DP-100 Exam Study Guide (Designing and Implementing a Data Science Solution on Azure)

DP-100 Designing and Implementing a Data Science Solution on Azure Exam Certification study guide

Preparing for DP-100 Designing and Implementing a Data Science Solution on the Azure Certificate exam? Don’t know where to start? This post is the DP-100 Certificate Study Guide (with links to each exam objective).

I have curated a list of articles from Microsoft documentation for each objective of the DP-100 exam. I hope this article will help you to prepare for the DP-100 Certification exam. Also, please share the post within your circles so it helps them to prepare for the exam.

DP-100 Azure Data Science Online Course

DP-100 Azure Data Science Practice Test & Lab

Other Data Science Learning Programs

Looking for DP-100 Dumps? Read This!

Using dp-100 exam dumps can get you permanently banned from taking any future Microsoft certificate exam. Read the FAQ page for more information. However, I strongly suggest you validate your understanding with practice questions.

Not Sure Which Exam Is Right for You?

Confused between AI-100 and DP-100? You are not alone. Read this blog post and choose the one that’s right for you!

DP-100 Sample Practice Exam Questions

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Full Disclosure: Some of the links in this post are affiliate links. I receive a commission when you purchase through them.

Set up an Azure Machine Learning Workspace (30-35%)

Create an Azure Machine Learning Workspace

Create an Azure Machine Learning workspace

Create & manage Azure Machine Learning workspaces

Configure workspace settings

Azure security workspace setting

Manage a workspace by using Azure Machine Learning Studio

Manage an Azure Machine Learning Studio workspace

Manage Data Objects in an Azure Machine Learning Workspace

Register and maintain datastores

Create & register datastores

Create and manage datasets

Create datasets

Manage Experiment Compute Contexts

Create a compute instance

What is an Azure Machine Learning compute instance?

Determine appropriate compute specifications for a training workload

Configure & submit training runs

Create compute targets for experiments and training

What are compute targets in Azure Machine Learning?

Set up compute targets for model training & deployment

Amazon link (affiliate)

Run Experiments and Train Models (25-30%)

Create Models by Using Azure Machine Learning Designer

Create a training pipeline by using Azure Machine Learning designer

Retrain models with Azure Machine Learning designer

Ingest data in a designer pipeline

Data ingestion options for Azure Machine Learning workflows

Use designer modules to define a pipeline data flow

Predict automobile price with the designer

Use custom code modules in the designer

Define custom R modules for Machine Learning Studio (classic)

Run Training Scripts in an Azure Machine Learning Workspace

Create and run an experiment by using the Azure Machine Learning SDK

Azure Machine Learning in Jupyter Notebooks

Configure run settings for a script

Configure and submit training runs

Script Run Config class

Consume data from a dataset in an experiment by using the Azure Machine Learning SDK

Create and register datastores

Consume data from a dataset in an experiment by using the Azure Machine Learning SDK

Create datasets

Generate Metrics from an Experiment Run

Automate the Model Training Process

Optimize and Manage Models (20-25%)

Use Automated ML to Create Optimal Models

Use the Automated ML interface in Azure Machine Learning studio

Forecast demand with automated machine learning

Use Automated ML from the Azure Machine Learning SDK

Configure automated ML experiments in Python

Select pre-processing options

Preprocessing Data in Azure Machine Learning Studio

What is automated machine learning (AutoML)?

Determine algorithms to be searched

How to select algorithms for Azure Machine Learning

Define a primary metric

The primary metric

Get data for an Automated ML run

Explore the results

Retrieve the best model

Retrieve the best model

Use Hyperdrive to Tune Hyperparameters

Use Model Explainers to Interpret Models

Manage Models

Deploy and Consume Models (20-25%)

Create Production Compute Targets

Consider security for deployed services

Use TLS to secure a web service through Azure ML

Evaluate compute options for the deployment

Deploy models with Azure Machine Learning

Deploy a Model as a Service

Create a Pipeline for Batch Inferencing

Publish a batch inferencing pipeline

Run batch predictions using Azure ML designer

Run a batch inferencing pipeline and obtain outputs

Build an Azure ML pipeline for batch scoring

Publish a Designer Pipeline as a Web Service

Create a target compute resource

Deploy models with Azure Machine Learning

Configure an Inference pipeline

Deploy a machine learning model with the designer

Consume a deployed endpoint

Deploy models with Azure Machine Learning

This brings us to the end of DP-100 Study Guide

What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you about how your preparation is going on!

In case you are looking for other Azure certification exams check out this page

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