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Describe an analytics workload on AzurehardMultiple ChoiceObjective-mapped

DP-900 Describe an analytics workload on Azure Practice Question

A manufacturing company collects sensor data from thousands of IoT devices. The data arrives as a stream of time-stamped readings with a fixed schema (DeviceID, Timestamp, Temperature, Pressure, Vibration). They need to store this data and support both real-time dashboards showing the last hour of data and complex analytical queries over years of historical data. The solution must minimize storage costs and provide sub-second response for real-time queries. Which Azure service is best suited for this workload?

⚠ Common exam trap

Microsoft often tests the misconception that any database with low-latency reads (like Cosmos DB) can handle both real-time and historical analytics, but the trap is that Cosmos DB lacks the columnar storage and query engine optimized for time-series aggregations, making it cost-prohibitive and slow for complex analytical queries over years of data.

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 purpose-built for high-performance analysis of large volumes of streaming telemetry data. It supports ingestion from IoT hubs, automatic indexing for sub-second queries on recent data (e.g., last hour), and cost-effective long-term storage via hot/cold tiering for years of historical analytics. Its columnar storage and Kusto Query Language (KQL) are optimized for time-series and aggregation queries, making it ideal for this mixed real-time and historical workload.

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 Cosmos DB with SQL API

    Why it's wrong here

    While Cosmos DB provides low-latency access and global distribution, it is not optimized for time-series analytical workloads. Storing billions of sensor readings would be very expensive and query performance for complex aggregations is not as efficient as a purpose-built time-series solution.

  • Azure SQL Database

    Why it's wrong here

    Azure SQL Database is a relational database that can handle time-series data but is not designed for high-velocity ingestion (millions of records per second) and its indexing strategies may not support sub-second queries on recent data while also allowing efficient ad-hoc analytics on historical data.

  • Azure Data Explorer

    Why this is correct

    Azure Data Explorer is a fully managed, high-performance analytics database purpose-built for large volumes of time-series and log data. It uses columnar storage and automatic indexing to ingest millions of events per second, while a hot cache in memory enables sub-second queries on recent data and cold storage tiers automatically for cost-effective historical analysis. Its Kusto Query Language (KQL) provides native time-window functions (e.g., bin and series_stats), making complex aggregations over billions of sensor reads both efficient and straightforward, which is exactly why this is the correct option.

  • Azure Table Storage

    Why it's wrong here

    Azure Table Storage is a key-value store that can store large amounts of structured data, but it lacks the ability to run complex analytical queries (aggregations, time-window functions) efficiently. It is not suitable for this workload.

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