Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 361:

  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?

  A. Azure Data Lake Analytics

  B. Azure Databricks

  C. Azure Container Service

  D. Apache Spark for HDInsight

  Correct Answer: C

  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.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where

• Question 362:

  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?

  A. Permutation Feature Importance

  B. Filter Based Feature Selection

  C. Fisher Linear Discriminant Analysis

  D. Synthetic Minority Oversampling Technique (SMOTE)

  Correct Answer: D

  Use the SMOTE module in Azure Machine Learning Studio (classic) to increase the number of underepresented 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.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

• Question 363:

  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?

  A. Solid State Drives (SSD)

  B. Computer Processing Unit (CPU) speed increase by using overclocking

  C. Graphic Processing Unit (GPU)

  D. High Random Access Memory (RAM) configuration

  E. Intel Software Guard Extensions (Intel SGX) technology

  Correct Answer: C

  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 364:

  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?

  A. Rattle

  B. TensorFlow

  C. Weka

  D. Scikit-learn

  Correct Answer: B

  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

• Question 365:

  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?

  A. Convert to CSV

  B. Convert to Dataset

  C. Convert to ARFF

  D. Convert to SVMLight

  Correct Answer: C

  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 366:

  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.

  A. Bulk Insert SQL Query

  B. AzCopy

  C. Python script

  D. Azure Storage Explorer

  E. Bulk Copy Program (BCP)

  Correct Answer: BCD

  You can move data to and from Azure Blob storage using different technologies:

  1.

  Azure Storage-Explorer

  2.

  AzCopy

  3.

  Python

  4.

  SSIS

  References: https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/move-azure-blob

• Question 367:

  You are developing deep learning models to analyze semi-structured, unstructured, and structured data types.

  You have the following data available for model building:

  1.

  Video recordings of sporting events

  2.

  Transcripts of radio commentary about events

  3.

  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?

  A. Azure Cognitive Services

  B. Azure Data Lake Analytics

  C. Azure HDInsight with Spark MLib

  D. Azure Machine Learning Studio

  Correct Answer: A

  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 368:

  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.

  A. Microsoft Hardware-Assisted Virtualization Detection Tool

  B. Kitematic

  C. BIOS-enabled virtualization

  D. VirtualBox

  E. Windows 10 64-bit Professional

  Correct Answer: CE

  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 369:

  Your team is building a data engineering and data science development environment. The environment must support the following requirements:

  1.

  support Python and Scala

  2.

  compose data storage, movement, and processing services into automated data pipelines

  3.

  the same tool should be used for the orchestration of both data engineering and data science

  4.

  support workload isolation and interactive workloads

  5.

  enable scaling across a cluster of machines

  You need to create the environment.

  What should you do?

  A. Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.

  B. Build the environment in Azure Databricks and use Azure Data Factory for orchestration.

  C. Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.

  D. Build the environment in Azure Databricks and use Azure Container Instances for orchestration.

  Correct Answer: B

  In Azure Databricks, we can create two different types of clusters.

  1.

  Standard, these are the default clusters and can be used with Python, R, Scala and SQL

  2.

  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 370:

  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?

  A. Create a Data Science Virtual Machine (DSVM) Windows edition.

  B. Create a Geo Al Data Science Virtual Machine (Geo-DSVM) Windows edition.

  C. Create a Deep Learning Virtual Machine (DLVM) Linux edition.

  D. Create a Deep Learning Virtual Machine (DLVM) Windows edition.

  E. Create a Data Science Virtual Machine (DSVM) Linux edition.

  Correct Answer: E

  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