DP-900 Describe core data concepts Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Azure Cosmos DB for transactions; Azure SQL Database for analytics

  Why it's wrong here

  Azure Cosmos DB is a multi-model NoSQL database that supports globally distributed, low-latency access, but it does not guarantee ACID transactions across partitions in the same way a relational OLTP engine does, making it a poor fit for an e-commerce transaction system that demands atomic updates. Azure SQL Database, while ACID-compliant and capable of running columnstore indexes, is primarily designed for online transaction processing (OLTP), not for the large-scale, complex analytical queries typical of an enterprise data warehouse. Thus the transaction half is insufficiently reliable and the analytics half lacks the massively parallel processing scale needed.

• ✓

  Azure SQL Database for transactions; Azure Synapse Analytics for analytics

  Why this is correct

  Azure SQL Database is a fully managed relational database engine that provides built-in features such as automatic backups, high availability, and strict ACID transaction guarantees, making it ideal for capturing e-commerce orders, inventory, and payments. Azure Synapse Analytics is a limitless analytics service that separates storage from compute and uses a massively parallel processing (MPP) architecture to run complex queries over trillions of rows, with built-in integration for data lakes, pipelines, and Power BI. This combination cleanly separates the operational and analytical layers, letting each service optimize for its own workload.

• ✗

  Azure Blob Storage for transactions; Azure Data Lake Storage for analytics

  Why it's wrong here

  Azure Blob Storage is an object store that offers virtually unlimited, cost-effective storage for unstructured files, but it provides no relational schema, no indexing for typical transactional queries, and no support for atomic multi-row operations, so it cannot replace a transactional database. Azure Data Lake Storage Gen2 is a hierarchical namespace built on Blob Storage; it is a storage layer designed to hold enormous volumes of raw and curated data, not an analytical query engine. You must layer a service such as Azure Synapse Analytics, Databricks, or HDInsight on top of it to run analytics, so pairing it as the analytics component without such an engine is incomplete.

• ✗

  Azure Database for MySQL for transactions; Azure Analysis Services for analytics

  Why it's wrong here

  Azure Database for MySQL is a solid, ACID-compliant relational database suitable for OLTP workloads, so the first half of this pairing is not inherently flawed. Azure Analysis Services, however, is a semantic modeling and in-memory engine that lets you build tabular or multidimensional models and perform fast DAX-based queries; it is not a primary analytical query service or data warehouse and typically requires another source to load data from. For cloud-scale analytics on an e-commerce platform, Microsoft's recommended and primary service is Azure Synapse Analytics, making this an outdated or secondary option.

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