DP-100: Designing and Implementing a Data Science Solution

Skills at a glance

• Design and prepare a machine learning solution (20–25%)
• Explore data, and run experiments (20–25%)
• Train and deploy models (25–30%)
• Optimize language models for AI applications (25–30%)

Design and prepare a machine learning solution (20–25%)

Design a machine learning solution

• Identify the structure and format for datasets
• Determine the compute specifications for machine learning workload
• Select the development approach to train a model

Create and manage resources in an Azure Machine Learning workspace

• Create and manage a workspace
• Create and manage datastores
• Create and manage compute targets
• Set up Git integration for source control

Create and manage assets in an Azure Machine Learning workspace

• Create and manage data assets
• Create and manage environments
• Share assets across workspaces by using registries

Explore data, and run experiments (20–25%)

Use automated machine learning to explore optimal models

• Use automated machine learning for tabular data
• Use automated machine learning for computer vision
• Use automated machine learning for natural language processing
• Select and understand training options, including preprocessing and algorithms
• Evaluate an automated machine learning run, including responsible AI guidelines

Use notebooks for custom model training

• Use the terminal to configure a compute instance
• Access and wrangle data in notebooks
• Wrangle data interactively with attached Synapse Spark pools and serverless Spark compute
• Retrieve features from a feature store to train a model
• Track model training by using MLflow
• Evaluate a model, including responsible AI guidelines

Automate hyperparameter tuning

• Select a sampling method
• Define the search space
• Define the primary metric
• Define early termination options

Train and deploy models (25–30%)

Run model training scripts

• Consume data in a job
• Configure compute for a job run
• Configure an environment for a job run
• Track model training with MLflow in a job run
• Define parameters for a job
• Run a script as a job
• Use logs to troubleshoot job run errors

Implement training pipelines

• Create custom components
• Create a pipeline
• Pass data between steps in a pipeline
• Run and schedule a pipeline
• Monitor and troubleshoot pipeline runs

Manage models

• Define the signature in the MLmodel file
• Package a feature retrieval specification with the model artifact
• Register an MLflow model
• Assess a model by using responsible AI principles

Deploy a model

• Configure settings for online deployment
• Deploy a model to an online endpoint
• Test an online deployed service
• Configure compute for a batch deployment
• Deploy a model to a batch endpoint
• Invoke the batch endpoint to start a batch scoring job

Optimize language models for AI applications (25–30%)

Prepare for model optimization

• Select and deploy a language model from the model catalog
• Compare language models using benchmarks
• Test a deployed language model in the playground
• Select an optimization approach

Optimize through prompt engineering and prompt flow

• Test prompts with manual evaluation
• Define and track prompt variants
• Create prompt templates
• Define chaining logic with the prompt flow SDK
• Use tracing to evaluate your flow

Optimize through Retrieval Augmented Generation (RAG)

• Prepare data for RAG, including cleaning, chunking, and embedding
• Configure a vector store
• Configure an Azure AI Search-based index store
• Evaluate your RAG solution

Optimize through fine-tuning

• Prepare data for fine-tuning
• Select an appropriate base model
• Run a fine-tuning job
• Evaluate your fine-tuned model