Making a slow query run faster
Optimizing query performance in Microsoft Fabric involves analyzing query execution plans, leveraging caching, and choosing the right compute and storage strategies for your workload. Fabric provides several native tools, such as Query Insights, the Monitoring Hub, and capacity metrics, to identify bottlenecks and tune performance across Warehouse, Lakehouse, and Power BI/Direct Lake workloads.
Must-know
- The Fabric Warehouse and SQL Analytics Endpoint expose Query Insights (via built-in views like
queryinsights.exec_requests_history) to help identify long-running or resource-intensive queries. - Result set caching in the Fabric Warehouse can automatically reuse cached results for repeated identical queries, reducing compute consumption and latency until underlying data changes.
- V-Order optimization on Delta Parquet files improves read performance for Lakehouse and Warehouse queries by producing more efficiently compressed and sortable file layouts, but it adds write-time overhead so it should be applied judiciously.
- Table maintenance operations, OPTIMIZE (file compaction/bin-packing) and VACUUM (removing stale files), reduce small-file fragmentation and improve scan performance on Delta tables in the Lakehouse.
- Partitioning and choosing appropriate distribution/indexing strategies (e.g., clustered columnstore behavior in Warehouse) can significantly reduce data scanned and improve join performance, but over-partitioning can hurt performance on smaller tables.
- Direct Lake mode in Power BI avoids costly Import/DirectQuery translation layers by reading Delta Parquet files directly, but performance depends on well-optimized (V-Order, compacted) files and can fall back to DirectQuery if guardrails (like row/version limits) are exceeded.
A data engineer ingests IoT sensor readings into a Fabric Lakehouse table every few seconds using a streaming pipeline. Over several weeks, ad hoc SQL queries against the table become noticeably slower, even though the table's total size has grown only modestly. Which action is most likely to restore query performance?
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 12, 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.