What You'll Learn

  • Designing and creating a suitable working environment for data science workloads.
  • Running jobs to prepare for production.
  • Managing
  • deploying
  • and monitoring scalable machine learning solutions.
  • Training machine learning models.

Requirements

  • As a candidate for this exam
  • you should have knowledge and experience in data science by using: Azure Machine
  • Learning MLflow
  • Azure AI services
  • including Azure AI Search
  • Azure AI Foundry

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

Who this course is for:

  • As a candidate for this exam
  • you should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure
DP-100: Designing and Implementing a Data Science - May 2025

Course Includes:

  • Price: FREE
  • Enrolled: 1553 students
  • Language: English
  • Certificate: Yes
  • Difficulty: Beginner
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