Book Image

Azure Data Scientist Associate Certification Guide

By : Andreas Botsikas, Michael Hlobil
Book Image

Azure Data Scientist Associate Certification Guide

By: Andreas Botsikas, Michael Hlobil

Overview of this book

The Azure Data Scientist Associate Certification Guide helps you acquire practical knowledge for machine learning experimentation on Azure. It covers everything you need to pass the DP-100 exam and become a certified Azure Data Scientist Associate. Starting with an introduction to data science, you'll learn the terminology that will be used throughout the book and then move on to the Azure Machine Learning (Azure ML) workspace. You'll discover the studio interface and manage various components, such as data stores and compute clusters. Next, the book focuses on no-code and low-code experimentation, and shows you how to use the Automated ML wizard to locate and deploy optimal models for your dataset. You'll also learn how to run end-to-end data science experiments using the designer provided in Azure ML Studio. You'll then explore the Azure ML Software Development Kit (SDK) for Python and advance to creating experiments and publishing models using code. The book also guides you in optimizing your model's hyperparameters using Hyperdrive before demonstrating how to use responsible AI tools to interpret and debug your models. Once you have a trained model, you'll learn to operationalize it for batch or real-time inferences and monitor it in production. By the end of this Azure certification study guide, you'll have gained the knowledge and the practical skills required to pass the DP-100 exam.
Table of Contents (17 chapters)
1
Section 1: Starting your cloud-based data science journey
6
Section 2: No code data science experimentation
9
Section 3: Advanced data science tooling and capabilities

Questions

In each chapter, you should find a couple of questions that will allow you to perform a knowledge check on the topics discussed in this chapter:

  1. Which of the following are applicable ways of deploying the Azure ML workspace?

    a. Azure CLI through the azure-cli-ml extension

    b. The Azure portal

    c. The deployment of an ARM template

    d. Azure ML Python SDK

  2. You are creating a custom role and you want to deny the ability to delete a workspace. Where do you need to add the Microsoft.MachineLearningServices/workspaces/delete action?

    a. To the Actions section of the JSON definition

    b. To the NotActions section of the JSON definition

    c. To the AssignableScopes section of the JSON definition

  3. What do you have to install in the Azure CLI before you can deploy an Azure ML workspace?