Querying and reshaping event data with KQL

In Microsoft Fabric, KQL (Kusto Query Language) is used to query and transform data stored in Eventhouse KQL databases, typically for real-time or high-volume analytical workloads. Learners create KQL Querysets to write, run, and save KQL queries against tables, and can also use KQL for lightweight transformations before or during ingestion.

Must-know

• KQL queries follow a tabular, pipe (|) based syntax where operators like where, extend, summarize, and project are chained left to right to progressively filter and shape data.
• The 'let' statement can define reusable variables or scalar/tabular expressions to simplify complex queries.
• Update policies allow automatic transformation of data as it lands in a source table, writing derived/transformed results into a target table without manual re-run of queries.
• The mv-expand operator is used to expand dynamic (JSON-like) arrays or property bags into multiple rows for easier analysis.
• KQL Querysets in Fabric can connect to one or more KQL databases (and cross-query with Lakehouse/Warehouse via appropriate connectors), and results can be visualized or pinned to dashboards.
• Functions such as bin(), ago(), and datetime() are commonly used for time-series bucketing and filtering, which is a core use case for KQL-based Eventhouse data.

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.