Implementing Data Engineering Solutions Using Microsoft Fabric
DP-700
Money Back Guarantee
98% Success Rate
Real Questions
1
Implement and manage an analytics solution 32.5%
1.1
Configure Microsoft Fabric workspace settings
4 subtopics1.1.1Configure Spark workspace settings
1.1.2Configure domain workspace settings
1.1.3Configure OneLake workspace settings
1.1.4Configure data workflow workspace settings
Learning Objectives
- Configure and optimize Spark workspace settings for different workloads
- Implement domain-based workspace organization
- Configure OneLake for optimal performance and cost
- Design effective data workflow configurations
1.2
Implement lifecycle management in Fabric
3 subtopics1.2.1Configure version control
1.2.2Implement database projects
1.2.3Create and configure deployment pipelines
Learning Objectives
- Implement version control for Fabric items
- Manage database projects and schema evolution
- Deploy items across workspaces using deployment pipelines
1.3
Configure security and governance
6 subtopics1.3.1Implement workspace-level access controls
1.3.2Implement item-level access controls
1.3.3Implement row-level, column-level, object-level, and folder/file-level access controls
1.3.4Implement dynamic data masking
1.3.5Apply sensitivity labels and endorse items
1.3.6Implement and use workspace logging
Learning Objectives
- Implement comprehensive security at multiple levels
- Configure row-level and column-level security
- Apply data governance and compliance controls
- Monitor and audit workspace activities
1.4
Orchestrate processes
3 subtopics1.4.1Choose between a pipeline and a notebook
1.4.2Design and implement schedules and event-based triggers
1.4.3Implement orchestration patterns with notebooks and pipelines
Learning Objectives
- Design appropriate orchestration solutions
- Implement complex scheduling and triggering patterns
- Use parameters and dynamic expressions effectively
Domain Hands-on Skills
Configure Spark pools and compute resourcesImplement Git integration and version controlSet up deployment pipelines across environmentsConfigure comprehensive security controlsDesign complex orchestration workflows
Common Mistakes to Avoid
- Oversizing Spark pools leading to unnecessary costs
- Incorrect RLS implementation causing data exposure
- Poor Git branching strategies causing merge conflicts
- Inefficient pipeline design with unnecessary activities
- Missing error handling in orchestration patterns
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Ingest and transform data 32.5%
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