Azure Analysis Services Test

Focuses on building and managing tabular data models, including the creation of relationships between tables, hierarchies, calculated columns, and measures. It evaluates a candidate's ability to design efficient data models, write DAX queries, and use best practices to optimize performance. Also includes working with large datasets and ensuring data accuracy through proper relationships and modeling techniques.

Examines knowledge of implementing security in AAS, including Role-Based Access Control (RBAC), Row-Level Security (RLS), and integration with Azure Active Directory (AAD). Topics include creating and managing security roles, defining security policies, and enforcing data access restrictions. Advanced questions will cover dynamic security models based on user attributes and multi-tenant security architectures.

Evaluates the ability to configure and manage data imports from a variety of sources, such as on-premise databases (SQL Server) and cloud services (Azure SQL Database, Azure Data Lake). The topic also explores the use of Direct Query for real-time data access and challenges in managing connectivity, data refresh strategies, and the implications of large-scale data imports on performance.

Tests understanding of query optimization techniques, focusing on the VertiPaq engine, DAX (Data Analysis Expressions) optimization, and other performance improvement methods. Includes topics such as managing memory consumption, improving query response times, and optimizing data refresh processes for large-scale datasets. Performance tuning for specific use cases, such as high-throughput scenarios, will be covered in advanced questions.

Covers advanced techniques for partitioning models and using aggregations to enhance performance in large data models. This topic focuses on setting up partitions to manage large datasets more effectively, implementing aggregations to speed up query times, and using incremental refresh strategies to maintain up-to-date data without overloading the system. Real-world scenarios and trade-offs for partitioning models are emphasized in harder questions.

Evaluates knowledge of integrating Azure Analysis Services with Power BI and other analytics tools. This includes setting up data sources, publishing models to Power BI, and using tools like SQL Server Data Tools (SSDT) for developing and deploying models. Advanced topics cover managing live connections in Power BI, handling large datasets, and optimizing performance for Power BI dashboards connected to AAS.

Assesses the ability to automate data refresh and processing pipelines using Azure Data Factory and other orchestration tools. The topic covers creating end-to-end automation workflows for data imports, configuring pipelines for different scenarios (e.g., real-time data streaming vs. batch processing), and integrating AAS with other Azure services for automation. Advanced questions will focus on optimizing data factory pipelines for performance and fault tolerance.

Focuses on setting up comprehensive monitoring and logging solutions for Azure Analysis Services using Azure Monitor, Log Analytics, and Performance Monitor (PerfMon). Candidates will be tested on setting up alerts, tracking resource usage, and diagnosing performance bottlenecks. Advanced questions will cover end-to-end monitoring solutions for enterprise deployments and setting up proactive monitoring to detect and resolve issues before they impact users.

Covers advanced troubleshooting skills for diagnosing and resolving issues in Azure Analysis Services environments. This includes understanding disaster recovery (DR) and high availability (HA) strategies, such as geo-redundancy, backup and restore, and failover setups. Candidates will be tested on identifying root causes of performance degradation, memory leaks, or system failures, and implementing DR and HA plans for business continuity.