AZ-305 Designing Microsoft Azure Infrastructure Solutions Exam

Recommend a solution for data integration

Data integration is the process of combining data from multiple sources into a unified view. In Azure, Azure Data Factory (ADF) often serves as the core orchestration service for data integration. It provides pipeline capabilities to move and transform data at scale across on-premises and cloud systems. When recommending a solution, consider latency, volume, and data formats to choose the right tools.

Integration patterns typically fall into two categories: batch and real-time. Batch integration uses scheduled Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) pipelines to move large data sets at regular intervals. Real-time integration relies on streaming services to process data continuously. Key streaming tools include:

• Azure Event Hubs for high-throughput data ingestion
• Azure Service Bus for reliable messaging
• Azure Stream Analytics for in-flight data transformation

When designing pipelines, focus on security, scalability, and monitoring. Use Managed Virtual Networks and Private Endpoints to secure data flows. Enable Azure Monitor and Log Analytics to track pipeline health, data drift, and performance metrics. For high data volumes, scale Integration Runtime nodes or use Data Flow compute optimizations.

Finally, integration often requires transformations and cleansing. Mapping Data Flows in ADF or Azure Synapse Pipelines let you design graphical transformations without writing code. For code-based processing, consider Azure Databricks with Spark or Azure Functions for lightweight micro-batch tasks. Always balance cost and performance when selecting compute options for data transformations.

Recommend a solution for data analysis

Data analysis turns raw data into actionable insights through queries, reports, and dashboards. In Azure, Azure Synapse Analytics provides a unified platform combining data warehousing, big data, and integration. It offers SQL pools for structured queries and Apache Spark pools for advanced analytics and machine learning. This flexibility makes Synapse a strong choice for centralized analysis workloads.

For interactive querying and reporting, consider Azure Analysis Services or Azure SQL Data Warehouse (now Synapse SQL Data Warehouse). These services support OLAP to accelerate multidimensional queries. Key features include:

• Columnstore indexes for fast analytics on large data sets
• Materialized views to precompute aggregates
• Dynamic scaling to match query workloads

Real-time analytics often require processing streaming data and feeding insights to dashboards. Azure Stream Analytics can ingest events, apply window functions, and output to Power BI or databases. Alternatively, Azure Databricks offers notebook-based analysis with Python, Scala, or SQL, plus built-in machine learning libraries. Choose the tool that matches your team’s expertise and the complexity of streaming queries.

Once data is processed and stored, visualization is the final step. Power BI integrates natively with Synapse, Azure SQL, and Analysis Services to create interactive dashboards. For self-service analytics, enable row-level security and data sensitivity labels to control access. By combining robust storage, compute, and visualization, your recommended solution will support scalable and governed data analysis across the organization.

Conclusion

In designing data integration and analysis solutions on Azure, start by understanding data movement patterns and processing requirements. Use Azure Data Factory and Azure Synapse Pipelines for scalable batch and real-time integration. For data analysis, leverage Azure Synapse Analytics, Analysis Services, and Power BI to deliver fast, secure, and interactive insights. By aligning your recommendations with volume, latency, and security needs, you will craft architectures that meet both operational and analytical goals.