Moving data into Fabric with a data pipeline
Fabric Data Factory pipelines let you orchestrate and automate data ingestion from a wide range of on-premises and cloud sources into Fabric using Copy Data activities and other pipeline activities. Pipelines are built from activities (Copy, Dataflow, Notebook, Stored Procedure, etc.) connected in a directed workflow with parameters, triggers, and control-flow logic like ForEach, If Condition, and Until. They provide a low-code, visual authoring experience similar to Azure Data Factory/Synapse pipelines, since Fabric pipelines share the same underlying engine.
Must-know
- The Copy activity is the primary tool for ingesting data at scale into Fabric destinations such as Lakehouse tables/files, Warehouse tables, and KQL databases, and supports schema mapping, partitioning, and staging for performance.
- Connections to sources use connectors and linked-service-like connection objects; on-premises or private-network sources require an On-premises Data Gateway or a Virtual Network (VNet) data gateway.
- Pipelines can be triggered on a schedule, via tumbling window, event-based triggers (e.g., storage events), or on-demand/manual execution, and can be invoked from another pipeline using the Invoke Pipeline activity.
- Parameters and variables allow pipelines to be reused across environments/sources, while pipeline expressions (dynamic content) let you reference activity outputs and system variables at runtime.
- Copy Data assistant provides a wizard-driven experience to quickly configure source, destination, and mapping for common ingestion scenarios without manually building the pipeline canvas.
- Monitoring is available through the Fabric Monitoring hub and pipeline run history, which shows activity-level status, duration, and error details for troubleshooting failed or long-running ingestions.
A pipeline ingests order records from an on-premises SQL Server database into a Fabric Lakehouse every night. The source table has millions of rows, and only a small number of rows are inserted or updated each day, identified by a ModifiedDate column. Reloading the entire table nightly is too slow. What should the engineer implement in the Copy activity to load only the changed rows efficiently?
What you have tried across DP-700's objectives, not a readiness score.
Implement and manage an analytics solution
- Tuning a workspace's Spark compute defaults and pool sizing
- Grouping and governing workspaces with a Fabric domain
- Setting per-workspace defaults for OneLake storage
- Standing up an Airflow job runtime inside a workspace
- Connecting a workspace to a Git repository
- Managing schema changes with a database project
- Promoting Fabric items across environments with a deployment pipeline
- Granting and restricting access at the workspace level
- Locking down who can open a single Fabric item
- Layering row, column, object, and file-level security rules
- Hiding sensitive column values behind a dynamic mask
- Classifying Fabric items with a sensitivity label
- Marking a trusted item as promoted or certified
- Reading a Fabric audit log to see who did what
- Securing data at the OneLake storage layer
- Picking the right build tool among a dataflow, a pipeline, and a notebook
- Kicking off a job on a schedule or in response to an event
- Chaining notebooks and pipelines together with parameters and dynamic expressions
Ingest and transform data
- Deciding between a full reload and an incremental load
- Shaping source data ahead of a dimensional-model load
- Landing a continuous stream of data into storage
- Matching a workload to the right Fabric data store
- Picking a transformation tool from dataflows, notebooks, KQL, or T-SQL
- Linking to external data without copying it via a OneLake shortcut
- Keeping a source database continuously replicated into Fabric
- Moving data into Fabric with a data pipeline
- Writing transform logic in PySpark, SQL, or KQL
- Flattening related tables into one wide, denormalized shape
- Rolling records up with group-by aggregations
- Dealing with duplicate rows, gaps, and data that arrives late
- Selecting the right engine for a real-time workload
- Weighing storage-in-place against a linked shortcut for a Real-Time Intelligence table
- Weighing an accelerated shortcut against a standard one for query speed
- Routing and reshaping live events with an Eventstream
- Handling a continuous flow of records with Spark's structured streaming
- Querying and reshaping event data with KQL
- Aggregating a stream over sliding or tumbling time windows
Monitor and optimize an analytics solution
- Watching an ingestion job's health and progress
- Watching a transformation job's health and progress
- Tracking whether a semantic model's refresh actually succeeded
- Setting up an alert to catch a failure early
- Tracking down why a pipeline run failed and fixing it
- Diagnosing why a dataflow run failed
- Debugging a notebook run that failed
- Troubleshooting a misbehaving Eventhouse
- Troubleshooting a misbehaving Eventstream
- Debugging a T-SQL statement that failed
- Fixing a broken or unreachable shortcut
- Speeding up a Lakehouse table with maintenance operations
- Making a slow pipeline run faster
- Tuning a Fabric warehouse for faster queries
- Improving throughput on real-time streaming components
- Tuning a Spark job to run faster and cheaper
- Making a slow query run faster
Coverage checked against the published exam guide on Aug 11, 2026.
These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.