Pearson Exam Ref DP-100 Designing and Implementing a Data Science Solution on Azure

Dayne Sorvisto

Prepare for Microsoft Exam DP-100 and demonstrate your real-world knowledge of managing data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Python, Azure Machine Learning, and MLflow. Designed for professionals with data science experience, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified: Azure Data Scientist Associate level.

Focus on the expertise measured by these objectives:

Design and prepare a machine learning solution Explore data and train models Prepare a model for deployment Deploy and retrain a model

This Microsoft Exam Ref:

Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you have experience in designing and creating a suitable working environment for data science workloads, training machine learning models, and managing, deploying, and monitoring scalable machine learning solutions

About the Exam

Exam DP-100 focuses on knowledge needed to design and prepare a machine learning solution, manage an Azure Machine Learning workspace, explore data and train models, create models by using the Azure Machine Learning designer, prepare a model for deployment, manage models in Azure Machine Learning, deploy and retrain a model, and apply machine learning operations (MLOps) practices.

About Microsoft Certification

Passing this exam fulfills your requirements for the Microsoft Certified: Azure Data Scientist Associate credential, demonstrating your expertise in applying data science and machine learning to implement and run machine learning workloads on Azure, including knowledge and experience using Azure Machine Learning and MLflow.

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Published: October 29, 2024
Title: Exam Ref DP-100 Designing and Implementing a Data Science Solution on Azure
Author: Dayne Sorvisto
Edition: 1
ISBN: 9780135350591
Publisher: Pearson Education
Language: English
Formats: ePub

ISBNs:
9780135350607
9780135350584
9780135350591

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