Designing and Implementing a Data Science Solution on Azure (DP-100) Exam
Designing and Implementing a Data Science Solution on Azure (DP-100) Exam
The Microsoft Azure Data Scientist Associate certification validates your expertise in designing and implementing machine learning solutions at cloud scale using Azure Machine Learning. Earning the DP-100 certification demonstrates your ability to manage the entire data science lifecycle within Azure, from data ingestion and preparation to model deployment and monitoring.
Who Should Take This Exam?
The DP-100 certification is ideal for data science professionals with experience in Python and machine learning fundamentals who want to leverage the power of Azure for their data science workflows. Target audiences include:
- Data Scientists: Transitioning their existing data science skills to the Azure cloud platform.
- Machine Learning Engineers: Deploying and managing machine learning models in production using Azure Machine Learning.
- Data Analysts: Expanding their skillset to incorporate cloud-based data science techniques using Azure.
- Anyone seeking to: Demonstrate their proficiency in designing and implementing end-to-end data science solutions on Microsoft Azure
Course Outline
Domain 1 - Understand to design and prepare a machine learning solution (20–25%)
Domain 2 - Understand to explore data and train models (35–40%)
Domain 3 - Understand to prepare a model for deployment (20–25%)
Domain 4 - Understand to deploy and retrain a model (10–15%)
Designing and Implementing a Data Science Solution on Azure (DP-100) Exam FAQs
Who should consider taking the DP-100 exam?
This exam is ideal for data scientists, data engineers, and analysts who want to validate their skills in designing, developing, and deploying data science solutions using Azure cloud services.
What core areas of Azure are covered in the DP-100 exam?
The exam focuses on core Azure services for data science, including Azure Machine Learning, Azure Databricks, Azure Synapse Analytics, data storage options (Blob Storage, Data Lake Storage), and compute resources (virtual machines, Azure Batch).
Does DP-100 require prior machine learning knowledge?
A basic understanding of machine learning concepts like algorithms, data preprocessing, and model evaluation is recommended for success in the exam.
How is model training and deployment addressed in the DP-100 exam?
The exam assesses your ability to configure and run machine learning experiments in Azure, including data preparation, model training, hyperparameter tuning, model deployment options (web services, batch scoring), and model monitoring.
Does the exam delve into specific Azure Machine Learning functionalities (e.g., pipelines)?
The exam emphasizes core functionalities like running scripts, managing compute resources, and utilizing modules within Azure Machine Learning, but might not cover advanced features in detail.
What is the role of data storage in the DP-100 exam?
Understanding how to store and access data using Azure data storage services like Azure Blob Storage and Azure Data Lake Storage for data science workloads is crucial for the exam.
Are there any hands-on labs or practical exercises involved in the DP-100 exam?
No, the exam format typically consists of multiple-choice and scenario-based questions where you'll apply your knowledge of Azure services to design and troubleshoot data science solutions.
What resources can help me prepare for the DP-100 exam?
Microsoft offers official learning paths, study guides, practice exams, and online training modules. Additionally, there might be third-party prep materials and practice exams available online.