Microsoft Certified: Fabric Data Engineer Associate (DP-700) Practice Exams & Study Guide

Configure Microsoft Fabric workspace settings including configuring Spark workspace settings, configuring domain workspace settings, configuring OneLake workspace settings, and configuring data workflow workspace settings. Implement lifecycle management in Fabric including configuring version control, implementing database projects, and creating and configuring deployment pipelines. Configure security and governance including implementing workspace-level access controls, implementing item-level access controls, implementing row-level, column-level, object-level, and folder/file-level access controls, implementing dynamic data masking, applying sensitivity labels to items, endorsing items, implementing and using workspace logging, and configuring and implementing OneLake security. Orchestrate processes including choosing between Dataflow gen 2, a pipeline and a notebook, designing and implementing schedules and event-based triggers, and implementing orchestration patterns with notebooks and pipelines (including parameters and dynamic expressions).

Design and implement loading patterns including designing and implementing full and incremental data loads, preparing data for loading into a dimensional model, and designing and implementing a loading pattern for streaming data. Ingest and transform batch data including choosing an appropriate data store, choosing between dataflows, notebooks, KQL, and T-SQL for data transformation, creating and managing shortcuts to data, implementing mirroring, ingesting data by using pipelines, ingesting data by using continuous integration from OneLake, transforming data by using Power Query (M), PySpark, SQL, and KQL, denormalizing data, grouping and aggregating data, and handling duplicate, missing, and late-arriving data. Ingest and transform streaming data including choosing an appropriate streaming engine, choosing between native storage, mirrored storage, or shortcuts in Real-Time Intelligence, choosing between accelerated shortcuts and non-accelerated shortcuts in Real-Time Intelligence, processing data by using eventstreams, processing data by using Spark structured streaming, processing data by using KQL, and creating windowing functions.

Monitor Fabric items including monitoring data ingestion, monitoring data transformation, monitoring semantic model refresh, and configuring alerts. Identify and resolve errors including identifying and resolving pipeline errors, identifying and resolving dataflow errors, identifying and resolving notebook errors, identifying and resolving eventhouse errors, identifying and resolving eventstream errors, identifying and resolving T-SQL errors, and identifying and resolving Shortcut errors. Optimize performance including optimizing a lakehouse table, optimizing a pipeline, optimizing a data warehouse, optimizing eventstreams and eventhouses, optimizing Spark performance, and optimizing query performance.