DP-900 Describe an analytics workload on Azure Practice Question
A manufacturing company ingests real-time sensor data from assembly line machines into Azure Event Hubs. The company needs to calculate a 5-minute rolling average of temperature readings for each machine and compare it against a static threshold value stored in a CSV file in Azure Blob Storage. If the average exceeds the threshold, an alert must be triggered. Which Azure service should be used for this real-time data processing?
⚠ Common exam trap
Test-takers frequently confuse Azure Stream Analytics with Azure Data Factory or Synapse Analytics, mistakenly thinking that any data processing involving Blob Storage or SQL-like queries must use a batch-oriented service, when in fact Stream Analytics is the only option that natively supports real-time windowed aggregations and reference data joins from Blob Storage.
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
Azure Stream Analytics is the correct choice because it is designed for real-time stream processing, including windowed aggregations like a 5-minute rolling average. It can directly ingest data from Azure Event Hubs, perform the calculation using a TumblingWindow or HoppingWindow function, and reference static data (the threshold CSV) from Azure Blob Storage via a reference data input. If the computed average exceeds the threshold, Stream Analytics can output the alert to a sink like Azure Functions or a notification service.
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 Stream Analytics
Why this is correct
Azure Stream Analytics is the correct choice because it is a fully managed, serverless stream-processing engine built specifically for real-time analytics on high-throughput data from sources like Event Hubs. It natively supports temporal windows (tumbling, hopping, sliding, and session) to compute a 5-minute rolling average with a simple SQL-like query, and it can join the live stream with static reference data held in Blob Storage to compare against a threshold. The service scales automatically, provides exactly-once event delivery, and requires no cluster provisioning or manual code beyond the streaming query.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is wrong because it is an ETL and data-integration service designed for batch-oriented, schedule-driven copy and orchestration of data between on-premises and cloud stores. It does not process events in real time or perform stateful windowed aggregations like a rolling average; at best, it could stage raw sensor data to storage and trigger a batch job, but that introduces latency and misses the requirement for a near-real-time alert. ADF lacks the streaming query engine and temporal functions that Azure Stream Analytics provides for continuously computing aggregations on unbounded data.
- ✗
Azure Synapse Analytics
Why it's wrong here
Azure Synapse Analytics is wrong for this use case because it is a distributed analytics platform centered on large-scale batch and interactive querying over data warehouses and data lakes. While Synapse has Spark pools and can ingest streaming data via Event Hubs or Synapse Link, such setups require explicit configuration for continuous processing and are not optimized for low-latency, 5-minute rolling window computations in a serverless manner. It is designed for complex analytics over stored data, not for event-at-a-time stream processing with reference-data joins from Blob Storage, making it an over-engineered and less-direct fit for the required alerting scenario.
- ✗
Azure HDInsight
Why it's wrong here
HDInsight is a managed Hadoop and Spark cluster service designed for batch and large-scale distributed processing, not for low-latency, event-streaming computations like a 5-minute rolling average. It lacks native integration with Event Hubs for real-time windowed aggregation against a static threshold from Blob Storage; such a task would require custom code and complex cluster management. It is tempting because HDInsight includes Spark Streaming, which can process streaming data, but this scenario demands a serverless, event-driven service like Azure Stream Analytics that directly consumes Event Hubs and references Blob Storage thresholds without provisioning clusters.
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
Data Roles and Core Concepts
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
Key term
Stream processing
Stream processing is a data processing method that continuously analyzes and acts on data in real time as it arrives, rather than storing it first and processing it later.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-900 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 DP-900 exam.