Picking a transformation tool from dataflows, notebooks, KQL, or T-SQL
Microsoft Fabric offers multiple transformation engines, and the exam expects you to pick the right one based on data volume, skill set, and target workload. Dataflows Gen2 suit low-code, Power Query-based ETL; notebooks suit large-scale, code-first transformations with Spark; KQL is for high-velocity log/telemetry analytics in Eventhouse/KQL databases; T-SQL is for relational transformations in the Warehouse and SQL analytics endpoint of the Lakehouse.
Must-know
- Dataflows Gen2 use the Power Query engine, are best for citizen developers and smaller-to-medium datasets, and can output directly to a Lakehouse, Warehouse, or other destinations via data destinations.
- Notebooks (PySpark, Spark SQL, Scala, R) run on Spark compute and are the preferred choice for large-scale, complex, or custom transformations requiring full programmatic control and reuse of existing code.
- KQL (Kusto Query Language) is used specifically for querying and transforming data in KQL databases/Eventhouses, optimized for time-series, streaming, and log/telemetry data with fast ingestion and near real-time analytics.
- T-SQL is used in the Fabric Warehouse (read-write) and via the SQL analytics endpoint of a Lakehouse (read-only) for set-based, relational transformations by users with traditional SQL skills.
- Dataflows Gen2 can have higher compute cost and slower performance at scale compared to notebooks, so large or performance-critical pipelines often favor Spark notebooks instead.
- You cannot write to a Lakehouse table via T-SQL through the SQL analytics endpoint: write operations to Lakehouse require Spark (notebooks) or Dataflows Gen2, while the Warehouse itself fully supports T-SQL DML.
A team of business analysts at a retail company needs to clean and reshape sales data from several CSV files stored in a Fabric Lakehouse before loading it into a semantic model. The analysts have Power Query experience but limited coding skills, and they want a visual, low-code interface with built-in data profiling and the ability to reuse transformation logic across future file loads. Which transformation approach should they use?
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.