Routing and reshaping live events with an Eventstream
Microsoft Fabric Eventstreams is a no-code feature within Real-Time Intelligence that lets you capture, transform, and route real-time event data from multiple sources to multiple destinations. It provides a visual, drag-and-drop canvas for building streaming pipelines without writing code, using built-in transformation operations. Eventstreams integrates natively with other Fabric items such as KQL Databases, Lakehouses, and Fabric Activator.
Must-know
- Eventstreams support built-in sources like Azure Event Hubs, IoT Hub, Kafka, Azure SQL DB Change Data Capture, and sample/demo data, plus custom endpoints.
- Available transformations include Filter, Aggregate (windowed), Group By, Union, Join, Expand, Manage Fields, and Manage Types, applied visually on the stream editor canvas.
- Eventstreams can fan out a single stream to multiple destinations simultaneously, such as a KQL Database, Lakehouse, Fabric Activator, or another Eventhouse.
- There are two editing/processing modes: the default no-code editor for source-to-destination routing, and an Enhanced Edit mode that unlocks the full set of stream transformation operators before landing data in a destination.
- Eventstreams uses Fabric capacity for compute and billing, so throughput and cost scale with the capacity SKU rather than a separate streaming units model.
- A common gotcha: transformations added in Enhanced Edit mode must be published, and once published some structural changes (like source/destination rewiring) may require careful reconfiguration rather than simple edits.
A retailer ingests point-of-sale events into a Fabric eventstream. Order lines with a Quantity value of zero or less are test records and should never reach the Lakehouse destination, while every valid order line must still flow through unchanged. Which eventstream transformation should the engineer add before the destination to meet 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.