Microsoft Fabric Test

This skill focuses on the hybrid Lakehouse architecture, bridging the gap between traditional data warehouses and data lakes. Candidates should understand the differences between handling structured and unstructured data, managing and optimizing storage within the Lakehouse architecture, and ensuring data governance and metadata management. The test also covers the Medallion architecture (Bronze, Silver, Gold layers) and its relevance in Microsoft Fabric. Advanced questions will assess the application of lakehouse concepts in large-scale enterprise environments.

This skill assesses expertise in building, managing, and optimizing data pipelines using Azure Data Factory and Synapse Analytics. The focus is on constructing scalable ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes for ingesting, transforming, and loading large datasets. Candidates are evaluated on their ability to orchestrate pipelines, manage dependencies, execute trigger-based processes, and handle real-time/batch processing. Advanced topics include pipeline automation, error handling, and optimizing pipeline performance for enterprise-level data flows.

This skill emphasizes Delta Lake technologies and their integration with Microsoft Fabric. Candidates will be tested on real-time data ingestion techniques, handling incremental data loads, using Delta tables for transactional data, and managing schema enforcement. Topics include data shortcuts for faster data access and efficient use of delta logs for tracking changes in data. Advanced questions involve large-scale integration across multiple data sources, ensuring consistency in delta lakes, and advanced management of data in distributed environments.

This skill evaluates the candidate’s ability to ensure data security and maintain strong data governance practices in Microsoft Fabric. Basic questions cover role-based access control (RBAC) and data encryption at rest and in transit. Medium and advanced questions focus on implementing multi-tenant environments, enforcing data privacy standards like GDPR and CCPA, and managing large-scale governance frameworks. Advanced scenarios may involve setting up audit trails, configuring compliance reporting, and handling security across different data sources within Fabric.

This skill covers strategies for improving the performance of data pipelines and query executions in Microsoft Fabric. Candidates must understand various optimization techniques like partitioning, indexing, query optimization, and Spark job tuning. The focus is on efficiently handling large datasets while minimizing resource consumption and improving response times. Advanced questions will evaluate the candidate’s ability to optimize for high-concurrency scenarios, dynamically scale resources, and perform load balancing to meet the demands of enterprise data analytics workflows.

This skill focuses on creating data models using Power BI and writing complex DAX (Data Analysis Expressions) queries to extract actionable insights. Candidates must demonstrate their ability to build robust data models, optimize semantic models for performance, and work with advanced calculations using DAX. Topics include measures, calculated columns, and performance tuning in Power BI reports. Advanced-level questions will involve handling large datasets, optimizing models for real-time analytics, and integrating Power BI with other Fabric components for end-to-end analytics.

This skill assesses the candidate's proficiency in setting up and managing real-time data streams using Azure Event Hubs, Azure Stream Analytics, and Apache Kafka. Candidates need to demonstrate their ability to configure pipelines that process streaming data and deliver real-time insights. Intermediate-level questions cover integrating streams into Fabric, while advanced questions explore managing high-volume, high-velocity streams and designing for scalability in mission-critical environments.

This skill focuses on advanced configurations of Apache Spark within Microsoft Fabric, particularly for high-performance data processing. Candidates must know how to configure Spark clusters for different workloads, optimize Spark jobs, and use adaptive query execution to improve performance. Topics include resource management, scalability, and advanced troubleshooting for performance bottlenecks. Advanced questions may cover tuning Spark for high-concurrency environments and managing complex distributed computing tasks using Spark in Fabric.

This skill explores the automation and AI-driven capabilities of Copilot in Microsoft Fabric. Candidates should understand how to use Copilot for automating routine data tasks, integrating machine learning models, and leveraging AI to improve data pipeline efficiency. Basic questions will cover task automation, while advanced questions focus on AI integration for predictive analytics, anomaly detection, and building automated models that adapt to changing data trends. Candidates should also be familiar with using AI-driven insights to drive business decisions in real-time.

This proficiency evaluates a candidate’s ability to apply built-in AI capabilities and Copilot assistance across Microsoft Fabric workloads. It focuses on how Fabric professionals use AI to accelerate development, automate transformations, optimize queries, and enhance real-time analytics