Designing and Implementing a Data Science Solution on Azure
DP-100
Money Back Guarantee
98% Success Rate
Real Questions
1
Design and prepare a machine learning solution 22.5%
1.1
Design a machine learning solution
3 subtopics1.1.1Identify the structure and format for datasets
1.1.2Determine the compute specifications for machine learning workload
1.1.3Select the development approach to train a model
Learning Objectives
- Design end-to-end machine learning solutions on Azure
- Select appropriate compute and storage resources
- Choose the right development approach for the scenario
1.2
Create and manage resources in an Azure Machine Learning workspace
4 subtopics1.2.1Create and manage a workspace
1.2.2Create and manage datastores
1.2.3Create and manage compute targets
1.2.4Set up Git integration for source control
Learning Objectives
- Create and configure Azure ML workspaces
- Manage compute and storage resources
- Implement source control for ML projects
1.3
Create and manage assets in an Azure Machine Learning workspace
3 subtopics1.3.1Create and manage data assets
1.3.2Create and manage environments
1.3.3Share assets across workspaces by using registries
Learning Objectives
- Manage ML assets lifecycle
- Configure environments for reproducibility
- Implement asset sharing strategies
Domain Hands-on Skills
Create Azure ML workspace using Portal, CLI, or SDKConfigure compute clusters with auto-scalingSet up datastores with secure authenticationImplement Git integration for notebooks
Common Mistakes to Avoid
- Not configuring network security for workspace
- Using wrong compute type for workload
- Not versioning datasets and models
- Hardcoding credentials in code
2
Explore data, and run experiments 22.5%
3
Train and deploy models 27.5%
4