DP-203 Design and implement data storage Practice Question
A logistics company needs to store delivery tracking data that is updated frequently by multiple services. The solution must support transactions across multiple documents and provide real-time analytics. Which Azure service should you recommend?
⚠ Common exam trap
Watch out — candidates often confuse Azure Table Storage's single-entity transactions with multi-document support, or mistakenly think Azure Data Lake Storage Gen2 can handle transactional updates, when it is designed for append-heavy, analytical workloads.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Cosmos DB with SQL API
Azure Cosmos DB with SQL API is the correct choice because it provides multi-document transaction support (ACID within a logical partition) and real-time analytics via its change feed and integrated analytical store. This meets the requirement for frequent updates from multiple services while enabling low-latency reads for analytics, unlike other Azure storage options that lack transactional guarantees across documents or real-time query capabilities.
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 Table Storage
Why it's wrong here
Azure Table Storage is a schemaless key-attribute store with no multi-document transactions and no analytical query capability; its atomic batch operations are limited to a single partition. It would suit cheap high-volume key lookups, not transactional updates across documents combined with real-time analytics.
- ✓
Azure Cosmos DB with SQL API
Why this is correct
Azure Cosmos DB with SQL API satisfies both constraints: multi-document transactions via server-side stored procedures and the transactional batch feature within a single logical partition, plus real-time analytics through the integrated change feed. Its schema-agnostic document model also suits frequently updated tracking records written concurrently by multiple services.
- ✗
Azure Data Lake Storage Gen2
Why it's wrong here
Azure Data Lake Storage Gen2 is object storage organised hierarchically for batch analytics; it offers no transactional writes across documents and no low-latency serving layer. It would be correct as the landing zone for large-scale analytical processing, not as the operational store for frequently updated tracking records.
- ✗
Azure Service Bus
Why it's wrong here
Azure Service Bus is a message broker that decouples producers from consumers; it stores messages transiently in queues or topics and provides no document store, multi-document transactions or analytical query engine. It would be correct for asynchronous command and event distribution between services, not for persisting and querying tracking records.
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