AZ-305 Design data storage solutions Practice Question
A company manages a fleet of millions of IoT devices that send telemetry data every minute. The data must be stored for 10 years to meet compliance requirements. For the first 30 days, data is accessed frequently for real-time dashboards and alerting. After 30 days, data is only accessed occasionally for historical analysis and reporting. The solution must be cost-effective and support high ingestion rates. Which Azure service should the company use to store and query this data?
⚠ Common exam trap
Test-takers frequently choose Azure Blob Storage or Cosmos DB because they associate them with 'storage' or 'IoT,' but they fail to recognize that Azure Data Explorer is the only service purpose-built for high-velocity time-series analytics with built-in hot/cold tiering and native support for real-time alerting and long-term retention at scale.
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
Azure Data Explorer (ADX) is designed for high-ingestion, time-series telemetry data and supports real-time dashboards and alerting on fresh data, while also providing cost-effective long-term storage for historical queries. Its columnar storage and indexing enable fast analytics on billions of records, making it ideal for IoT scenarios with millions of devices sending data every minute and a 10-year retention requirement.
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 Blob Storage with Azure Data Lake Storage Gen2
Why it's wrong here
Blob Storage with ADLS Gen2 is a valid, cost-effective landing zone for raw IoT telemetry, offering massive scale and lifecycle management. However, it is an object store, not a query engine: it has no native time-series indexing, partitioning, or retention-aware query semantics, and no built-in KQL/SQL-style analytics. Any exploration requires an external service such as Azure Data Explorer, Databricks, or Synapse to read the files, adding latency and operational complexity that conflicts with real-time and interactive historical analysis.
- ✓
Azure Data Explorer
Why this is correct
Azure Data Explorer is the only service here built specifically for high-fidelity time-series analytics: its columnar engine ingests millions of events per second, automatically creates inverted indexes, and uses a hot/cold cache with data tiering to balance performance and cost. KQL natively supports time-based operations such as bin(), summarize, anomaly detection, and lag/lead calculations, which can run on both streaming and historical data. This makes it the appropriate choice for a fleet of millions of devices where real-time visibility and long-term retention are required.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database uses a row-oriented, transactional engine with B-tree indexes that are optimized for OLTP workflows, not the compound-write, append-heavy pattern of IoT telemetry. Sustained ingestion from millions of devices would force heavy DTU/vCore consumption and frequent index maintenance, while lookups by time range become expensive because there is no native time-series partitioning or compression. The required throughput would require massive over-provisioning, making it cost-prohibitive for this workload.
- ✗
Azure Cosmos DB with SQL API
Why it's wrong here
Cosmos DB is an excellent globally distributed multi-model database, but its SQL API charges request units (RUs) per operation, so the constant high-velocity writes from millions of devices translate into enormous, predictable RU expenditure. Its index is document-centric and does not provide columnar compression or time-series aggregation functions, so typical telemetry queries like 'average temperature per hour over 30 days' require client-side aggregation or expensive scans. Additionally, Cosmos DB struggles with long-range, scan-heavy analytical queries, making it a poor fit for this scenario.
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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.