Create, configure, and manage Microsoft Fabric Data Agents that enable natural language Q&A over lakehouses, warehouses, Power BI semantic models, KQL databases, and ontologies. Use when asked to build data agents, configure NL2SQL/NL2DAX/NL2KQL experiences, write agent instructions, create example queries, automate data agent provisioning via REST API or PowerShell, integrate Fabric data agents with Azure AI Foundry, or troubleshoot data agent configuration issues.

Diagnose and resolve Microsoft Fabric Data Agent issues including tenant settings, data source configuration, query generation failures, cross-region capacity errors, XMLA endpoint setup, Power BI semantic model integration, lakehouse/warehouse/KQL connectivity, example query validation, publishing/sharing problems, and Azure AI Foundry integration. Use when asked to troubleshoot data agent, fix Fabric AI agent, debug NL2SQL/NL2DAX/NL2KQL, resolve Copilot tenant settings, or diagnose Fabric data agent errors.

Guide for working with Microsoft Fabric IQ (preview), the semantic intelligence workload for unified data and business vocabulary. Use when creating or managing ontology items, defining entity types, binding data to ontologies, creating relationship types, configuring data agents with ontology sources, working with Graph in Microsoft Fabric, managing Fabric IQ tenant settings, querying ontology graphs, generating ontologies from Power BI semantic models, or remediate Fabric IQ preview features. Covers ontology, graph, data agent, operations agent, and semantic model items.

Troubleshoot Microsoft Fabric Lakehouse access control issues including OneLake security roles, SQL analytics endpoint permissions, workspace roles, data access roles, row-level security (RLS), column-level security (CLS), object-level security (OLS), dynamic data masking, shortcut permissions, Direct Lake security integration, DefaultReader role, ReadAll permission, and OneLake data access role conflicts. Use when users report permission denied, unauthorized access, missing data, empty query results, or cannot see tables in Fabric Lakehouse.

Diagnose and resolve Microsoft Fabric network performance issues including connectivity failures, latency, private endpoints, managed VNets, outbound access protection, gateway diagnostics, OneLake endpoint routing, service tag configuration, firewall allowlisting, DNS resolution, and Spark session startup delays. Use when remediate Fabric networking, slow Spark jobs, connection timeouts, private link errors, managed private endpoint approval, or capacity throttling.

Diagnose and resolve Microsoft Fabric OneLake performance issues including slow queries, cold cache latency, small file problems, Delta table fragmentation, V-Order optimization, Spark throttling, capacity SKU sizing, and cross-region data access. Use when remediate OneLake read/write performance, lakehouse query slowness, Direct Lake fallback, table maintenance failures, Spark concurrency limits, warehouse cold starts, or optimizing Delta parquet file layouts. Supports PowerShell, T-SQL, and Spark SQL diagnostic workflows.

Diagnose and resolve Microsoft Fabric Power BI security issues including row-level security (RLS), object-level security (OLS), column-level security (CLS), workspace permissions, sensitivity labels, service principal authentication, XMLA endpoint access, DirectLake security fallback, Entra ID app registration, and data loss prevention (DLP) policy restrictions. Use when remediate access denied errors, missing data in reports, broken visuals from OLS, RLS not filtering, sensitivity label problems, permission model conflicts, or capacity-related security issues.

Diagnose and resolve Apache Spark performance issues in Microsoft Fabric notebooks and Spark Job Definitions. Use when PySpark jobs are slow, notebooks take too long, Spark stages are skewed, shuffles are excessive, out-of-memory errors occur, Delta Lake writes are slow, or Fabric capacity is throttled. Covers data skew, shuffle optimization, broadcast joins, partition tuning, VOrder, Optimized Write, resource profiles, autotune, native execution engine, small file compaction, and Spark UI interpretation. Keywords include slow notebook, OOM, spill, shuffle, skew, broadcast, repartition, coalesce, OPTIMIZE, VACUUM, Z-ORDER, checkpoint, cache, persist, executor memory, driver memory, spark.sql.shuffle.partitions, autoBroadcastJoinThreshold, maxPartitionBytes, Fabric capacity throttling, CU utilization.

Diagnose and resolve Microsoft Fabric REST API errors including HTTP 401 Unauthorized, 403 Forbidden, 404 Not Found, 429 Throttling, and 5xx server errors. Use when debugging Fabric API authentication failures, Entra ID token issues, insufficient scopes, throttling/rate limiting, long running operation (LRO) polling failures, pagination problems, service principal configuration, or capacity API errors. Covers PowerShell and C# remediate workflows against api.fabric.microsoft.com.

Diagnose and resolve performance issues in Microsoft Fabric Delta Lake and Spark workloads. Use when remediate slow Spark notebooks, long-running Spark jobs, Delta table query degradation, small file problems, VOrder configuration, resource profile tuning, autotune configuration, capacity throttling (HTTP 430), session startup delays, executor memory errors, skewed partitions, or streaming ingestion bottlenecks. Covers Lakehouse, SQL analytics endpoint, Spark compute, environment configuration, Delta OPTIMIZE, VACUUM, Z-Order, bin-compaction, and Fabric capacity SKU sizing.

Diagnose and resolve Microsoft Fabric Spark compute issues including HTTP 430 throttling errors, job queueing failures, slow session startup, autoscale problems, capacity SKU limits, environment publishing failures, library conflicts, node sizing, burst factor configuration, and VNet provisioning delays. Use when remediate Spark notebooks, Spark job definitions, lakehouse operations, starter pools, custom pools, or concurrency limits in Fabric workspaces.

Diagnose and resolve performance issues with Microsoft Fabric User Data Functions. Use when functions are slow, timing out, returning errors, consuming excessive capacity units, or exhibiting cold start latency. Covers execution timeouts, response size limits, connection bottlenecks, logging analysis, capacity metrics monitoring, invocation diagnostics, Python optimization, and UDF service limit remediate.