DP-100 Practice Test Overview: A Human-Friendly Guide
This guide provides a realistic overview of what to expect in your DP-100 (Designing and Implementing a Data Science Solution on Azure) exam preparation. Forget the robotic, overly technical descriptions; let's make this real.
Total Questions: Around 40-60 (The exact number varies slightly per exam). Think of it like a marathon, not a sprint!
Target Job Roles: This exam is perfect if you're aiming for roles like:
- Data Scientist: You'll use Azure to build and deploy your models.
- Data Engineer: You need to know how to manage and process data within Azure's ecosystem.
- Machine Learning Engineer: This exam will boost your skills in building, deploying, and managing machine learning solutions on Azure.
Language: English (obviously!)
Key Coverage Areas: Think of this as a roadmap to success.
I. Core Azure Services for Data Science: This section is huge. It's like the foundation of your house – you can't build without it.
- Azure Machine Learning: This is your primary workspace. Imagine it as your central command center for all your data science projects. We’ll cover creating and managing machine learning workloads, training models, and deploying them.
- Model Training and Deployment: This isn't just theoretical; we'll cover real-world scenarios and practical examples.
- Experiment Tracking and Management: Think of this as keeping a detailed logbook of your experiments – essential for reproducibility.
- Model Management and Monitoring: Once you've built a great model, you need to keep an eye on it! This section covers monitoring for drift and ensuring your model remains accurate.
- Azure Synapse Analytics: This is your powerful data warehouse. I think of it like a massive, highly organized library for all your data.
- Data Integration and Transformation: Getting your data ready is key – this section covers how to clean and prepare your data for analysis.
- Data Warehousing and Analytics: Unlocking the insights within your data – we'll cover querying, analyzing, and visualizing your data.
- Azure Data Lake Storage: Think of this as your incredibly spacious, secure storage for all your raw data. It's like the raw materials before you build your house.
- Data Ingestion and Management: We will cover methods for efficiently moving large data sets into your storage.
- Data Governance and Security: Protecting your precious data is paramount. This section emphasizes security best practices.
II. Data Preparation and Exploration: Before building any models, you need clean, well-understood data. This is like choosing the right ingredients for a delicious cake.
- Data Cleaning and Preprocessing: Handling missing values, outliers, and inconsistencies. Think of this as prepping vegetables before cooking.
- Exploratory Data Analysis (EDA): Understanding patterns and trends in your data through visualizations. Imagine tasting your ingredients and adjusting the recipe.
III. Machine Learning Model Development and Deployment: This is where the magic happens. It's constructing the house itself!
- Model Selection and Training: Choosing the right algorithm and tuning its parameters.
- Model Evaluation and Optimization: Measuring performance and improving accuracy.
- Deployment and Monitoring: Getting your model up and running and keeping an eye on its performance.
IV. Azure DevOps for Data Science: This is about collaboration and version control – crucial for working on larger projects.
This is not an exhaustive list, but it gives you a solid foundation. Remember – consistent practice is key! Good luck!
