Microsoft Data Science (DP-100) Exam Questions Apr - 2025
Description:
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