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.
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DP-100 Azure Data Science Online Course
| Pluralsight (Learning Path) | Microsoft Azure Data Scientist Course |
| LinkedIn Learning [Free Trial] | Azure Machine Learning Development |
| Udemy | A-Z Machine Learning with Azure ML |
DP-100 Azure Data Science Practice Test & Lab
| Whizlabs Exam Questions | Microsoft Azure Exam DP-100 Certification |
| Udemy Practice Test | 170+ Azure Data Practice Exam Questions |
| Amazon e-book (pdf) | Data Science with Azure Machine Learning |
Other Data Science Learning Programs
| Coursera [Specialization] | Data Science by John Hopkins University |
| Udacity | Become an Azure Machine Learning Engineer |
| Datacamp | Data Science for all [Interactive course] |
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.
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DP-100 Sample Practice Exam Questions
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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
Create & manage Azure Machine Learning workspaces
Configure workspace settings
Azure security workspace setting
Manage a workspace by using Azure Machine Learning Studio
Manage Data Objects in an Azure Machine Learning Workspace
Register and maintain datastores
Create and manage 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
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
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
Generate Metrics from an Experiment Run
Log metrics from an experiment run
Enable logging in Azure ML training runs
Retrieve and view experiment outputs
Evaluate machine learning experiment results
Set input & output directories
Use logs to troubleshoot experiment run errors
Collect ML pipeline log files in Application Insights for alerts & debugging
Automate the Model Training Process
Create a pipeline by using the SDK
Use automated machine learning to predict taxi fares
Pass data between steps in a pipeline
Pass data between pipeline steps
Run a pipeline
Running automated machine learning experiments
Monitor pipeline runs
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
Get data for an Automated ML run
Retrieve the best model
Use Hyperdrive to Tune Hyperparameters
Select a sampling method
Sampling the hyperparameter space
Define the search space
Tune hyperparameters for your model with Azure ML
Define the primary metric
Primary metric in Azure Machine Learning
Choosing a primary metric to handle imbalanced data
Define early termination options
Specify early termination policy
Find the model that has optimal hyperparameter values
Use Model Explainers to Interpret Models
Select a model interpreter
Interpretability with Azure Machine Learning
Generate feature importance data
Manage Models
Register a trained model
Register your model (optional)
Monitor model usage
Monitor Azure Machine Learning
Monitoring data from Azure Machine Learning
Monitor data drift
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 a Model as a Service
Configure deployment settings
Deploy models with Azure Machine Learning
Consume a deployed service
Consume an Azure ML model deployed as a web service
Troubleshoot deployment container issues
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
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
This brings us to the end of the DP-100 Designing And Implementing A Data Science Solution On Azure 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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