A company runs a high-throughput time-series workload that stores sensor data from IoT devices. Data is ingested at a rate of millions of events per second. The application requires low-latency queries (under 100 ms) on recent data (less than 30 days old) and the ability to run occasional analytical queries on historical data older than 30 days. The solution must automatically move cold data to a cost-optimized tier and provide built-in analytics. Which Azure service should they use?

Azure Cosmos DB with analytical store

Azure Cosmos DB is a general-purpose NoSQL database that can handle time-series but is less optimized for high-ingestion rates and low-latency time-series queries, and may have higher cost.

Azure Event Hubs with Azure Data Lake Storage

Event Hubs is a data ingestion service, not a queryable storage service. Data Lake Storage provides cost-effective storage but does not offer low-latency warm queries or built-in time-series analytics.

Azure SQL Database with elastic query

Azure SQL Database is a relational database and is not designed for high-ingestion time-series data or automatic cold tiering of historical data.

What does this AZ-305 question test?

Read the scenario before looking for a memorised answer.

What is the correct answer to this question?

The correct answer is: Azure Time Series Insights (TSI) — Azure Time Series Insights (TSI) is a fully managed time-series data service designed for high-ingestion rates, low-latency queries on warm data, and automatic data archiving to cold storage with cost optimization. TSI also includes built-in analytics for time-series data. Azure Cosmos DB with analytical store can support time-series but has higher latency and cost for warm queries. Event Hubs is for ingestion only. Azure SQL Database with elastic query is not optimized for time-series workloads.

What should I do if I get this AZ-305 question wrong?

Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.