Free Microsoft Azure Data Fundamentals DP-900 Exam Questions

Azure SQL Database is a fully managed platform as a service (PaaS) database engine that handles most of the database management functions such as upgrading, patching, backups, and monitoring without user involvement.

SQL Database delivers predictable performance with multiple resource types, service tiers, and compute sizes. It provides dynamic scalability with no downtime, built-in intelligent optimization, global scalability and availability, and advanced security options. These capabilities allow you to focus on rapid app development and accelerating your time-to-market, rather than on managing virtual machines and infrastructure.

https://docs.microsoft.com/en-us/azure/azure-sql/database/sql-database-paas-overview

Azure Data Factory is a cloud-based ETL and data integration service used to create data ingestion pipelines. It enables you to build, schedule, and orchestrate data workflows that extract data from various sources, transform it, and load it into target destinations, with visual pipeline design and monitoring capabilities.

When performing diagnostic analytics, three key actions used are: Drill-down (exploring data at greater granularity to understand causes), Correlation analysis (identifying relationships between variables), and Comparison (comparing datasets to identify differences). These actions help answer 'why' questions by investigating relationships and patterns in historical data.

Three Azure services that support Apache Spark are: (1) Azure Synapse Analytics - integrates Spark pools for big data processing, (2) Azure Databricks - a fully managed Spark platform with collaborative notebooks, and (3) HDInsight - Azure's managed Hadoop and Spark cluster service. All three enable distributed data processing using the Apache Spark framework.

https://azure.microsoft.com/en-us/services/sql-database/#features

Atomicity is the ACID property that guarantees each transaction is treated as a single, indivisible unit of work. It ensures that either all operations within a transaction complete successfully, or if any operation fails, the entire transaction is rolled back with no partial changes applied to the database. This all-or-nothing approach is fundamental to maintaining data consistency.

The Gremlin API in Azure Cosmos DB is specifically designed for graph database scenarios. It allows you to store and query graph data structures with nodes, edges, and properties, making it ideal for applications that need to model relationships and network data.

https://docs.microsoft.com/en-us/azure/architecture/data-guide/relational-data/online-transaction-processing

https://www.sqlshack.com/start-your-journey-with-azure-cosmos-db/

https://docs.microsoft.com/en-us/azure/postgresql/overview-postgres-choose-server-options

https://docs.databricks.com/data/data-sources/azure/synapse-analytics.html

Azure Cosmos DB is a globally distributed, non-relational database service that provides guaranteed latency of less than 10 milliseconds for both reads and writes at the 99th percentile. It is specifically designed for applications requiring ultra-low latency and high throughput, with automatic scaling and multi-region replication.

A key/value store associates each data value with a unique key.

An application can store arbitrary data as a set of values. Any schema information must be provided by the application. The key/value store simply retrieves or stores the value by key.

https://docs.microsoft.com/en-us/azure/architecture/guide/technology-choices/data-store-overview

https://docs.microsoft.com/en-us/power-bi/consumer/end-user-dashboards

https://docs.microsoft.com/en-us/power-bi/visuals/power-bi-visualization-kpi

https://docs.microsoft.com/en-us/power-bi/consumer/end-user-drill