DP-900 Describe core data concepts Practice Question
Which TWO Azure services can be used to perform real-time data ingestion and processing? (Choose two.)
⚠ Common exam trap
Many candidates confuse batch processing services like Azure Data Factory or storage services like Blob Storage with real-time ingestion, forgetting that real-time requires event-driven, low-latency ingestion and processing capabilities.
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 Event Hubs
Azure Event Hubs (B) is correct because it is a fully managed, real-time data ingestion service designed to receive millions of events per second from sources such as applications, IoT devices, and logs, buffering them for downstream stream processing. Azure Stream Analytics (E) is correct because it is a real-time stream processing engine that ingests data from sources like Event Hubs or IoT Hub and runs continuous SQL-like queries over the data to produce immediate insights, making it a core service for real-time processing. Azure SQL Database (A) is a relational database service intended for transactional storage and querying, not real-time event ingestion or stream processing. Azure Blob Storage (C) is scalable object storage for batch and archival data rather than a real-time ingestion/processing engine. Azure Data Factory (D) is a data integration and orchestration service primarily used for scheduled or triggered batch data movement and transformation, not real-time stream processing.
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 database service optimized for OLTP workloads, maintaining ACID transactions and row-based storage. Although it can store streaming results, it does not natively ingest live event streams or perform low-latency stream processing; data must be loaded via separate connectors, which introduces latency and prevents true real-time analytics on the incoming telemetry.
- ✓
Azure Event Hubs
Why this is correct
Azure Event Hubs is a fully managed real-time data ingestion platform that accepts millions of events per second via AMQP, HTTPS, and Kafka protocols. It provides partitioned, ordered event buffering with configurable retention, enabling publishers to send streaming data and consumers (e.g., Stream Analytics, Functions) to process it with sub-second latency. As the entry point for telemetry, it is the foundational service for real-time pipelines.
- ✗
Azure Blob Storage
Why it's wrong here
Azure Blob Storage is an object store for unstructured data, designed for high-capacity, low-cost persistence and batch access. It does not provide event-streaming semantics, pub/sub messaging, or continuous processing; data written to blobs is static until an external process (like Data Lake Analytics or Databricks) runs a separate job to read it. It can serve as an output sink or archive for streaming pipelines, but it is not a solution for performing real-time processing itself.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is a cloud ETL/ELT orchestration service that uses pipelines, activities, and triggers to move and transform data, typically on a scheduled, tumbling-window, or event-driven (manual) basis. Its integration and data-flow activities are designed for batch processing and do not support continuous, per-event stream processing; the service polls or copies in chunks rather than consuming live events with low latency. While it can orchestrate a streaming pipeline's surrounding batch dependencies, it is not the component that performs real-time analysis.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics is a serverless stream-processing engine that executes SQL-like queries against data in motion, directly consuming from Azure Event Hubs, IoT Hub, or Blob Storage (in near-real-time). It supports windowing functions, temporal aggregations, pattern matching, and alerts, then writes results to destinations like Power BI, Azure SQL Database, or Blob Storage. This makes it one of the two core Azure services (with Event Hubs) required to perform real-time data processing.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
Go deeper
Related to this question
Learn chapter
Azure Stream Analytics
Key term
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
Key term
Data ingestion
Data ingestion is the process of moving data from various sources into a storage system where it can be accessed, analyzed, and used.
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Senior Network & Security Engineer · founder of Courseiva
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