Shaping source data ahead of a dimensional-model load
Preparing data for a dimensional model in Microsoft Fabric involves shaping raw data into fact and dimension tables using structures like star or snowflake schemas, typically within Dataflows Gen2, Fabric notebooks (PySpark/Spark SQL), or pipelines. The goal is to transform source data into a form optimized for analytical querying, with clear separation between measurable facts and descriptive dimensions.
Must-know
- Dimension tables should contain descriptive attributes and a surrogate key (typically an integer, generated during transformation) rather than relying solely on natural/business keys from source systems.
- Fact tables should store foreign keys referencing dimension surrogate keys plus numeric measures, and should be at a consistent, well-defined grain (e.g., one row per transaction line).
- Slowly Changing Dimensions (SCDs) require explicit handling logic (e.g., Type 1 overwrite vs. Type 2 versioning with effective/expiry dates) since Fabric does not automatically manage historical change tracking for you.
- A common gotcha is failing to add a dedicated "Unknown" or "N/A" member row (often with key 0 or -1) in dimension tables to handle late-arriving or missing foreign key references in fact data, which otherwise causes join failures or null joins.
- Date/time dimensions are typically pre-built or generated as a standalone table with one row per day (or finer granularity) rather than derived ad hoc, ensuring consistent time-based filtering and rollups across fact tables.
- In Fabric, dimensional model preparation commonly leverages the medallion architecture, transforming and conforming data in the silver layer before loading curated star-schema tables into the gold layer for consumption by Power BI or SQL analytics endpoints.
A data engineer is preparing a Customer dimension for a Fabric warehouse using a Dataflow Gen2. The business requires that historical sales reports always show the customer's address exactly as it was at the time of the sale, even after the customer later moves. Which approach should the data engineer use when preparing the dimension data before loading it?
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.