Designing and Implementing a Data Science Solution on Azure (DP-100T01)
Training plan
Module 1: Explore and configure the Azure Machine Learning workspace
- Explore Azure Machine Learning workspace resources and assets
- Explore developer tools for workspace interaction
- Make data available in Azure Machine Learning
- Work with compute targets in Azure Machine Learning
- Work with environments in Azure Machine Learning
Module 2: Experiment with Azure Machine Learning
- Find the best classification model with Automated Machine Learning
- Track model training in Jupyter notebooks with MLflow
Module 3: Optimize model training with Azure Machine Learning
- Run a training script as a command job in Azure Machine Learning
- Track model training with MLflow in jobs
- Perform hyperparameter tuning with Azure Machine Learning
- Run pipelines in Azure Machine Learning
Module 4: Manage and review models in Azure Machine Learning
- Register an MLflow model in Azure Machine Learning
- Create and explore the Responsible AI dashboard for a model in Azure Machine Learning
Module 5: Deploy and consume models with Azure Machine Learning
- Deploy a model to a managed online endpoint
- Deploy a model to a batch endpoint
Module 6: Develop generative AI apps in Azure
- Plan and prepare to develop AI solutions on Azure
- Choose and deploy models from the model catalog in Azure AI Foundry portal
- Develop an AI app with the Azure AI Foundry SDK
- Get started with prompt flow to develop language model apps in the Azure AI Foundry
- Develop a RAG-based solution with your own data using Azure AI Foundry
- Fine-tune a language model with Azure AI Foundry
- Implement a responsible generative AI solution in Azure AI Foundry
- Evaluate generative AI performance in Azure AI Foundry portal
Recommended prerequisite knowledge
Prior to attending this training, students should have:
- earned the Microsoft Certified: Azure Data Fundamentals (DP900) certification or equivalent knowledge.
- an understanding of data science, including data preparation, model training, and evaluating competing models to choose the best one.
- an understanding of data science with Python; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.
Credentials and certification
Exam features
- Code: DP-100
- Title: Designing and Implementing a Data Science Solution on Azure
- Duration: 120 minutes
- Number of Questions: 40 to 60
- Question Format: Multiple choice, multiple response, scenario-based
- Passing Score: 700 out of 1000
- Cost: $0 (included in your training)
Exam topics
- Design and prepare a machine learning solution
- Explore data and train models
- Prepare a model for deployment
- Deploy and retrain a model
Eccentrix Corner Articles: Azure Data Scientist Associate DP-100 Resources
Explore our technical articles on Azure Data Scientist Associate DP-100 published on Eccentrix Corner. These resources dive deeper into key concepts, share best practices, and provide practical guides to maximize your learning and certification success. Our experts share real-world insights to help you master data science on Microsoft Azure.
Azure Data Scientist Training
The Azure Data Scientist training equips professionals with the skills needed to design and implement data science solutions using Microsoft Azure. This course focuses on building and deploying machine learning models, managing datasets, and using Azure Machine Learning to solve real-world problems.
Ideal for data scientists, developers, and IT professionals, this training prepares participants to pass the DP-100 certification exam and validate their expertise in creating impactful data science solutions.
Why Choose the Azure Data Scientist Associate Training?
Data science is at the forefront of innovation, driving decision-making and insights across industries. This training empowers participants to leverage Azure’s powerful tools to build, train, and deploy machine learning models. The Azure Data Scientist Associate certification demonstrates your ability to transform raw data into valuable insights using cutting-edge cloud technologies.
Through hands-on exercises and expert guidance, you will develop practical skills that can be directly applied to data science projects, making you an indispensable part of any data-driven organization.
Key Skills Developed in the Training
Prepare and manage datasets
Learn to clean, preprocess, and structure data for machine learning models using Azure tools.Train and evaluate machine learning models
Gain expertise in training models using Azure Machine Learning, hyperparameter tuning, and performance evaluation.Implement machine learning pipelines
Master the creation and optimization of pipelines to automate workflows and enhance scalability.Deploy machine learning models
Learn how to deploy trained models as web services, integrate them into applications, and monitor their performance.Optimize model performance
Use tools like Azure Machine Learning Designer to improve accuracy, efficiency, and scalability of your solutions.Secure and manage machine learning environments
Understand how to protect sensitive data, manage resources, and ensure compliance with Azure’s security best practices.
Interactive, Instructor-Led Training for Practical Experience
The Azure Data Scientist Associate training is delivered by Microsoft-certified instructors who provide expert guidance and real-world scenarios. Participants will engage in interactive exercises to develop and deploy machine learning models, ensuring they gain hands-on experience with Azure Machine Learning tools.
This course not only prepares you for the DP-100 certification exam but also equips you with practical skills to tackle real-world challenges in data science.
Who Should Attend?
This training is ideal for:
- Data scientists seeking to enhance their skills with Azure Machine Learning tools
- Developers interested in integrating machine learning models into applications
- IT professionals preparing for the Azure Data Scientist Associate (DP-100) certification
- Organizations looking to leverage advanced data science solutions to improve decision-making
Advance Your Career with Azure Data Science Expertise
The Microsoft Certified: Azure Data Scientist Associate (DP-100) training provides you with the skills to design and implement advanced data science solutions in Azure. Enroll today to earn a globally recognized certification and take your data science career to the next level.
DP-100 Exam Success Strategies
Mastering the DP-100 certification requires more than technical knowledge – strategic preparation, effective time management, and optimal mental performance are equally crucial for success.
DP-100 Exam Statistics & Success Rates
- Average Pass Rate: 65-70% on first attempt (Microsoft Associate level average)
- Most Common Score Range: 720-780 for passing candidates
- Average Study Time: 6-8 weeks for experienced data scientists
- Retake Rate: 25-30% of candidates require a second attempt
- Top Failure Areas: Azure Machine Learning pipelines (35%), model deployment and monitoring (31%), data preparation and feature engineering (27%)
Study Method Comparison
| Study Approach | Duration | Pass rate | Best For |
|---|---|---|---|
|
Hands-on Practice Only |
4-5 weeks |
45-55% |
Experienced data scientists |
|
Documentation + Practice |
6-7 weeks |
70-75% |
Methodical learners |
|
Training + Labs + Practice |
6-8 weeks |
85-90% |
Comprehensive preparation |
|
Practice Tests Only |
2-3 weeks |
35-45% |
Not recommended |
Strategic Study Approach
- Create a 6-8 week study timeline – Don’t cram for this associate-level certification
- Follow the 70-20-10 rule – 70% hands-on practice with Azure ML Studio and Python, 20% reading documentation, 10% practice tests
- Focus on scenario-based learning – DP-100 emphasizes real-world machine learning workflows over memorization
- Study in 90-minute focused blocks with 15-minute breaks to maximize retention
Common Exam Pitfalls to Avoid
- Don’t confuse Azure Machine Learning designer with notebooks – understand when to use visual ML designer vs. code-first approaches
- Model training vs. model deployment scenarios – know the difference between experimentation and production deployment workflows
- Data drift vs. model drift monitoring – distinguish between input data changes and model performance degradation
- Compute targets and their purposes – understand when to use compute instances, clusters, and inference clusters
- AutoML vs. custom model training – know when automated ML is appropriate vs. custom algorithm implementation
- Model interpretability and responsible AI – understand fairness, explainability, and bias detection requirements
Topic Weight Distribution
| Exam Domain | Weight | Focus Areas | Priority |
|---|---|---|---|
|
Design a Machine Learning Solution |
20-25% |
Workspace setup, compute resources, data requirements |
High |
|
Explore Data and Train Models |
35-40% |
Data preparation, feature engineering, model training |
Critical |
|
Prepare Data for Modeling |
15-20% |
Data ingestion, transformation, feature selection |
High |
|
Deploy and Retrain a Model |
20-25% |
Model deployment, monitoring, retraining pipelines |
Critical |
Exam Day Time Management
- Allocate 90 seconds per question on average – this gives buffer time for complex scenarios
- Read case studies completely first before attempting related questions
- Flag uncertain questions and return to them – don’t get stuck on difficult items
- Reserve 15 minutes at the end for reviewing flagged questions and checking answers
Managing Exam Stress & Performance
- Get 7-8 hours of quality sleep the night before – avoid last-minute cramming
- Arrive 30 minutes early to settle in and complete check-in procedures calmly
- Use deep breathing techniques if you feel overwhelmed during the exam
- Trust your preparation – your first instinct is usually correct on scenario questions
Technical Preparation Tips
- Practice with Azure ML Studio, Python SDK, and CLI – know multiple ways to create and manage ML workflows
- Master MLOps concepts and practices – understand CI/CD for machine learning and model versioning
- Understand data science workflows thoroughly – from data ingestion to model monitoring in production
- Review responsible AI principles – know how to implement fairness, explainability, and accountability
Final Week Preparation
- Take 2-3 practice exams to identify knowledge gaps and build confidence
- Review Microsoft’s official exam objectives one final time
- Avoid learning new concepts – focus on reinforcing what you already know
- Prepare your exam day logistics – route to test center, required identification, arrival time
Mental Preparation Strategies
- Visualize success scenarios – imagine yourself confidently answering questions
- Remind yourself of your hands-on experience – you’ve likely built many of these ML solutions before
- Stay positive during difficult questions – every candidate faces challenging scenarios
- Remember that 700/1000 passes – you don’t need perfection, just solid competency
How to Schedule Your DP-100 Exam
- Official Testing Provider: Pearson VUE is Microsoft’s authorized testing partner for DP-100
- Scheduling Process: Create a Pearson VUE account, search for “DP-100”, select your preferred test center and date
- Exam Cost: Included with your Eccentrix training – exam voucher provided for this associate-level certification
- Scheduling Timeline: Book at least 2-3 weeks in advance for better time slot availability
- Rescheduling Policy: Free rescheduling up to 24 hours before your exam appointment
- Required ID: Government-issued photo ID (passport, driver’s license) matching your registration name exactly
Success Mindset: Approach DP-100 as a validation of your existing Azure data science skills rather than a test of memorized facts. Your practical experience with machine learning workflows, model development, and MLOps is your greatest asset.
Frequently asked questions about the Microsoft Scientist Associate DP-100 training (FAQ)
What topics are covered in the DP-100 training?
The course includes dataset preparation, machine learning model training, pipeline implementation, deployment, and performance optimization.
Who is this course designed for?
This course is designed for data scientists, developers, and IT professionals working with machine learning and data science solutions.
What tools and services are used in this training?
Participants will use Azure Machine Learning, Azure Data Factory, and related services to manage and implement machine learning solutions.
What are the prerequisites for this course?
A basic understanding of programming (Python preferred), data analysis, and Azure fundamentals is recommended.
How does this training prepare for the DP-100 certification?
The course aligns with the exam objectives, combining theoretical knowledge and practical exercises to ensure success.
Does this training include hands-on projects?
Yes, participants will engage in hands-on projects to apply their skills to real-world scenarios.
Is the Azure Data Scientist Associate training available as a virtual class?
Yes, this certification training is offered as a live, interactive virtual class via Microsoft Teams, allowing participants to attend remotely while completing hands-on exercises with a certified instructor.
What Azure tools and services are used in the DP-100 training?
Participants use Azure Machine Learning, Azure Data Factory, and related services to design, train, deploy, and monitor machine learning models at cloud scale.
