AZ-305 Design infrastructure solutions Practice Question
Your company has a large number of IoT devices sending telemetry to Azure IoT Hub. The data must be processed in near real-time to detect anomalies and trigger alerts. Additionally, the processed data must be stored in a time-series database for historical analysis. Which combination of Azure services should you recommend?
⚠ Common exam trap
Many candidates confuse Azure Stream Analytics with Azure Data Factory or Azure Functions, mistakenly thinking batch or serverless compute can handle near real-time stream processing, while overlooking that Azure Data Explorer is the only Azure service purpose-built for time-series storage and analytics 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 Stream Analytics and Azure Data Explorer
Azure Stream Analytics provides real-time stream processing to detect anomalies and trigger alerts from IoT Hub telemetry. Azure Data Explorer (ADX) is a fully managed, high-performance time-series database optimized for storing and analyzing large volumes of time-stamped data, making it the ideal choice for historical analysis. Together, they meet both the near real-time processing and time-series storage requirements.
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 Functions and Azure SQL Database
Why it's wrong here
Azure Functions is an event-driven, serverless compute service, not a streaming ingest or analytics engine; it lacks native support for high-throughput, sustained telemetry streams, and its concurrency and checkpointing model is unsuited for continuous, late-arriving IoT data. Azure SQL Database is an OLTP relational store optimized for row-based transactions, not for append-only, high-cardinality time-series data; time-range aggregations at IoT scale suffer from high latency and require heavy indexing that still underperforms a purpose-built time-series store. Together they are a poor fit for real-time telemetry processing and storage.
- ✗
Azure HDInsight and Azure Cosmos DB
Why it's wrong here
Azure HDInsight is a managed Hadoop/Spark cluster designed primarily for batch processing and complex ETL jobs; while Spark Structured Streaming exists, it requires infrastructure management, cluster sizing, and tuning for sub-second latency, making it far less turnkey than a fully managed stream processor. Azure Cosmos DB is a multi-model NoSQL database with global distribution and flexible schemas, but it lacks time-series-specific optimizations such as automatic time-based partitioning, downsampling, and native time-series query functions; its request-unit (RU) billing also makes high-volume, append-only telemetry ingestion cost-prohibitive. This combination is not optimal for a high-throughput IoT telemetry solution.
- ✗
Azure Data Factory and Azure Blob Storage
Why it's wrong here
Azure Data Factory (ADF) is a cloud ETL/ELT orchestration service that moves and transforms data on a schedule, not a real-time stream-processing engine; it cannot continuously ingest or analyze IoT telemetry with low latency, as it relies on batch-oriented pipelines and lacks windowing, temporal queries, and event-driven streaming semantics. Azure Blob Storage is a scalable object store suitable for landing raw files, but it does not provide interactive time-series query capabilities or built-in analytics without a separate compute engine like Synapse or Data Explorer. The batch latency introduced by ADF makes this pair unsuitable for real-time IoT analytics.
- ✓
Azure Stream Analytics and Azure Data Explorer
Why this is correct
Azure Stream Analytics is a fully managed, serverless stream-processing engine that uses SQL-like syntax to run continuous queries over IoT Hub or Event Hubs data, enabling real-time filtering, windowed aggregations, and pattern detection. Azure Data Explorer (ADX) is a fast, fully managed analytics database specifically optimized for time-series and telemetry data, with native time-based partitioning, high-cardinality grouping, and built-in time-series functions (e.g., series_decompose) for trend and anomaly analysis. This combination is ideal for IoT telemetry: Stream Analytics handles the real-time processing and ingestion while ADX provides low-latency, interactive storage and exploration, making it the correct answer.
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