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Back to DP-100

Azure Data Scientist Associate Exam Guide

A comprehensive 6-week study plan to help you prepare for the Microsoft Azure Data Scientist Associate certification exam.

Exam Information

100 minutes

Duration

40-60 questions

Questions

700/1000

Passing Score

$165 USD

Exam Cost

Recommended Study Plan

Week 1

Design and Prepare ML Solutions

10-12 hours

Azure Machine Learning workspace setup
Data assets and datastores
Compute targets and environments
Azure ML SDK and CLI configuration

Recommended Resources:

Microsoft Learn

Azure ML Documentation

Week 2

Explore Data and Train Models

12-14 hours

Data exploration with Azure ML notebooks
Feature engineering techniques
Model training with scikit-learn and PyTorch
Automated ML (AutoML) capabilities

Recommended Resources:

Hands-on Labs

Azure ML Studio

Week 3

Prepare Data for Modeling

10-12 hours

Data preprocessing and transformation
Handle missing values and outliers
Feature selection and dimensionality reduction
Data pipelines in Azure ML

Recommended Resources:

Azure ML Designer

Documentation

Week 4

Train and Evaluate Models

12-14 hours

Training runs and experiments
Hyperparameter tuning
Model evaluation metrics
MLflow integration

Recommended Resources:

Azure ML Studio

MLflow

Week 5

Deploy and Consume Models

10-12 hours

Model deployment options
Real-time and batch inference
Managed endpoints
Model monitoring and logging

Recommended Resources:

Azure Portal

Azure ML Endpoints

Week 6

Manage and Optimize Models

8-10 hours

Model versioning and registry
Responsible AI practices
Cost optimization
Review and practice exams

Recommended Resources:

Practice Tests

Review Materials

Exam Tips & Strategies

Hands-On Practice

DP-100 is highly practical. Spend significant time working in Azure ML Studio, training models, and deploying endpoints.

Time Management

You have 100 minutes for 40-60 questions including case studies. Allocate extra time for scenario-based questions.

Know the SDK

Be familiar with the Azure ML Python SDK v2 syntax for creating compute, datasets, and running experiments.

Responsible AI

Understand fairness, interpretability, and responsible AI principles - these are increasingly important exam topics.

Key Topics to Master

Azure Machine Learning workspace

Compute instances and clusters

Datastores and datasets

AutoML capabilities

Azure ML Designer

Model training and experiments

Hyperparameter tuning

Model evaluation metrics

Real-time and batch endpoints

MLflow tracking and registry

Responsible AI dashboard

Managed online endpoints

Pipeline components

Feature stores

Model monitoring

For the most up-to-date exam information, visit the official exam guide.

View Official Exam Guide

Back to DP-100

Azure Data Scientist Associate Exam Guide

A comprehensive 6-week study plan to help you prepare for the Microsoft Azure Data Scientist Associate certification exam.

Exam Information

100 minutes

Duration

40-60 questions

Questions

700/1000

Passing Score

$165 USD

Exam Cost

Recommended Study Plan

Week 1

Design and Prepare ML Solutions

10-12 hours

Azure Machine Learning workspace setup
Data assets and datastores
Compute targets and environments
Azure ML SDK and CLI configuration

Recommended Resources:

Microsoft Learn

Azure ML Documentation

Week 2

Explore Data and Train Models

12-14 hours

Data exploration with Azure ML notebooks
Feature engineering techniques
Model training with scikit-learn and PyTorch
Automated ML (AutoML) capabilities

Recommended Resources:

Hands-on Labs

Azure ML Studio

Week 3

Prepare Data for Modeling

10-12 hours

Data preprocessing and transformation
Handle missing values and outliers
Feature selection and dimensionality reduction
Data pipelines in Azure ML

Recommended Resources:

Azure ML Designer

Documentation

Week 4

Train and Evaluate Models

12-14 hours

Training runs and experiments
Hyperparameter tuning
Model evaluation metrics
MLflow integration

Recommended Resources:

Azure ML Studio

MLflow

Week 5

Deploy and Consume Models

10-12 hours

Model deployment options
Real-time and batch inference
Managed endpoints
Model monitoring and logging

Recommended Resources:

Azure Portal

Azure ML Endpoints

Week 6

Manage and Optimize Models

8-10 hours

Model versioning and registry
Responsible AI practices
Cost optimization
Review and practice exams

Recommended Resources:

Practice Tests

Review Materials

Exam Tips & Strategies

Hands-On Practice

DP-100 is highly practical. Spend significant time working in Azure ML Studio, training models, and deploying endpoints.

Time Management

You have 100 minutes for 40-60 questions including case studies. Allocate extra time for scenario-based questions.

Know the SDK

Be familiar with the Azure ML Python SDK v2 syntax for creating compute, datasets, and running experiments.

Responsible AI

Understand fairness, interpretability, and responsible AI principles - these are increasingly important exam topics.

Key Topics to Master

Azure Machine Learning workspace

Compute instances and clusters

Datastores and datasets

AutoML capabilities

Azure ML Designer

Model training and experiments

Hyperparameter tuning

Model evaluation metrics

Real-time and batch endpoints

MLflow tracking and registry

Responsible AI dashboard

Managed online endpoints

Pipeline components

Feature stores

Model monitoring

For the most up-to-date exam information, visit the official exam guide.

View Official Exam Guide