DP-100: Designing and Implementing a Data Science Solution on Azure

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.

Microsoft Hardware-Assisted Virtualization Detection Tool
Kitematic
BIOS-enabled virtualization
VirtualBox
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/

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?

Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.
Build the environment in Azure Databricks and use Azure Data Factory for orchestration.
Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.
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

You are building an intelligent solution using machine learning models.

The environment must support the following requirements:

Data scientists must build notebooks in a cloud environment
Data scientists must use automatic feature engineering and model building in machine learning pipelines.
Notebooks must be deployed to retrain using Spark instances with dynamic worker allocation.
Notebooks must be exportable to be version controlled locally.

You need to create the environment.

Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Correct answer: To work with this question, an Exam Simulator is required.

Explanation:

Step 1: Create an Azure HDInsight cluster to include the Apache Spark Mlib libraryStep 2: Install Microsot Machine Learning for Apache SparkYou install AzureML on your Azure HDInsight cluster. Microsoft Machine Learning for Apache Spark (MMLSpark) provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets. Step 3: Create and execute the Zeppelin notebooks on the clusterStep 4: When the cluster is ready, export Zeppelin notebooks to a local environment.Notebooks must be exportable to be version controlled locally. References: https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-zeppelin-notebookhttps://azuremlbuild.blob.core.windows.net/pysparkapi/intro.html

Step 1: Create an Azure HDInsight cluster to include the Apache Spark Mlib library

Step 2: Install Microsot Machine Learning for Apache Spark

You install AzureML on your Azure HDInsight cluster.

Microsoft Machine Learning for Apache Spark (MMLSpark) provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.

Step 3: Create and execute the Zeppelin notebooks on the cluster

Step 4: When the cluster is ready, export Zeppelin notebooks to a local environment.

Notebooks must be exportable to be version controlled locally.

References:

https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-zeppelin-notebook

https://azuremlbuild.blob.core.windows.net/pysparkapi/intro.html

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?

Azure Machine Learning Service
Azure Machine Learning Studio
Azure Databricks
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

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?

Create a Data Science Virtual Machine (DSVM) Windows edition.
Create a Geo Al Data Science Virtual Machine (Geo-DSVM) Windows edition.
Create a Deep Learning Virtual Machine (DLVM) Linux edition.
Create a Deep Learning Virtual Machine (DLVM) Windows edition.

Correct answer: A

Explanation:

In the DSVM, your training models can use deep learning algorithms on hardware that's based on graphics processing units (GPUs). PostgreSQL is available for the following operating systems: Linux (all recent distributions), 64-bit installers available for macOS (OS X) version 10.6 and newer – Windows (with installers available for 64-bit version; tested on latest versions and back to Windows 2012 R2. Incorrect Answers: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. C, 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. Reference:https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

In the DSVM, your training models can use deep learning algorithms on hardware that's based on graphics processing units (GPUs).

PostgreSQL is available for the following operating systems: Linux (all recent distributions), 64-bit installers available for macOS (OS X) version 10.6 and newer – Windows (with installers available for 64-bit version;

tested on latest versions and back to Windows 2012 R2.

Incorrect Answers:

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.

C, 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.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

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?

Azure Cognitive Services
Azure Data Lake Analytics
Azure HDInsight with Spark MLib
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

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.

Bulk Insert SQL Query
AzCopy
Python script
Azure Storage Explorer
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

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?

Convert to CSV
Convert to Dataset
Convert to ARFF
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

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?

Rattle
TensorFlow
Weka
Scikit-learn

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.D: Scikit-learn is one of the most useful library for machine learning in Python. It is on NumPy, SciPy and matplotlib, this library contains a lot of effiecient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction. Reference: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.

D: Scikit-learn is one of the most useful library for machine learning in Python. It is on NumPy, SciPy and matplotlib, this library contains a lot of effiecient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.

Reference:

https://www.infoworld.com/article/3278008/what-is-tensorflow-the-machine-learning-library-explained.html

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?

Solid State Drives (SSD)
Computer Processing Unit (CPU) speed increase by using overclocking
Graphic Processing Unit (GPU)
High Random Access Memory (RAM) configuration
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

Vendor:	Microsoft
Exam Code:	DP-100
Exam Name:	Designing and Implementing a Data Science Solution on Azure
Date:	Mar 08, 2021
File Size:	14 MB

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