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Exam Designing and Implementing a Data Science Solution on Azure
Number DP-100
File Name Microsoft.DP-100.PassGuide.2019-11-16.48q.vcex
Size 2 MB
Posted Nov 16, 2019
Download Microsoft.DP-100.PassGuide.2019-11-16.48q.vcex

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Demo Questions

Question 1

You are developing a hands-on workshop to introduce Docker for Windows to attendees. 
You need to ensure that workshop attendees can install Docker on their devices. 
Which two prerequisite components should attendees install on the devices? Each correct answer presents part of the solution. 
NOTE: Each correct selection is worth one point.


  1. Microsoft Hardware-Assisted Virtualization Detection Tool
  2. Kitematic
  3. BIOS-enabled virtualization
  4. VirtualBox
  5. Windows 10 64-bit Professional
Correct answer: CE
Explanation:
C: Make sure your Windows system supports Hardware Virtualization Technology and that virtualization is enabled.Ensure that hardware virtualization support is turned on in the BIOS settings. For example:    E: To run Docker, your machine must have a 64-bit operating system running Windows 7 or higher. References:https://docs.docker.com/toolbox/toolbox_install_windows/https://blogs.technet.microsoft.com/canitpro/2015/09/08/step-by-step-enabling-hyper-v-for-use-on-windows-10/
C: Make sure your Windows system supports Hardware Virtualization Technology and that virtualization is enabled.
Ensure that hardware virtualization support is turned on in the BIOS settings. For example: 
 
E: To run Docker, your machine must have a 64-bit operating system running Windows 7 or higher. 
References:
https://docs.docker.com/toolbox/toolbox_install_windows/
https://blogs.technet.microsoft.com/canitpro/2015/09/08/step-by-step-enabling-hyper-v-for-use-on-windows-10/



Question 2

Your team is building a data engineering and data science development environment. 
The environment must support the following requirements:
  • support Python and Scala 
  • compose data storage, movement, and processing services into automated data pipelines 
  • the same tool should be used for the orchestration of both data engineering and data science 
  • support workload isolation and interactive workloads 
  • enable scaling across a cluster of machines 
You need to create the environment. 
What should you do?


  1. Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.
  2. Build the environment in Azure Databricks and use Azure Data Factory for orchestration.
  3. Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.
  4. Build the environment in Azure Databricks and use Azure Container Instances for orchestration.
Correct answer: B
Explanation:
In Azure Databricks, we can create two different types of clusters. Standard, these are the default clusters and can be used with Python, R, Scala and SQL High-concurrency Azure Databricks is fully integrated with Azure Data Factory. Incorrect Answers:D: Azure Container Instances is good for development or testing. Not suitable for production workloads.References:https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning
In Azure Databricks, we can create two different types of clusters. 
  • Standard, these are the default clusters and can be used with Python, R, Scala and SQL 
  • High-concurrency 
Azure Databricks is fully integrated with Azure Data Factory. 
Incorrect Answers:
D: Azure Container Instances is good for development or testing. Not suitable for production workloads.
References:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning



Question 3

You plan to build a team data science environment. Data for training models in machine learning pipelines will be over 20 GB in size. 
You have the following requirements:
  • Models must be built using Caffe2 or Chainer frameworks. 
  • Data scientists must be able to use a data science environment to build the machine learning pipelines and train models on their personal devices in both connected and disconnected network environments. 
Personal devices must support updating machine learning pipelines when connected to a network. 
You need to select a data science environment. 
Which environment should you use?


  1. Azure Machine Learning Service
  2. Azure Machine Learning Studio
  3. Azure Databricks
  4. Azure Kubernetes Service (AKS)
Correct answer: A
Explanation:
The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft’s Azure cloud built specifically for doing data science. Caffe2 and Chainer are supported by DSVM. DSVM integrates with Azure Machine Learning. Incorrect Answers:B: Use Machine Learning Studio when you want to experiment with machine learning models quickly and easily, and the built-in machine learning algorithms are sufficient for your solutions.References:https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview
The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft’s Azure cloud built specifically for doing data science. Caffe2 and Chainer are supported by DSVM. 
DSVM integrates with Azure Machine Learning. 
Incorrect Answers:
B: Use Machine Learning Studio when you want to experiment with machine learning models quickly and easily, and the built-in machine learning algorithms are sufficient for your solutions.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview



Question 4

You are implementing a machine learning model to predict stock prices. 
The model uses a PostgreSQL database and requires GPU processing. 
You need to create a virtual machine that is pre-configured with the required tools. 
What should you do?


  1. Create a Data Science Virtual Machine (DSVM) Windows edition.
  2. Create a Geo Al Data Science Virtual Machine (Geo-DSVM) Windows edition.
  3. Create a Deep Learning Virtual Machine (DLVM) Linux edition.
  4. Create a Deep Learning Virtual Machine (DLVM) Windows edition.
  5. Create a Data Science Virtual Machine (DSVM) Linux edition.
Correct answer: E
Explanation:
Incorrect Answers:A, C: PostgreSQL (CentOS) is only available in the Linux Edition.B: The Azure Geo AI Data Science VM (Geo-DSVM) delivers geospatial analytics capabilities from Microsoft's Data Science VM. Specifically, this VM extends the AI and data science toolkits in the Data Science VM by adding ESRI's market-leading ArcGIS Pro Geographic Information System.D: DLVM is a template on top of DSVM image. In terms of the packages, GPU drivers etc are all there in the DSVM image. Mostly it is for convenience during creation where we only allow DLVM to be created on GPU VM instances on Azure.References:https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview
Incorrect Answers:
A, C: PostgreSQL (CentOS) is only available in the Linux Edition.
B: The Azure Geo AI Data Science VM (Geo-DSVM) delivers geospatial analytics capabilities from Microsoft's Data Science VM. Specifically, this VM extends the AI and data science toolkits in the Data Science VM by adding ESRI's market-leading ArcGIS Pro Geographic Information System.
D: DLVM is a template on top of DSVM image. In terms of the packages, GPU drivers etc are all there in the DSVM image. Mostly it is for convenience during creation where we only allow DLVM to be created on GPU VM instances on Azure.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview



Question 5

You are developing deep learning models to analyze semi-structured, unstructured, and structured data types. 
You have the following data available for model building:
  • Video recordings of sporting events 
  • Transcripts of radio commentary about events 
  • Logs from related social media feeds captured during sporting events 
You need to select an environment for creating the model. 
Which environment should you use?


  1. Azure Cognitive Services
  2. Azure Data Lake Analytics
  3. Azure HDInsight with Spark MLib
  4. Azure Machine Learning Studio
Correct answer: A
Explanation:
Azure Cognitive Services expand on Microsoft’s evolving portfolio of machine learning APIs and enable developers to easily add cognitive features – such as emotion and video detection; facial, speech, and vision recognition; and speech and language understanding – into their applications. The goal of Azure Cognitive Services is to help developers create applications that can see, hear, speak, understand, and even begin to reason. The catalog of services within Azure Cognitive Services can be categorized into five main pillars - Vision, Speech, Language, Search, and Knowledge. References:https://docs.microsoft.com/en-us/azure/cognitive-services/welcome
Azure Cognitive Services expand on Microsoft’s evolving portfolio of machine learning APIs and enable developers to easily add cognitive features – such as emotion and video detection; facial, speech, and vision recognition; and speech and language understanding – into their applications. The goal of Azure Cognitive Services is to help developers create applications that can see, hear, speak, understand, and even begin to reason. The catalog of services within Azure Cognitive Services can be categorized into five main pillars - Vision, Speech, Language, Search, and Knowledge. 
References:
https://docs.microsoft.com/en-us/azure/cognitive-services/welcome



Question 6

You must store data in Azure Blob Storage to support Azure Machine Learning. 
You need to transfer the data into Azure Blob Storage. 
What are three possible ways to achieve the goal? Each correct answer presents a complete solution. 
NOTE: Each correct selection is worth one point.


  1. Bulk Insert SQL Query
  2. AzCopy
  3. Python script
  4. Azure Storage Explorer
  5. Bulk Copy Program (BCP)
Correct answer: BCD
Explanation:
You can move data to and from Azure Blob storage using different technologies: Azure Storage-Explorer AzCopy Python SSIS References:https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/move-azure-blob
You can move data to and from Azure Blob storage using different technologies: 
  • Azure Storage-Explorer 
  • AzCopy 
  • Python 
  • SSIS 
References:
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/move-azure-blob



Question 7

You are moving a large dataset from Azure Machine Learning Studio to a Weka environment. 
You need to format the data for the Weka environment. 
Which module should you use?


  1. Convert to CSV
  2. Convert to Dataset
  3. Convert to ARFF
  4. Convert to SVMLight
Correct answer: C
Explanation:
Use the Convert to ARFF module in Azure Machine Learning Studio, to convert datasets and results in Azure Machine Learning to the attribute-relation file format used by the Weka toolset. This format is known as ARFF. The ARFF data specification for Weka supports multiple machine learning tasks, including data preprocessing, classification, and feature selection. In this format, data is organized by entites and their attributes, and is contained in a single text file. References:https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-arff
Use the Convert to ARFF module in Azure Machine Learning Studio, to convert datasets and results in Azure Machine Learning to the attribute-relation file format used by the Weka toolset. This format is known as ARFF. 
The ARFF data specification for Weka supports multiple machine learning tasks, including data preprocessing, classification, and feature selection. In this format, data is organized by entites and their attributes, and is contained in a single text file. 
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-arff



Question 8

You plan to create a speech recognition deep learning model. 
The model must support the latest version of Python. 
You need to recommend a deep learning framework for speech recognition to include in the Data Science Virtual Machine (DSVM). 
What should you recommend?


  1. Rattle
  2. TensorFlow
  3. Weka
  4. Deeplearning4j
Correct answer: B
Explanation:
TensorFlow is an open source library for numerical computation and large-scale machine learning. It uses Python to provide a convenient front-end API for building applications with the framework TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations. Incorrect Answers:A: Rattle is the R analytical tool that gets you started with data analytics and machine learning.C: Weka is used for visual data mining and machine learning software in Java.References:https://www.infoworld.com/article/3278008/what-is-tensorflow-the-machine-learning-library-explained.html
TensorFlow is an open source library for numerical computation and large-scale machine learning. It uses Python to provide a convenient front-end API for building applications with the framework 
TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations. 
Incorrect Answers:
A: Rattle is the R analytical tool that gets you started with data analytics and machine learning.
C: Weka is used for visual data mining and machine learning software in Java.
References:
https://www.infoworld.com/article/3278008/what-is-tensorflow-the-machine-learning-library-explained.html



Question 9

You plan to use a Deep Learning Virtual Machine (DLVM) to train deep learning models using Compute Unified Device Architecture (CUDA) computations. 
You need to configure the DLVM to support CUDA. 
What should you implement?


  1. Solid State Drives (SSD)
  2. Computer Processing Unit (CPU) speed increase by using overclocking
  3. Graphic Processing Unit (GPU)
  4. High Random Access Memory (RAM) configuration
  5. Intel Software Guard Extensions (Intel SGX) technology
Correct answer: C
Explanation:
A Deep Learning Virtual Machine is a pre-configured environment for deep learning using GPU instances. References:https://azuremarketplace.microsoft.com/en-au/marketplace/apps/microsoft-ads.dsvm-deep-learning
A Deep Learning Virtual Machine is a pre-configured environment for deep learning using GPU instances. 
References:
https://azuremarketplace.microsoft.com/en-au/marketplace/apps/microsoft-ads.dsvm-deep-learning



Question 10

You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and PyTorch. 
You need to select a pre-configured DSVM to support the frameworks. 
What should you create?


  1. Data Science Virtual Machine for Windows 2012
  2. Data Science Virtual Machine for Linux (CentOS)
  3. Geo AI Data Science Virtual Machine with ArcGIS
  4. Data Science Virtual Machine for Windows 2016
  5. Data Science Virtual Machine for Linux (Ubuntu)
Correct answer: E
Explanation:
Caffe2 and PyTorch is supported by Data Science Virtual Machine for Linux. Microsoft offers Linux editions of the DSVM on Ubuntu 16.04 LTS and CentOS 7.4. Only the DSVM on Ubuntu is preconfigured for Caffe2 and PyTorch. Incorrect Answers:D: Caffe2 and PytOCH are only supported in the Data Science Virtual Machine for Linux.References:https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview
Caffe2 and PyTorch is supported by Data Science Virtual Machine for Linux. 
Microsoft offers Linux editions of the DSVM on Ubuntu 16.04 LTS and CentOS 7.4. 
Only the DSVM on Ubuntu is preconfigured for Caffe2 and PyTorch. 
Incorrect Answers:
D: Caffe2 and PytOCH are only supported in the Data Science Virtual Machine for Linux.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview









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