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 Data Science Course (Online Training)
Pluralsight (Learning Path) Microsoft Azure Data Scientist Course [Free trial] LinkedIn Learning [Free Trial] Azure Machine Learning Development Udemy A-Z Machine Learning using Azure Machine Learning Coursera [Specialization] Data Science by John Hopkins University
DP-100 Azure Data Science Practice Test & Lab
Udemy Practice Test 70+ Practice Exam Questions Guided Projects on AML Learn by doing: Azure Machine Learning Studio Amazon e-book (pdf) Cloud Data Science with Azure Machine Learning
Other Data Science Learning Materials
Educative Data Science for Non-Programmers Treehouse Beginning Data Science Track
To view other Azure Certificate Study Guides, click here
Full Disclosure: Some of the links in this post are affiliate links. I receive a commission when you purchase through them.
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Set up an Azure Machine Learning workspace (30-35%)
Create an Azure Machine Learning workspace
Create an Azure Machine Learning workspace
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace
Configure workspace settings
https://docs.microsoft.com/en-us/cli/azure/security/workspace-setting?view=azure-cli-latest
Manage a workspace by using Azure Machine Learning Studio
https://docs.microsoft.com/en-us/azure/machine-learning/studio/manage-workspace
Manage data objects in an Azure Machine Learning workspace
Register and maintain data stores
Create and manage datasets
Manage experiment compute contexts
Create a compute instance
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-instance
Determine appropriate compute specifications for a training workload
Create compute targets for experiments and training
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets
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
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-retrain-designer
Ingest data in a designer pipeline
https://docs.microsoft.com/en-us/azure/machine-learning/concept-data-ingestion
Use designer modules to define a pipeline data flow
Use custom code modules in designer
https://docs.microsoft.com/en-us/azure/machine-learning/studio/custom-r-modules
Run training scripts in an Azure Machine Learning workspace
Create and run an experiment by using the Azure Machine Learning SDK
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-1st-experiment-sdk-setup
Consume data from a data store in an experiment by using the Azure Machine Learning SDK
Consume data from a dataset in an experiment by using the Azure Machine Learning SDK
Choose an estimator for a training experiment
Generate metrics from an experiment run
Log metrics from an experiment run
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments
Retrieve and view experiment outputs
Use logs to troubleshoot experiment run errors
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines-application-insights
Amazon link (affiliate)
Automate the model training process
Create a pipeline by using the SDK
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-auto-train-models
Pass data between steps in a pipeline
https://docs.microsoft.com/en-us/learn/modules/create-pipelines-in-aml/3-pipeline-data
Run a pipeline
Monitor pipeline runs
https://docs.microsoft.com/en-us/azure/machine-learning/monitor-azure-machine-learning
Optimize and manage models (20-25%)
Use Automated ML to create optimal models
Use the Automated ML interface in Azure Machine Learning studio
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-automated-ml-forecast
Use Automated ML from the Azure Machine Learning SDK
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-train
Select scaling functions and pre-processing options
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml#preprocessing
Determine algorithms to be searched
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-define-task-type
Define a primary metric
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-train#primary-metric
Get data for an Automated ML run
Retrieve the best model
Use Hyperdrive to tune hyperparameters
Select a sampling method
Define the search space
Define the primary metric
Define early termination options
Find the model that has optimal hyperparameter values
Use model explainers to interpret models
Select a model interpreter
Generate feature importance data
Manage models
Register a trained model
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where#registermodel
Monitor model history
Monitor data drift
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-datasets
Amazon link (affiliate)
Deploy and consume models (20-25%)
Create production compute targets
Consider security for deployed services
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-secure-web-service
Evaluate compute options for deployment
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where#deploy-to-target
Deploy a model as a service
Configure deployment settings
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where
Consume a deployed service
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-consume-web-service
Troubleshoot deployment container issues
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment
Create a pipeline for batch Inferencing
Publish a batch inferencing pipeline
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-run-batch-predictions-designer
Run a batch inferencing pipeline and obtain outputs
Publish a designer pipeline as a web service
Create a target compute resource
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where#deploy-to-target
Configure an Inference pipeline
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy
Consume a deployed endpoint
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where#consume-web-services
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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