-bit image from a 64-bit host and service a 64-bit image from a 32-bit host.
Service all platforms (32-bit, 64-bit, and Itanium).
Use existing Package Manager scripts.
DISM Command-Line Options
To service a Windows image offline, you must apply or mount it. WIM images can be mounted using the WIM commands within DISM, or applied and then recaptured using ImageX. You can also use the WIM commands to list the indexes or verify the architecture for the image you are mounting. After you update the image, you must dismount it and then either commit or discard the changes you have made.
NOT Sysprep
Sysprep is a tool designed for corporate system administrators, OEMs, and others who need to deploy the Windows XP operating system on multiple computers. After performing the initial setup steps on a single system, you can run Sysprep to prepare the sample computer for cloning.
Sysprep prepares the image for capture by cleaning up various user-specific and computer-specific settings, as well as log files. The reference installation now is complete and ready to be imaged.

NEW QUESTION: 4
Using a bar chart, you can view the following data:
Response:
A. None of above
B. Total times of occurrence of a specific type of interaction within a specified period To display the data, select Interaction Count from the View by dropdown list.
C. Point one and two
D. Total number of followers who are involved in a specific type of interaction within a specified period To display the data, select Contact Count from the View by dropdown list.
Answer: C

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