Designing and Implementing Data Science Solution on Azure (DP-100) 

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

The DP-100 Exam is for Azure Data Scientist who applies their knowledge of data science and machine learning for implementing and running machine learning workloads on Azure. Moreover, this exam DP-100 requires planning and developing a suitable working environment for data science workloads on Azure and running data experiments and training predictive models.

Who should take the Microsoft Exam (DP-100) Exam?

The DP-100 is best suitable for,

• The candidate who is able to define and set the development environment.
• Candidates who know how to apply scientific techniques to gain actionable visions and communicate results to stakeholders.
• Candidates must know how to prepare data for modelling as well as how to develop models.
• Candidates who are having a background in mathematics, statistics, and computer science.

Course Outline

The Microsoft Azure (DP-100) Exam covers the topics as per exam updates as of March 14, 2023 - 

Domain 1 - Understand to Design and prepare a machine learning solution (20–25%)

1.1 Design a machine learning solution

• Learn to Determine the appropriate compute specifications for a training workload
• Learn to Describe model deployment requirements
• Learn to Select which development approach to use to build or train a model

1.2 Manage an Azure Machine Learning workspace

• Learn to Create an Azure Machine Learning workspace
• Learn to Manage a workspace by using developer tools for workspace interaction
• Learn to Set up Git integration for source control

1.3 Manage data in an Azure Machine Learning workspace

• Learn to Select Azure Storage resources
• Learn to Register and maintain datastores
• Learn to Create and manage data assets

1.4 Manage compute for experiments in Azure Machine Learning

• Learn to Create compute targets for experiments and training
• Learn to Select an environment for a machine learning use case
• Learn to Configure attached compute resources, including Apache Spark pools
• Learn to Monitor compute utilization

Domain 2 - Understand to Explore data and train models (35–40%)

2.1 Explore data by using data assets and data stores

• Learn to Access and wrangle data during interactive development
• Learn to Wrangle interactive data with Apache Spark

2.2 Create models by using the Azure Machine Learning designer

• Learn to Create a training pipeline
• Learn to Consume data assets from the designer
• Learn to Use custom code components in designer
• Learn to Evaluate the model, including responsible AI guidelines

2.3 Use automated machine learning to explore optimal models

• Learn to Use automated machine learning for tabular data
• Learn to Use automated machine learning for computer vision
• Learn to Use automated machine learning for natural language processing (NLP)
• Learn to Select and understand training options, including preprocessing and algorithms
• Learn to Evaluate an automated machine learning run, including responsible AI guidelines

2.4 Use notebooks for custom model training

• Learn to Develop code by using a compute instance
• Learn to Track model training by using MLflow
• Learn to Evaluate a model
• Learn to Train a model by using Python SDKv2
• Learn to Use the terminal to configure a compute instance

2.5 Tune hyperparameters with Azure Machine Learning

• Learn to Select a sampling method
• Learn to Define the search space
• Learn to Define the primary metric
• Learn to Define early termination options

Domain 3 - Understand to Prepare a model for deployment (20–25%)

3.1 Run model training scripts

• Learn to Configure job run settings for a script
• Learn to Configure compute for a job run
• Learn to Consume data from a data asset in a job
• Learn to Run a script as a job by using Azure Machine Learning
• Learn to Use MLflow to log metrics from a job run
• Learn to Use logs to troubleshoot job run errors
• Learn to Configure an environment for a job run
• Learn to Define parameters for a job

3.2 Implement training pipelines

• Learn to Create a pipeline
• Learn to Pass data between steps in a pipeline
• Learn to Run and schedule a pipeline
• Learn to Monitor pipeline runs
• Learn to Create custom components
• Learn to Use component-based pipelines

3.3 Manage models in Azure Machine Learning

• Learn to Describe MLflow model output
• Learn to Identify an appropriate framework to package a model
• Learn to Assess a model by using responsible AI guidelines

Domain 4 - Understand to Deploy and retrain a model (10–15%)

4.1 Deploy a model

• Learn to Configure settings for online deployment
• Learn to Configure compute for a batch deployment
• Learn to Deploy a model to an online endpoint
• Learn to Deploy a model to a batch endpoint
• Learn to Test an online deployed service
• Learn to Invoke the batch endpoint to start a batch scoring job

4.2 Apply machine learning operations (MLOps) practices

• Learn to Trigger an Azure Machine Learning job, including from Azure DevOps or GitHub
• Learn to Automate model retraining based on new data additions or data changes
• Learn to Define event-based retraining triggers

Exam Pattern 

• Exam Name: Designing and Implementing aData Science Solution on Azure
• Exam Code: DP-100
• Number of Questions:80
• Length of Time:  120 Minutes
• Registration Fee:$165.00
• Passing score: 700 (on a scale of 1-1000)
• Exam Language English, Japanese, Chinese, Korean

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