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

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