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

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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