Setting up an alert to catch a failure early
Fabric provides several native mechanisms to configure alerts that notify users or trigger actions when specific conditions occur in data, pipelines, or capacity usage. The primary no-code tool is Data Activator (Reflex), which continuously monitors data from Power BI, Eventstreams, or KQL querysets and fires triggers when defined conditions are met. Learners should know where alerts can be set (visuals, capacity metrics, Activator) and how notifications/actions are delivered.
Must-know
- Data Activator (Reflex) is the main Fabric service for building condition-based alerts on streaming or batch data from Power BI reports, Eventstreams, or KQL querysets, without writing code.
- In Power BI, you can set alerts directly on dashboard tiles or Real-Time Dashboards using the 'Set alert' option for numeric or KPI visuals, triggered when a value crosses a defined threshold.
- The Fabric Capacity Metrics app lets admins configure alerts on capacity utilization (CU%) to proactively notify before throttling or overage penalties occur.
- Alerts created through Data Activator can trigger actions such as Teams messages, emails, or Power Automate flows, giving flexible downstream automation.
- Alert rules require appropriate workspace and data source permissions; if the underlying semantic model, dataset, or Eventstream schema changes significantly, existing alerts may need to be reconfigured or recreated.
- Data Activator triggers are defined on 'objects' (entities) with properties and events, and conditions can be simple thresholds or more complex patterns (e.g., no activity within X minutes).
A data engineer ingests IoT temperature readings into a Fabric eventstream. The team wants to be notified in a Microsoft Teams channel as soon as three consecutive readings from any sensor exceed 90 degrees, without building a custom polling solution. Which approach should the engineer configure?
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.