Microsoft DP-100 Exam Topic (Design and Prepare a Machine Learning Solution)

1. Design and Prepare a Machine Learning Solution (DP-100) Exam

2. In the Microsoft DP-100 exam, Design and Prepare a Machine Learning Solution carries significant weight (20–25%). This topic evaluates a candidate’s ability to plan, build, and manage machine learning solutions using Azure Machine Learning (Azure ML). It focuses on dataset preparation, compute selection, development approaches, and effective resource management within an Azure ML workspace.

3. Designing a Machine Learning Solution • The first step in designing a machine learning solution is identifying the structure and format of datasets. Candidates must understand when to use structured data (such as CSV or Parquet files), semi-structured data (like JSON), or unstructured data (images, text, or audio). Proper data formatting ensures compatibility with Azure ML pipelines and training jobs. • Next, you must determine compute specifications based on workload requirements. Azure ML offers different compute options, including CPU-based compute for lightweight training and GPU-based compute for deep learning and large-scale models. Selecting the right compute helps optimize performance and cost.

4. Another critical decision is selecting the development approach. Azure ML supports: • Designer (no-code/low-code) for visual model building • Automated ML (AutoML) for rapid experimentation • SDK/CLI-based development for advanced customization and control • Choosing the appropriate approach depends on project complexity, team expertise, and deployment goals.

5. Creating and Managing Azure Machine Learning Resources • An Azure Machine Learning workspace is the central resource for managing experiments, models, and assets. Candidates should know how to create and manage workspaces using the Azure portal, ARM templates, or Azure CLI. • Within the workspace, datastores are used to securely connect to data sources such as Azure Blob Storage, Data Lake, or SQL databases. Datastores simplify data access without exposing credentials in code. • Compute targets are essential for training and inference. Azure ML allows the creation of compute instances for development and compute clusters for scalable training. Understanding autoscaling and cost management is vital for exam success. • To support collaboration and DevOps practices, candidates must know how to set up Git integration. Azure ML integrates with GitHub and Azure DevOps, enabling version control for notebooks, scripts, and pipelines.

6. Managing Assets in Azure Machine Learning • Azure ML enables structured asset management to promote reusability and consistency. • Data assets store references to datasets with versioning support. • Environments define dependencies such as Python packages and Docker images, ensuring consistent training and deployment. • Registries allow assets (models, environments, and components) to be shared across multiple workspaces, supporting enterprise-scale machine learning. • Mastering asset management helps maintain governance, reproducibility, and collaboration across teams.

7. Sample DP-100 Exam Questions • Q1. You are training a deep learning model that requires high parallel processing. Which compute option should you select in Azure Machine Learning? • A. Azure ML Compute Instance (CPU)B. Azure ML Compute Cluster with GPUC. Azure App ServiceD. Azure Functions • Correct Answer:B • Q2. What is the primary benefit of using Azure Machine Learning • datastores? • A. They automatically clean dataB. They store trained modelsC. They securely manage connections to data sourcesD. They replace Azure Storage accounts • Correct Answer: C

8. Q3. You want to reuse datasets and environments across multiple Azure ML workspaces. Which feature should you use? • A. Compute clustersB. PipelinesC. RegistriesD. Endpoints • Correct Answer: C

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