Kicking off a job on a schedule or in response to an event
Microsoft Fabric lets you automate pipeline (and other item) execution using time-based schedules or event-based triggers so that data movement and transformation run without manual intervention. Schedules are configured per item (pipeline, notebook, dataflow) with recurrence and time-zone settings, while event triggers fire pipelines in response to Fabric or storage events surfaced through the Real-Time hub/Reflex (Data Activator).
Must-know
- Each Fabric item (Data pipeline, notebook, dataflow, semantic model refresh) has its own Schedule settings pane where you set a start date/time, time zone, and recurrence (once, by minutes, hourly, daily, or weekly on specific days).
- The minimum supported schedule interval is a few minutes (commonly quoted as no more frequent than every 1-5 minutes depending on item type), so near-real-time needs should use event-based triggers instead.
- Event-based automation in Fabric is delivered through the Real-Time hub/Data Activator (Reflex), which can subscribe to Fabric workspace events (e.g., item refresh completed) or external events like Azure Blob/ADLS storage 'blob created/deleted' to start a pipeline or send an alert.
- Storage event triggers require the underlying storage account to have Event Grid enabled and the Fabric workspace/service principal to have the necessary permissions on that storage account.
- A single pipeline can have multiple triggers attached (e.g., one schedule plus one or more event triggers), and each trigger can be independently enabled or disabled without republishing the whole pipeline.
- Monitoring of both scheduled and event-triggered runs is done through the pipeline's Run history/Monitor hub, which shows trigger type, trigger time, and status, aiding troubleshooting of missed or duplicate runs.
A data engineer must configure a Fabric data pipeline's schedule trigger so that it runs at 06:00 every Monday through Friday and does not run on Saturday or Sunday, without anyone having to manually pause or resume the trigger. Which configuration meets this requirement?
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.