Download Designing and Implementing a Data Science Solution on Azure.DP-100.PracticeTest.2019-09-05.38q.tqb

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

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

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 theAI 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

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 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

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

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

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

Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values. Note: Multivariate imputation by chained equations (MICE), sometimes called “fully conditional specification” or “sequential regression multiple imputation” has emerged in the statistical literature as one principled method of addressing missing data. Creating multiple imputations, as opposed to single imputations, accounts for the statistical uncertainty in the imputations. In addition, the chained equations approach is very flexible and can handle variables of varying types (e.g., continuous or binary) as well as complexities such as bounds or survey skip patterns. References:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

Use the Multiple Imputation by Chained Equations (MICE) method. References:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data