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NEW QUESTION: 1
You are performing feature engineering on a dataset.
You must add a feature named CityName and populate the column value with the text London.
You need to add the new feature to the dataset.
Which Azure Machine Learning Studio module should you use?
A. Preprocess Text
B. Execute Python Script
C. Edit Metadata
D. Latent Dirichlet Allocation
Answer: C
Explanation:
Explanation/Reference:
Explanation:
Typical metadata changes might include marking columns as features.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/edit-metadata Testlet 1 Case study Overview You are a data scientist in a company that provides data science for professional sporting events. Models will use 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.

Assess 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
Media used for penalty event detection will be provided by consumer devices. Media may include

images and videos captured during the sporting event and shared 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 video transcripts or radio commentary, and logs from related social media feeds captured during the sporting events.
Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo

formats.
Penalty detection and sentiment
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.
Advertisements
During the initial weeks in production, the following was observed:
Ad response rated 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 uncorrelated features.
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 slow.
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 shows 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.
Ad response models must be trained at the beginning of each event and applied during the sporting

event.
Market segmentation models must optimize for similar ad response history.

Sampling must guarantee mutual and collective exclusively between 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.
Ad response models must support non-linear boundaries of features.

The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated

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:

NEW QUESTION: 2
The QueueDetail entity type must inherit from the InboundQueue entity type in the ExternalQueue service project using table-per-type inheritance.
You need to modify the entities in the designer.
What should you do? (Each correct answer presents part of the solution. Choose all that apply.)
A. Remove the association between the entities.
B. Set the InboundQueue BaseType to QueueDetail.
C. Set the QueueDetail BaseType to InboundQueue.
D. Remove the OrderNum property in QueueDetail.
E. Right-click the entities and validate the table mapping.
F. Remove the OrderNum property in InboundQueue.
Answer: A,C,D,E
Explanation:
http://www.robbagby.com/entity-framework/entity-framework-modeling-tableper-type-inheritance/
Topic 6, Flight Information Consolidation
Background
You are developing a flight information consolidation service. The service retrieves flight information from a number of sources and combines them into a single data set. The consolidated flight information is stored in a SQL Server database. Customers can query and retrieve the data by using a REST API provided by the service. The service also offers access to historical flight information. The historical flight information can be filtered and queried in an ad hoc manner. The service runs on a Windows Azure Web Role. SSL is not used.
Business Requirements
A new data source for historical flight information is being developed by a contractor located on another continent.
If a time zone is not specified, then it should be interpreted as Coordinated Universal Time (UTC).
When you upgrade a service from a staging deployment to a production deployment, the time that the service is unavailable must be minimized.
The default port must be used for HTTP.
Technical Requirements
The existing sources of flight information and the mechanism of exchange are listed below.
Blue Yonder Airlines provides flight information in an XML file.
Consolidated Messenger provides flight information in a Microsoft Access database that is uploaded every 12 hours to the service using SFTP. The company uses port 22 for SFTP.
Margie's Travel provides and consumes flight information using serialized
ADO.NET DataSets.
Data is periodically synced between the service and Margie's Travel.
Trey Research provides data from multiple sources serialized in proprietary binary
formats. The data must be read by using .NET assemblies provided by Trey Research. The assemblies use a common set of dependencies. The current version of the Trey Research assemblies is 1.2.0.0. All assemblies provided by Trey Research are signed with a key pair contained in a file named Trey.snk, which Trey Research also supplies.
The application specification requires that any third-party assemblies must have strong names.

NEW QUESTION: 3
You need to configure versioning and logging for Azure Machine Learning models. Which Machine Learning service application should you use?
A. pipelines
B. activities
C. experiments
D. models
E. deployments
Answer: C

NEW QUESTION: 4

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