Skills at a glance
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Implement and manage an analytics solution (30–35%)
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Ingest and transform data (30–35%)
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Monitor and optimize an analytics solution (30–35%)
Implement and manage an analytics solution (30–35%)
Configure Microsoft Fabric workspace settings
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Configure Spark workspace settings
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Configure domain workspace settings
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Configure OneLake workspace settings
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Configure data workflow workspace settings
Implement lifecycle management in Fabric
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Configure version control
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Implement database projects
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Create and configure deployment pipelines
Configure security and governance
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Implement workspace-level access controls
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Implement item-level access controls
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Implement row-level, column-level, object-level, and folder/file-level access controls
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Implement dynamic data masking
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Apply sensitivity labels to items
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Endorse items
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Implement and use workspace logging
Orchestrate processes
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Choose between a pipeline and a notebook
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Design and implement schedules and event-based triggers
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Implement orchestration patterns with notebooks and pipelines, including parameters and dynamic expressions
Ingest and transform data (30–35%)
Design and implement loading patterns
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Design and implement full and incremental data loads
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Prepare data for loading into a dimensional model
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Design and implement a loading pattern for streaming data
Ingest and transform batch data
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Choose an appropriate data store
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Choose between dataflows, notebooks, KQL, and T-SQL for data transformation
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Create and manage shortcuts to data
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Implement mirroring
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Ingest data by using pipelines
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Transform data by using PySpark, SQL, and KQL
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Denormalize data
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Group and aggregate data
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Handle duplicate, missing, and late-arriving data
Ingest and transform streaming data
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Choose an appropriate streaming engine
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Choose between native storage, followed storage, or shortcuts in Real-Time Intelligence
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Process data by using eventstreams
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Process data by using Spark structured streaming
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Process data by using KQL
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Create windowing functions
Monitor and optimize an analytics solution (30–35%)
Monitor Fabric items
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Monitor data ingestion
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Monitor data transformation
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Monitor semantic model refresh
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Configure alerts
Identify and resolve errors
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Identify and resolve pipeline errors
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Identify and resolve dataflow errors
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Identify and resolve notebook errors
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Identify and resolve eventhouse errors
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Identify and resolve eventstream errors
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Identify and resolve T-SQL errors
Optimize performance
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Optimize a lakehouse table
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Optimize a pipeline
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Optimize a data warehouse
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Optimize eventstreams and eventhouses
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Optimize Spark performance
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Optimize query performance
