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.
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 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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