Designing and Implementing a Data Science Solution on Azure (DP-100) Practice Exam
Designing and Implementing a Data Science Solution on Azure (DP-100) Practice Exam
Designing and Implementing a Data Science Solution on Azure (DP-100) Practice Exam
The Microsoft Azure Data Scientist Associate certification validates your expertise in designing and implementing machine learning solutions at cloud scale using Azure Machine Learning. Earning the DP-100 certification demonstrates your ability to manage the entire data science lifecycle within Azure, from data ingestion and preparation to model deployment and monitoring.
Who Should Take This Exam?
The DP-100 certification is ideal for data science professionals with experience in Python and machine learning fundamentals who want to leverage the power of Azure for their data science workflows. Target audiences include:
Data Scientists: Transitioning their existing data science skills to the Azure cloud platform.
Machine Learning Engineers: Deploying and managing machine learning models in production using Azure Machine Learning.
Data Analysts: Expanding their skillset to incorporate cloud-based data science techniques using Azure.
Anyone seeking to: Demonstrate their proficiency in designing and implementing end-to-end data science solutions on Microsoft Azure.
Are There Prerequisites?
While there are no formal prerequisites, Microsoft recommends that candidates possess:
Experience with Python programming and familiarity with machine learning libraries (NumPy, Pandas, Scikit-learn).
Understanding of core data science concepts (data wrangling, exploratory data analysis, machine learning algorithms).
Basic knowledge of cloud computing principles would be beneficial.
What Does a Microsoft Azure Data Scientist Associate Do?
With a DP-100 certification, you may qualify for roles such as:
Azure Data Scientist: Designing, developing, and deploying machine learning models on Microsoft Azure.
Cloud Data Scientist: Utilizing Azure Machine Learning for the entire data science lifecycle within the cloud.
Machine Learning Engineer (Azure Focus): Specializing in building, deploying, and managing machine learning pipelines in Azure.
Exam Details
Number of Questions:80
Length of Time: 120 Minutes
Registration Fee:$165.00
Passing score: 700 (on a scale of 1-1000)
Course Outline
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