Exam 70-735 OEM Manufacturing and Deployment for Windows 10

Published: June 19, 2017
Languages: English
Audiences: IT Professionals
Technology: Windows 10
Credit toward certification: MCP

Skills measured
This exam measures your ability to accomplish the technical tasks listed below. The percentages indicate the relative weight of each major topic area on the exam. The higher the percentage, the more questions you are likely to see on that content area on the exam. View video tutorials about the variety of question types on Microsoft exams.

Please note that the questions may test on, but will not be limited to, the topics described in the bulleted text.

Do you have feedback about the relevance of the skills measured on this exam? Please send Microsoft your comments. All feedback will be reviewed and incorporated as appropriate while still maintaining the validity and reliability of the certification process. Note that Microsoft will not respond directly to your feedback. We appreciate your input in ensuring the quality of the Microsoft Certification program.

If you have concerns about specific questions on this exam, please submit an exam challenge.

If you have other questions or feedback about Microsoft Certification exams or about the certification program, registration, or promotions, please contact your Regional Service Center.

Prepare the Imaging Environment (20-25%)
Install deployment tools and scripts
Prepare the Windows Assessment and Deployment Kit (Windows ADK), prepare the required tools from the Windows ADK installation
Add customizations to the image
Use tools to design an answer file that will add branding to the device, add OEM information such as support URL or phone support number, provide the default product key for OEM Activation implementation, set the default user languages, add the custom logo and wallpaper
Create a Windows Preinstallation Environment (Windows PE)
Use the Windows ADK scripts to create the Windows PE source files, add optional packages, add default languages, add custom scripts, create a bootable USB or ISO file of the Windows PE, add device drivers

Service the Offline Image (40-45%)
Add drivers to the image
Choose the recommended installation paths for adding drivers, add INF-based drivers offline, add INF-based drivers from a folder path using deployment tools
Add language packs to the image
Distinguish the difference between a language pack and a language interface pack; determine when to use Feature on Demand language packs, how to apply the ordering of Feature on Demand language packs when adding new languages to the image, and which language packs should be applied to the Recovery image; set the default time zone in the image; set the default input and system locales in the image
Add update packages to the image
Choose which updates to apply, select which updates to apply to Windows image and Recovery image
Service in-box applications
Reapply in-box applications, select the appropriate dependency packages for each application bundle, troubleshoot installation failures, pin apps to Start layout and taskbar
Optimize the image
Mark updates in a Recovery image as permanent, export a Recovery image, set scratch space size, check the overall size of a Recovery image for partition layout schemes
Deploy the image
Select the disk partition layout, run DISM to apply the image, set up the recovery environment, boot into Audit mode for online servicing

Service the Online Image (35-40%)
Preinstall Office 2016
Prepare office files for preinstallation, create configuration files, add multiple languages, set up the first user experience
Create restore packages
Use ScanState to create restore packages of installed desktop applications, registry settings, and application settings
Prepare the recovery environment
Create extensibility scripts, create configuration files, create migration files, copy backup files to the recovery folder for Push Button Reset
Reseal the image
Use Sysprep to reseal the image to OOBE, boot to Windows PE for final capture, optimize the image for disk footprint, mark update packages as permanent, optimize the image for deployment, use deployment tools to capture the final image for mass deployment
Deploy and validate the image
Test the final image deployment, verify that settings are correct, the image passes system validation tests, and Push Button Reset restores the image to its correct state

QUESTION 1
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are building a new image of Windows 10 that contains a push-button reset solution.
You need to test whether push-button reset works as expected.
Solution: From Windows 10. you press and hold the SHIFT key, and then you restart the computer. After the computer restarts, you click Troubleshoot, and then you click Reset this PC.
Does this meet the goal?

A. Yes
B. No

Answer: B


QUESTION 2
Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question Is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You have a computer named Computer1 that runs Windows 10. Computer1 has the Windows Assessment and Deployment Kit (Windows ADK) installed.
You are building a new image of Windows 10.
You copy the installation media for Windows 10 to Computer1.
You need to add drivers to the Windows 10 image.

A. Mount the Install.wim file.
B. Mount the Boot.wim file.
C. Modify the Winpeshl.ini file.
D. Create an answer file.
E. Modify the Windows.ini file.
F. Create a provisioning package.
G. Load a catalog file (.clg).
H. Create a cabinet file (.cab).

Answer: B


QUESTION 3
This question requires that you evaluate the underlined text to determine if it is correct.
To provide the default product key for OEM activation, you create an answer file by using Windows System Image Manager (Windows SIM), and you add the Microsoft-Windows-Shell-Setup component and the ProductKey component to the generalize pass.
Review the underlined text. It it makes the statement correct, select “No change is needed.” If the statement is incorrect, select the answer choice that makes the statement correct.

A. No change is needed
B. auditSystem pass
C. specialize pass
D. windowsPE pass

Answer: C


QUESTION 4
You deploy an image of Windows 10.
From audit mode, you install several applications for a customer, and then you run sysprep.exe /oobe /quit.
You need to identify whether any errors occurred when you ran sysprep.exe.
Which folder contains the log files?

A. %WINDIR%\Logs
B. %WlNDIR%\System32\LogFiles
C. %WINDIR%\Panther\
D. %WINDIR%\System32\Sysprep\Panther

Answer: D

 

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70-774 Perform Cloud Data Science with Azure Machine Learning

Published: February 14, 2017
Languages: English
Audiences: Data scientists
Technology: Azure Machine Learning, Bot Framework, Cognitive Services
Credit toward certification: MCSE

Skills measured
This exam measures your ability to accomplish the technical tasks listed below. View video tutorials about the variety of question types on Microsoft exams.

Please note that the questions may test on, but will not be limited to, the topics described in the bulleted text.

Do you have feedback about the relevance of the skills measured on this exam? Please send Microsoft your comments. All feedback will be reviewed and incorporated as appropriate while still maintaining the validity and reliability of the certification process. Note that Microsoft will not respond directly to your feedback. We appreciate your input in ensuring the quality of the Microsoft Certification program.

If you have concerns about specific questions on this exam, please submit an exam challenge.

If you have other questions or feedback about Microsoft Certification exams or about the certification program, registration, or promotions, please contact your Regional Service Center.

Prepare Data for Analysis in Azure Machine Learning and Export from Azure Machine Learning
Import and export data to and from Azure Machine Learning
Import and export data to and from Azure Blob storage, import and export data to and from Azure SQL Database, import and export data via Hive Queries, import data from a website, import data from on-premises SQL
Explore and summarize data
Create univariate summaries, create multivariate summaries, visualize univariate distributions, use existing Microsoft R or Python notebooks for custom summaries and custom visualizations, use zip archives to import external packages for R or Python
Cleanse data for Azure Machine Learning
Apply filters to limit a dataset to the desired rows, identify and address missing data, identify and address outliers, remove columns and rows of datasets
Perform feature engineering
Merge multiple datasets by rows or columns into a single dataset by columns, merge multiple datasets by rows or columns into a single dataset by rows, add columns that are combinations of other columns, manually select and construct features for model estimation, automatically select and construct features for model estimation, reduce dimensions of data through principal component analysis (PCA), manage variable metadata, select standardized variables based on planned analysis

Develop Machine Learning Models
Select an appropriate algorithm or method
Select an appropriate algorithm for predicting continuous label data, select an appropriate algorithm for supervised versus unsupervised scenarios, identify when to select R versus Python notebooks, identify an appropriate algorithm for grouping unlabeled data, identify an appropriate algorithm for classifying label data, select an appropriate ensemble
Initialize and train appropriate models
Tune hyperparameters manually; tune hyperparameters automatically; split data into training and testing datasets, including using routines for cross-validation; build an ensemble using the stacking method
Validate models
Score and evaluate models, select appropriate evaluation metrics for clustering, select appropriate evaluation metrics for classification, select appropriate evaluation metrics for regression, use evaluation metrics to choose between Machine Learning models, compare ensemble metrics against base models

Operationalize and Manage Azure Machine Learning Services
Deploy models using Azure Machine Learning
Publish a model developed inside Azure Machine Learning, publish an externally developed scoring function using an Azure Machine Learning package, use web service parameters, create and publish a recommendation model, create and publish a language understanding model
Manage Azure Machine Learning projects and workspaces
Create projects and experiments, add assets to a project, create new workspaces, invite users to a workspace, switch between different workspaces, create a Jupyter notebook that references an intermediate dataset
Consume Azure Machine Learning models
Connect to a published Machine Learning web service, consume a published Machine Learning model programmatically using a batch execution service, consume a published Machine Learning model programmatically using a request response service, interact with a published Machine Learning model using Microsoft Excel, publish models to the marketplace
Consume exemplar Cognitive Services APIs
Consume Vision APIs to process images, consume Language APIs to process text, consume Knowledge APIs to create recommendations

Use Other Services for Machine Learning
Build and use neural networks with the Microsoft Cognitive Toolkit
Use N-series VMs for GPU acceleration, build and train a three-layer feed forward neural network, determine when to implement a neural network
Streamline development by using existing resources
Clone template experiments from Cortana Intelligence Gallery, use Cortana Intelligence Quick Start to deploy resources, use a data science VM for streamlined development
Perform data sciences at scale by using HDInsights
Deploy the appropriate type of HDI cluster, perform exploratory data analysis by using Spark SQL, build and use Machine Learning models with Spark on HDI, build and use Machine Learning models using MapReduce, build and use Machine Learning models using Microsoft R Server
Perform database analytics by using SQL Server R Services on Azure
Deploy a SQL Server 2016 Azure VM, configure SQL Server to allow execution of R scripts, execute R scripts inside T-SQL statements

QUESTION 1
You are building an Azure Machine Learning Solution for an Online retailer.
When a customer selects a product, you need to recommend products that the customer might like to purchase at the same time. The recommendation should be based on what other customers purchased the same product.
Which model should you use?

A. Collaborative Filtering
B. Boosted Decision Tree Regression Model
C. Two-Class boosted decision tree
D. K-Means Clustering

Answer: A


QUESTION 2
You are analyzing taxi trips in New York City. You leverage the Azure Data Factory to create data pipelines and to orchestrate data movement.
You plan to develop a predictive model for 170 million rows (37 GB) of raw data in Apache Hive by using Microsoft R Serve to identify which factors contributes to the passenger tipping behavior.
All of the platforms that are used for the analysis are the same. Each worker node has eight processor cores and 28 GB Of memory.
Which type of Azure HDInsight cluster should you use to produce results as quickly as possible?

A. Hadoop
B. HBase
C. Interactive Hive
D. Spark

Answer: A


QUESTION 3
Note: This question is part of a series of questions that present the same Scenario.
Each question I the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution while others might not have correct solution.
Start of repeated Scenario:
A Travel agency named Margie’s Travel sells airline tickets to customers in the United States.
Margie’s Travel wants you to provide insights and predictions on flight delays. The agency is considering implementing a system that will communicate to its customers as the flight departure near about possible delays due to weather conditions.
The flight data contains the following attributes:
* DepartureDate: The departure date aggregated at a per hour granularity.
* Carrier: The code assigned by the IATA and commonly used to identify a carrier.
* OriginAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight’s Origin)
* DestAirportID: The departure delay in minutes.
*DepDet30: A Boolean value indicating whether the departure was delayed by 30 minutes or more ( a value of 1 indicates that the departure was delayed by 30 minutes or more)
The weather data contains the following Attributes: AirportID, ReadingDate (YYYY/MM/DD HH), SKYConditionVisibility, WeatherType, Windspeed, StationPressure, PressureChange and HourlyPrecip.
End of repeated Scenario:
You plan to predict flight delays that are 30 minutes or more.
You need to build a training model that accurately fits the data. The solution must minimize over fitting and minimize data leakage. Which attribute should you remove?

A. OriginAirportID
B. DepDel
C. DepDel30
D. Carrier
E. DestAirportID

Answer: B

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