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Azure Data Scientist Associate Certification Guide

You're reading from  Azure Data Scientist Associate Certification Guide

Product type Book
Published in Dec 2021
Publisher Packt
ISBN-13 9781800565005
Pages 448 pages
Edition 1st Edition
Languages
Authors (2):
Andreas Botsikas Andreas Botsikas
Profile icon Andreas Botsikas
Michael Hlobil Michael Hlobil
Profile icon Michael Hlobil
View More author details

Table of Contents (17) Chapters

Preface Section 1: Starting your cloud-based data science journey
Chapter 1: An Overview of Modern Data Science Chapter 2: Deploying Azure Machine Learning Workspace Resources Chapter 3: Azure Machine Learning Studio Components Chapter 4: Configuring the Workspace Section 2: No code data science experimentation
Chapter 5: Letting the Machines Do the Model Training Chapter 6: Visual Model Training and Publishing Section 3: Advanced data science tooling and capabilities
Chapter 7: The AzureML Python SDK Chapter 8: Experimenting with Python Code Chapter 9: Optimizing the ML Model Chapter 10: Understanding Model Results Chapter 11: Working with Pipelines Chapter 12: Operationalizing Models with Code Other Books You May Enjoy

Questions

In each chapter, you will find a couple of questions to check your understanding of the topics discussed:.

  1. You want to log the number of validation rows you will use within a script. Which method of the Run class will you use?

    a. log_table

    b. log_row

    c. log

  2. You want to run a Python script that utilizes scikit-learn. How would you configure the AzureML environment?

    a. Add the scikit-learn Conda dependency.

    b. Add the sklearn Conda dependency.

    c. Use the AzureML Azure-Minimal environment, which already contains the needed dependencies.

  3. You need to use MLflow to track the metrics generated in an Experiment and store them in your AzureML workspace. Which two pip packages do you need to have in your Conda environment?

    a. mlflow

    b. azureml-mlflow

    c. sklearn

    d. logger

  4. You need to use MLflow to track the value 0.1 for the training_rate metric. Which of the following code achieves this requirement? Assume all classes are correctly imported at the top of the script:

    a. mlflow...

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