Matching a workload to the right Fabric data store
Microsoft Fabric provides multiple data store options (Lakehouse, Warehouse, and specialized stores like KQL Database and Eventhouse), each optimized for different data types, workloads, and skill sets. Choosing the right store depends on whether you need file-based flexibility, SQL-based structured analytics, or real-time analytical queries over streaming/log data.
Must-know
- A Lakehouse stores data as files (Delta/Parquet) in OneLake and supports both structured tables and unstructured files, making it ideal for data engineering, data science, and Spark-based workloads.
- A Warehouse provides a fully relational, T-SQL-based experience with full DDL/DML support and is best suited for traditional data warehousing, BI reporting, and teams with strong SQL skills.
- Both Lakehouse and Warehouse store data in Delta Lake format in OneLake, enabling cross-engine querying (e.g., a Warehouse's SQL endpoint can query Lakehouse tables and vice versa) without data duplication.
- KQL Database (within an Eventhouse) is optimized for high-velocity, time-series, and semi-structured data such as logs, telemetry, and IoT streams, using Kusto Query Language (KQL) for near real-time analytics.
- Lakehouse SQL endpoints are read-only for querying Delta tables via T-SQL, while write operations to Lakehouse tables must go through Spark, pipelines, or dataflows, not the SQL endpoint.
- Choice of data store should be driven by workload pattern (batch vs. streaming), query language preference (Spark/PySpark vs. T-SQL vs. KQL), governance/security needs, and downstream consumption (Power BI, notebooks, or client apps).
A data engineering team at a retail company must maintain a curated dimensional model that is refreshed nightly using ELT logic requiring multi-statement T-SQL transactions, including INSERT, UPDATE, and DELETE operations across several related tables. The resulting model is consumed directly by Power BI reports through T-SQL queries. Which Fabric data store should the team choose to host this model?
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.