Speeding up a Lakehouse table with maintenance operations
Optimizing a Lakehouse table in Microsoft Fabric primarily involves compacting small Parquet files into larger ones and applying Delta Lake write optimizations to improve query performance and reduce storage/compute overhead. Fabric provides both automatic maintenance settings and manual commands (OPTIMIZE, VACUUM, Z-Order) to keep Delta tables efficient over time.
Must-know
- V-Order is a Fabric-specific write-time optimization applied to Parquet files that improves read performance for Power BI, SQL, and Spark engines by sorting and encoding data for faster compression and retrieval, and it is enabled by default for Spark writes in Fabric.
- The OPTIMIZE command compacts many small files into fewer, larger files to reduce metadata overhead and improve read throughput, and can be combined with ZORDER BY on frequently filtered columns to colocate related data.
- VACUUM removes old, unreferenced data files left behind by updates/deletes/compaction after the table's retention period (default 7 days), reclaiming storage but permanently removing time-travel history beyond that window.
- Table maintenance can be configured to run automatically (scheduled or triggered) in Fabric Lakehouse settings, or run manually via Spark notebooks/SQL, giving flexibility between hands-off and on-demand optimization.
- Small file problems typically arise from frequent, small streaming or incremental writes; increasing batch size or scheduling periodic OPTIMIZE jobs mitigates this and reduces job planning/read latency.
- Partitioning strategy matters: over-partitioning a table (e.g., by high-cardinality columns) can worsen performance by creating excessive small files, so partition columns should be chosen based on common filter predicates and reasonable cardinality.
A data engineer ingests IoT telemetry into a Fabric Lakehouse Delta table through frequent small streaming micro-batches. After several months, query performance has degraded because the table now consists of thousands of very small Parquet files. Without changing the ingestion pattern, which action should the engineer take first to resolve this?
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.