Skills at a glance

• Implement and manage an analytics solution (30–35%)

• Ingest and transform data (30–35%)

• Monitor and optimize an analytics solution (30–35%)

Implement and manage an analytics solution (30–35%)

Ingest and transform data (30–35%)

Monitor and optimize an analytics solution (30–35%)

Implement and manage an analytics solution (30–35%)

Configure Microsoft Fabric workspace settings

• Configure Spark workspace settings

• Configure domain workspace settings

• Configure OneLake workspace settings

• Configure data workflow workspace settings

Configure Spark workspace settings

Configure domain workspace settings

Configure OneLake workspace settings

Configure data workflow workspace settings

Implement lifecycle management in Fabric

• Configure version control

• Implement database projects

• Create and configure deployment pipelines

Configure version control

Implement database projects

Create and configure deployment pipelines

Configure security and governance

• Implement workspace-level access controls

• Implement item-level access controls

• Implement row-level, column-level, object-level, and folder/file-level access controls

• Implement dynamic data masking

• Apply sensitivity labels to items

• Endorse items

• Implement and use workspace logging

Implement workspace-level access controls

Implement item-level access controls

Implement row-level, column-level, object-level, and folder/file-level access controls

Implement dynamic data masking

Apply sensitivity labels to items

Endorse items

Implement and use workspace logging

Orchestrate processes

• Choose between a pipeline and a notebook

• Design and implement schedules and event-based triggers

• Implement orchestration patterns with notebooks and pipelines, including parameters and dynamic expressions

Choose between a pipeline and a notebook

Design and implement schedules and event-based triggers

Implement orchestration patterns with notebooks and pipelines, including parameters and dynamic expressions

Ingest and transform data (30–35%)

Design and implement loading patterns

• Design and implement full and incremental data loads

• Prepare data for loading into a dimensional model

• Design and implement a loading pattern for streaming data

Design and implement full and incremental data loads

Prepare data for loading into a dimensional model

Design and implement a loading pattern for streaming data

Ingest and transform batch data

• Choose an appropriate data store

• Choose between dataflows, notebooks, KQL, and T-SQL for data transformation

• Create and manage shortcuts to data

• Implement mirroring

• Ingest data by using pipelines

• Transform data by using PySpark, SQL, and KQL

• Denormalize data

• Group and aggregate data

• Handle duplicate, missing, and late-arriving data

Choose an appropriate data store

Choose between dataflows, notebooks, KQL, and T-SQL for data transformation

Create and manage shortcuts to data

Implement mirroring

Ingest data by using pipelines

Transform data by using PySpark, SQL, and KQL

Denormalize data

Group and aggregate data

Handle duplicate, missing, and late-arriving data

Ingest and transform streaming data

• Choose an appropriate streaming engine

• Choose between native storage, followed storage, or shortcuts in Real-Time Intelligence

• Process data by using eventstreams

• Process data by using Spark structured streaming

• Process data by using KQL

• Create windowing functions

Choose an appropriate streaming engine

Choose between native storage, followed storage, or shortcuts in Real-Time Intelligence

Process data by using eventstreams

Process data by using Spark structured streaming

Process data by using KQL

Create windowing functions

Monitor and optimize an analytics solution (30–35%)

Monitor Fabric items

• Monitor data ingestion

• Monitor data transformation

• Monitor semantic model refresh

• Configure alerts

Monitor data ingestion

Monitor data transformation

Monitor semantic model refresh

Configure alerts

Identify and resolve errors

• Identify and resolve pipeline errors

• Identify and resolve dataflow errors

• Identify and resolve notebook errors

• Identify and resolve eventhouse errors

• Identify and resolve eventstream errors

• Identify and resolve T-SQL errors

Identify and resolve pipeline errors

Identify and resolve dataflow errors

Identify and resolve notebook errors

Identify and resolve eventhouse errors

Identify and resolve eventstream errors

Identify and resolve T-SQL errors

Optimize performance

• Optimize a lakehouse table

• Optimize a pipeline

• Optimize a data warehouse

• Optimize eventstreams and eventhouses

• Optimize Spark performance

• Optimize query performance

Optimize a lakehouse table

Optimize a pipeline

Optimize a data warehouse

Optimize eventstreams and eventhouses

Optimize Spark performance

Optimize query performance