DP-100 : Designing and Implementing a Data Science Solution on Azure : Part 01

Last Updated on November 4, 2022 by InfraExam

DP-100 : Designing and Implementing a Data Science Solution on Azure : Part 01

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.

   • Microsoft Hardware-Assisted Virtualization Detection Tool
   • Kitematic
   • BIOS-enabled virtualization
   • VirtualBox
   • Windows 10 64-bit Professional

   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.

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?

   • 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.
   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.

3. DRAG DROP

   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.

   

   

   Explanation:

   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.

4. 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)
   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.

5. 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.
   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.

6. 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
   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.

7. 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)
   Explanation:
   You can move data to and from Azure Blob storage using different technologies:
   – Azure Storage-Explorer
   – AzCopy
   – Python
   – SSIS

8. 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
   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.

9. 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
   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 libraries for machine learning in Python. It is on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.

10. 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
    Explanation:
    A Deep Learning Virtual Machine is a pre-configured environment for deep learning using GPU instances.

11. 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?

    • Data Science Virtual Machine for Windows 2012
    • Data Science Virtual Machine for Linux (CentOS)
    • Geo AI Data Science Virtual Machine with ArcGIS
    • Data Science Virtual Machine for Windows 2016
    • Data Science Virtual Machine for Linux (Ubuntu)
    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.

12. HOTSPOT

    You are performing sentiment analysis using a CSV file that includes 12,000 customer reviews written in a short sentence format. You add the CSV file to Azure Machine Learning Studio and configure it as the starting point dataset of an experiment. You add the Extract N-Gram Features from Text module to the experiment to extract key phrases from the customer review column in the dataset.

    You must create a new n-gram dictionary from the customer review text and set the maximum n-gram size to trigrams.

    What should you select? To answer, select the appropriate options in the answer area.

    NOTE: Each correct selection is worth one point.

    

    

    Explanation:

    Vocabulary mode: Create
    For Vocabulary mode, select Create to indicate that you are creating a new list of n-gram features.

    N-Grams size: 3
    For N-Grams size, type a number that indicates the maximum size of the n-grams to extract and store. For example, if you type 3, unigrams, bigrams, and trigrams will be created.

    Weighting function: Leave blank
    The option, Weighting function, is required only if you merge or update vocabularies. It specifies how terms in the two vocabularies and their scores should be weighted against each other.

13. You are developing a data science workspace that uses an Azure Machine Learning service.

    You need to select a compute target to deploy the workspace.

    What should you use?

    • Azure Data Lake Analytics
    • Azure Databricks
    • Azure Container Service
    • Apache Spark for HDInsight
    Explanation:
    Azure Container Instances can be used as compute target for testing or development. Use for low-scale CPU-based workloads that require less than 48 GB of RAM.

14. You are solving a classification task.

    The dataset is imbalanced.

    You need to select an Azure Machine Learning Studio module to improve the classification accuracy.

    Which module should you use?

    • Permutation Feature Importance
    • Filter Based Feature Selection
    • Fisher Linear Discriminant Analysis
    • Synthetic Minority Oversampling Technique (SMOTE)
    Explanation:

    Use the SMOTE module in Azure Machine Learning Studio (classic) to increase the number of underrepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.

    You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult to collect. Typically, you use SMOTE when the class you want to analyze is under-represented.

15. DRAG DROP

    You configure a Deep Learning Virtual Machine for Windows.

    You need to recommend tools and frameworks to perform the following:

    – Build deep neural network (DNN) models
    – Perform interactive data exploration and visualization

    Which tools and frameworks should you recommend? To answer, drag the appropriate tools to the correct tasks. Each tool may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

    NOTE: Each correct selection is worth one point.

    

    

    Explanation:

    Box 1: Vowpal Wabbit
    Use the Train Vowpal Wabbit Version 8 module in Azure Machine Learning Studio (classic), to create a machine learning model by using Vowpal Wabbit.

    Box 2: PowerBI Desktop
    Power BI Desktop is a powerful visual data exploration and interactive reporting tool
    BI is a name given to a modern approach to business decision making in which users are empowered to find, explore, and share insights from data across the enterprise.

16. You use Azure Machine Learning Studio to build a machine learning experiment.

    You need to divide data into two distinct datasets.

    Which module should you use?

    • Assign Data to Clusters
    • Load Trained Model
    • Partition and Sample
    • Tune Model-Hyperparameters
    Explanation: Partition and Sample with the Stratified split option outputs multiple datasets, partitioned using the rules you specified.

17. DRAG DROP

    You are creating an experiment by using Azure Machine Learning Studio.

    You must divide the data into four subsets for evaluation. There is a high degree of missing values in the data. You must prepare the data for analysis.

    You need to select appropriate methods for producing the experiment.

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

    NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

    

    

    Explanation:

    The Clean Missing Data module in Azure Machine Learning Studio, to remove, replace, or infer missing values.

    Incorrect Answers:
    – Latent Direchlet Transformation: Latent Dirichlet Allocation module in Azure Machine Learning Studio, to group otherwise unclassified text into a number of categories. Latent Dirichlet Allocation (LDA) is often used in natural language processing (NLP) to find texts that are similar. Another common term is topic modeling.
    – Build Counting Transform: Build Counting Transform module in Azure Machine Learning Studio, to analyze training data. From this data, the module builds a count table as well as a set of count-based features that can be used in a predictive model.
    – Missing Value Scrubber: The Missing Values Scrubber module is deprecated.
    Feature hashing: Feature hashing is used for linguistics, and works by converting unique tokens into integers.
    – Replace discrete values: the Replace Discrete Values module in Azure Machine Learning Studio is used to generate a probability score that can be used to represent a discrete value. This score can be useful for understanding the information value of the discrete values.

18. HOTSPOT

    You are retrieving data from a large datastore by using Azure Machine Learning Studio.

    You must create a subset of the data for testing purposes using a random sampling seed based on the system clock.

    You add the Partition and Sample module to your experiment.

    You need to select the properties for the module.

    Which values should you select? To answer, select the appropriate options in the answer area.

    NOTE: Each correct selection is worth one point.

    

    

    Explanation:

    Box 1: Sampling
    Create a sample of data
    This option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.
    1. Add the Partition and Sample module to your experiment in Studio, and connect the dataset.
    2. Partition or sample mode: Set this to Sampling.
    3. Rate of sampling. See box 2 below.

    Box 2: 0
    3. Rate of sampling. Random seed for sampling: Optionally, type an integer to use as a seed value.

    This option is important if you want the rows to be divided the same way every time. The default value is 0, meaning that a starting seed is generated based on the system clock. This can lead to slightly different results each time you run the experiment.

19. You are creating a machine learning model. You have a dataset that contains null rows.

    You need to use the Clean Missing Data module in Azure Machine Learning Studio to identify and resolve the null and missing data in the dataset.

    Which parameter should you use?

    • Replace with mean
    • Remove entire column
    • Remove entire row
    • Hot Deck
    • Custom substitution value
    • Replace with mode
    Explanation:
    Remove entire row: Completely removes any row in the dataset that has one or more missing values. This is useful if the missing value can be considered randomly missing.

20. HOTSPOT

    The finance team asks you to train a model using data in an Azure Storage blob container named finance-data.

    You need to register the container as a datastore in an Azure Machine Learning workspace and ensure that an error will be raised if the container does not exist.

    How should you complete the code? To answer, select the appropriate options in the answer area.

    NOTE: Each correct selection is worth one point.

    

    

    Explanation:

    Box 1: register_azure_blob_container
    Register an Azure Blob Container to the datastore.

    Box 2: create_if_not_exists = False
    Create the file share if it does not exist, defaults to False.