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Microsoft Certified Fabric Data Engineer Associate

About this course

This “Exam Prep DP-700: Microsoft Fabric Data Engineer Associate” Specialization comprehensive certification course is designed for data professionals aiming to master end-to-end analytics design, implementation, and management using Microsoft Fabric. Dive deep into Microsoft’s unified data platform and learn how to configure, secure, and orchestrate data solutions across Fabric’s integrated services – covering data engineering, data science, and business intelligence workloads.

Skills Measured

  • Implement and manage an analytics solution
  • Ingest and transform data
  • Monitor and optimize an analytics solution

COURSE OUTLINE

Implement and manage an analytics solution
Configure Microsoft Fabric workspace settings
  • Configure Spark workspace settings
  • Configure domain workspace settings
  • Configure OneLake workspace settings
  • Configure data workflow workspace settings
  • Configure version control
  • Implement database projects
  • Create and configure deployment pipelines
  • 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
  • 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
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
  • 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 streaming engine
  • Choose between native storage, mirrored 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
  • 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 a lakehouse table
  • Optimize a pipeline
  • Optimize a data warehouse
  • Optimize event streams and event houses
  • Optimize Spark performance
  • Optimize query performance