Azure
DP-100: Azure Data Scientist Associate
The intermediate-level certification DP-100: Designing and Implementing a Data Science Solution on Azure attests to your proficiency in developing, implementing, and overseeing complete machine learning (ML) solutions on Microsoft Azure. In order to address real-world data science workflows, from data ingestion to deployment and monitoring, you will work with tools such as Azure Machine Learning, MLflow, and Azure AI Services.
5/5(4,890 Reviews)
Level
Intermediate
Duration
8 Weeks
What is DP-100: Azure Data Scientist Associate?
The intermediate certification DP-100: Azure Data Scientist Associate attests to your proficiency in developing, constructing, and implementing machine learning solutions on Microsoft Azure.
Data preparation, experimentation, model training, hyperparameter tuning, deployment, and monitoring are all covered, as is the entire machine learning lifecycle.
The Python SDK v2, MLflow, Azure Machine Learning services, and tools like AutoML, HyperDrive, and Responsible AI features will all be heavily utilized by you.
Training Plan
About trainer
Working professional who is carrying more then 10 years of industry experience.
Decks
The candidate will get access to the presentation taken-up during the session.
Notes
Consolidated notes in word document.
Assignments
Assignments for every module will be covered.
MCQs
MCQs for every module covered in the session would be provided.
Video Recording
Daily Session would be recorded and shared to the candidate.
Dumps for Certification
We will provide dumps for the certification exam, which will help you to prepare for the exam.
Updated Content
We provide Generative AI Driven content By experts.
Projects
3 Live Projects will be provided for practice.
DP-100: Azure Data Scientist Associate Course Curriculum
.
Introduction to Data Science and Azure
Section 1
Learn core data science concepts, lifecycle, and Azure tools for ML solutions.
Data science lifecycle
Azure Machine Learning
Cloud-based ML
Experimentation
.
Data Preparation
Section 2
Clean, transform, and prepare data for machine learning tasks.
Data wrangling
Feature engineering
Data cleansing
Azure Data Factory
.
Exploratory Data Analysis (EDA)
Section 3
Analyze datasets to discover patterns and relationships.
Data visualization
Descriptive statistics
Correlation analysis
Outlier detection
.
Model Training and Evaluation
Section 4
Train ML models and assess their performance using metrics.
Regression & classification
Model validation
Performance metrics
Cross-validation
.
Hyperparameter Tuning and Optimization
Section 5
Optimize model performance using tuning techniques and AutoML.
Hyperparameter tuning
Grid search / Random search
AutoML pipelines
Model selection
.
Deploying Machine Learning Models
Section 6
Deploy models as scalable web services for production use.
Azure ML endpoints
Model serialization
Containerized deployment
REST API integration
.
Monitoring and Retraining Models
Section 7
Track model performance and retrain to address drift or degradation.
Model drift detection
Logging & telemetry
Scheduled retraining
Performance tracking
.
Responsible AI and Ethics
Section 8
Implement ethical and compliant AI solutions in Azure.
Bias detection
Fairness metrics
Transparency
Compliance
.
Automated Machine Learning (AutoML)
Section 9
Automate model selection, training, and evaluation for faster deployment.
AutoML pipelines
Feature selection
Model ranking
Experiment tracking
.
Exam Preparation & Case Studies
Section 10
Review concepts with practical exercises, case studies, and practice tests.
Hands-on labs
Mock exams
Scenario analysis
Best practices
Key Projects
DP-100: Azure Data Scientist Associate
Customer Churn Prediction
Using Azure Machine Learning, create a machine learning model to identify which clients are most likely to leave.
Gather and prepare client data from SQL databases or Azure Blob Storage.
Conduct exploratory data analysis and feature engineering.
Use custom ML pipelines or AutoML to train classification models.
Set up the model as a real-time endpoint and keep an eye out for drift in its performance.
Sales Forecasting with Time Series
Create a time series forecasting program to estimate product sales.
Utilize past sales information that is kept in Azure Data Lake.
Use Python or Azure ML Designer to clean and transform data for training.
Use Azure AutoML to create custom regression models or forecasts.
For continuous predictions, implement a batch inference pipeline and connect it to Power BI for visualization.
Sentiment Analysis on Customer Reviews
Use Azure Machine Learning and Azure Cognitive Services to develop an NLP solution for analyzing customer sentiment from reviews.
Take in text data from Cosmos DB or Azure Blob Storage.
Tokenization, stop-word removal, and embeddings are examples of preprocessing text.
To classify reviews as neutral, negative, or positive, train and assess classification models.
Install as a real-time API to connect to a dashboard or web application.
DP-100: Azure Data Scientist Associate – Associate Training Program
Category
Associate
Exam Name:
DP-100: Azure Data Scientist Associate – Associate
Exam Code:
N.A.
Exam Duration:
N.A.
Exam Format:
N.A.
Passing Score:
N.A.
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Frequently Asked Questions (FAQs)
What is the DP-100: Azure Data Scientist Associate certification?
DP-100 validates skills in designing and implementing machine learning solutions on Azure. It focuses on using Azure Machine Learning for real-world projects. Techpratham explains that this certification is ideal for aspiring data scientists.
Who should take the DP-100 exam?
The exam is designed for data scientists, ML engineers, and AI professionals. It suits candidates who want to build, train, and deploy machine learning models. Techpratham highlights that Python and ML knowledge is very useful before attempting.
What skills are measured in DP-100?
The exam tests data preparation, model training, deployment, and monitoring ML solutions. It also covers responsible AI, pipelines, and optimization. Techpratham notes that practical knowledge of Azure Machine Learning Studio is essential.
What are the prerequisites for DP-100?
There are no official prerequisites, but experience with Python, data science workflows, and ML libraries helps. Knowledge of Azure services also adds value. Techpratham suggests starting with AI-900 for beginners before moving to DP-100.
How long is the DP-100 certification valid?
The DP-100 certification is valid for one year and can be renewed online. Renewal requires passing a short free Microsoft assessment. Techpratham emphasizes the importance of staying current with ML trends and Azure updates.
How much does the DP-100 exam cost?
The cost is usually $165 USD, though it varies by country and region. Microsoft sometimes offers student or organizational discounts. Techpratham advises checking the official Microsoft certification page for accurate pricing.
What is the format of the DP-100 exam?
The exam includes multiple-choice, case studies, drag-and-drop, and performance-based labs. It evaluates both theoretical ML concepts and hands-on Azure ML tasks. Techpratham explains that working on real ML projects gives a strong advantage.
How should I prepare for the DP-100 exam?
Preparation should include Microsoft Learn resources, practice labs, and ML projects. Candidates should practice building models with Azure ML Designer and Python SDK. Techpratham recommends balancing theory with end-to-end ML pipelines for confidence.
What job roles can I get after DP-100 certification?
This certification prepares you for roles like Data Scientist, Machine Learning Engineer, and AI Specialist. It also supports career growth in analytics and research fields. Techpratham points out that certified professionals are in high demand globally.
Is DP-100 considered difficult?
The exam is moderately difficult since it blends ML theory with Azure tools. With proper practice in ML pipelines and real project work, it becomes manageable. Techpratham stresses that consistent hands-on labs are the key to success.
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