AZ-305 Design data storage solutions Practice Question
A company is designing a data storage solution for an IoT pipeline that ingests time-series data from millions of devices. The data is append-only and queried by time range. The solution must support low-latency queries and automated retention policies. Which Azure data store should they choose?
⚠ Common exam trap
Watch out — candidates often confuse Cosmos DB's low-latency individual item access with the need for time-series range queries, overlooking that Cosmos DB lacks native time-series indexing and automated retention policies, while ADX is the only Azure service explicitly designed for high-throughput append-only time-series analytics with built-in lifecycle management.
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 Data Explorer (ADX)
Azure Data Explorer (ADX) is purpose-built for interactive analytics on large volumes of streaming, time-series data. It supports append-only ingestion, low-latency queries over time ranges via its Kusto Query Language (KQL), and native automated retention policies (e.g., soft-delete and hard-delete periods) without manual management.
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 SQL Database
Why it's wrong here
Azure SQL Database is a relational row-store system built for ACID transactions and structured application queries, not for continuous high-volume append-only IoT data. Ingesting millions of device telemetry events would require expensive per-row inserts, create index maintenance overhead, and force a rigid schema that conflicts with evolving payloads. Its query engine, while powerful, lacks the native time-series data partitioning and columnar compression that telemetry workloads need.
- ✗
Azure Cosmos DB
Why it's wrong here
Azure Cosmos DB is a globally distributed multi-model database tuned for real-time transactional workloads, offering flexible schemas and low-latency reads/writes, but it treats each document write as a Request Unit (RU) charge, making massive IoT append streams cost-prohibitive at scale. Its indexing over JSON documents and lock-free writes are not optimized for range scans and aggregations over millions of time-stamped records, which is the core pattern of IoT analytics. While it can store telemetry, it is not a purpose-built time-series engine.
- ✓
Azure Data Explorer (ADX)
Why this is correct
Azure Data Explorer (ADX) is a columnar analytics engine purpose-built for time-series and IoT data, ingesting high-velocity telemetry from Event Hubs and IoT Hub at gigabytes per second with low latency. It automatically compresses and indexes columns, uses Kusto Query Language (KQL) for time-based aggregates, filters, and pattern detection, and stores data in immutable, sharded extents that enable fast scans and retention policies. This is the correct choice for interactive, near-real-time diagnostics and analytics over append-only sensor streams.
- ✗
Azure Blob Storage with Azure Data Lake Storage Gen2
Why it's wrong here
Azure Blob Storage with Azure Data Lake Storage Gen2 provides a secure, tiered, and highly durable data lake for batch and big-data analytics, but it offers no native indexing or query engine for low-latency interactive queries. To analyze IoT telemetry stored here, you must orchestrate on-demand compute like Azure Synapse or Databricks, which adds cold-start overhead and decouples ingestion from querying. ADX, by contrast, combines storage and querying in one system optimized for time-series.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AZ-305 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AZ-305 exam.