Writing transform logic in PySpark, SQL, or KQL
In Microsoft Fabric, you can transform data using PySpark and SQL in Notebooks against Lakehouse tables/files, and using KQL in KQL Queryset (or update policies) against Eventhouse/KQL databases. Choosing the right language and engine depends on the data store (Lakehouse vs. Eventhouse), the data format, and whether you need distributed batch processing, declarative SQL transforms, or near-real-time analytical queries.
Must-know
- Fabric Notebooks support PySpark, Spark SQL, Scala, and SparkR (via %%language magic cells), letting you mix PySpark DataFrame operations with SQL queries in the same notebook against Lakehouse tables and files.
- PySpark DataFrame transformations (select, filter, withColumn, groupBy, join, etc.) execute lazily and are optimized/executed by the Spark engine only when an action (e.g., show, write, count) is triggered.
- You can register a DataFrame as a temporary view (createOrReplaceTempView) to run Spark SQL statements against it, or query Lakehouse Delta tables directly with SQL using spark.sql().
- T-SQL against the SQL analytics endpoint of a Lakehouse or a Warehouse is read/query-focused for the Lakehouse endpoint (no DML), whereas the Warehouse supports full T-SQL DML (INSERT/UPDATE/DELETE/MERGE) for transformations.
- KQL (Kusto Query Language) is used to query and transform data in Eventhouse/KQL databases via the KQL Queryset, using operators like extend, project, summarize, and mv-expand for filtering, shaping, and aggregating streaming or log-style data.
- Update policies in a KQL database let you automatically transform and route incoming data from a source table into one or more target tables using a KQL function, enabling lightweight ETL within the Eventhouse without moving data to Spark.
A data engineer is transforming a lakehouse table in a Fabric notebook using PySpark. The source table contains a column named Items that stores an array of struct values (ProductId, Quantity) for each order. The engineer needs to produce one output row per item while keeping the other order-level columns (OrderId, OrderDate) unchanged for each resulting row. Which transformation should the engineer apply to the DataFrame?
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.