AZ-305 Design data storage solutions Practice Question
A company is designing a data storage solution for a globally distributed application that requires low-latency read access to frequently accessed data and high throughput for write operations. The data is non-relational and can be stored as key-value pairs. Which Azure service should they use?
⚠ Common exam trap
A common mix-up: candidates confuse Azure Table Storage as a sufficient key-value store for global scenarios, overlooking its lack of global distribution and guaranteed low-latency SLAs, which Cosmos DB uniquely provides.
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
Azure Cosmos DB is the correct choice because it is a globally distributed, multi-model database service that provides guaranteed low-latency reads (under 10 ms at the 99th percentile) and high throughput for write operations, with automatic indexing and turnkey global distribution. It natively supports key-value data models, making it ideal for non-relational, frequently accessed data requiring consistent performance across regions.
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 NoSQL key-value store that runs in a single Azure region (or a paired region for geo-redundancy) and does not offer turnkey, multi-region write-enabled distribution like Cosmos DB. To serve users globally with low latency, you would need custom replication and failover logic. Its default consistency model is weaker, and it lacks Cosmos DB's single-digit-millisecond latency SLA.
- ✓
Azure Cosmos DB
Why this is correct
Azure Cosmos DB is a multi-model database that provides turnkey global distribution: any number of Azure regions can be associated with the account, and data is automatically replicated so requests are served from the nearest region. It supports key-value APIs, multiple consistency models, and offers 99.999% availability with single-digit-millisecond reads/writes at any scale—exactly what a globally distributed key-value workload needs.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a fully managed relational engine with a rigid schema, requiring tables, columns, and indexes to be defined before storing data. A key-value workload demands schema-agnostic inserts and simple point lookups by a single key; forcing every record into relational rows adds complexity, restricts value flexibility, and requires manual partitioning for horizontal scale. Its geo-replication is primarily for disaster recovery, not for low-latency multi-region key-value reads, and its cost model does not align with high-throughput point-lookup patterns.
- ✗
Azure Blob Storage
Why it's wrong here
Azure Blob Storage is object storage designed for large unstructured files like images, videos, and backups, not for item-level key-value operations. Although blobs are addressed by a key-like URL (container/name), reads return entire blobs and cannot provide the fine-grained, millisecond-latency point lookups typical of key-value databases. It has no query engine or throughput SLA for small record reads, making it technically unsuitable for this global key-value design.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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