DP-900 Describe an analytics workload on Azure Practice Question
A retail chain needs to blend two data sources for a near real-time dashboard: daily batch files from store systems (CSV files on Azure Blob Storage updated once per day) and live web clickstream data from Azure Event Hubs. The dashboard must refresh every 5 minutes with combined data. Which combination of Azure services should be used to ingest and process both data types most efficiently?
⚠ Common exam trap
Many exam-takers assume Power BI alone can handle both batch and streaming ingestion, but it lacks native batch file ingestion from Blob Storage and requires a separate processing service like Stream Analytics for real-time 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
✓
C) Azure Synapse Pipelines + Azure Stream Analytics
Azure Synapse Pipelines can orchestrate the daily batch CSV files from Azure Blob Storage, while Azure Stream Analytics processes the live web clickstream data from Azure Event Hubs in near real-time. Together, they enable a combined data pipeline that refreshes every 5 minutes, meeting the dashboard's latency requirement efficiently.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A) Azure Data Factory + Azure Analysis Services
Why it's wrong here
Azure Data Factory (ADF) is an orchestration and ETL service optimized for batch/scheduled data movement from stores like Blob Storage; it does not natively consume Event Hubs streams with sub-minute latency. Azure Analysis Services (AAS) is an enterprise OLAP semantic modeling engine that queries already-prepared data, so it cannot ingest or blend streaming and batch payloads itself. Even if ADF loaded CSV batches into an Azure SQL Database, there would still be no low-latency pipeline for event streams, making this pair insufficient for a near real-time blended dashboard.
- ✗
B) Azure Stream Analytics + Power BI
Why it's wrong here
Azure Stream Analytics excels at continuous SQL queries over live Event Hubs/IoT Hub data, but it is not designed to read or transform periodic CSV file dumps from Blob Storage as a scheduled batch integration. Power BI is a visualization and reporting platform, not an enterprise data integration or ingestion service, so it cannot perform the blending logic or orchestrate the two pipelines. Finally, this pairing lacks a persistent unified data store where batch and streaming data are landed and joined, forcing an ad-hoc architecture that fails the 'blend two data sources' requirement.
- ✓
C) Azure Synapse Pipelines + Azure Stream Analytics
Why this is correct
This combination directly covers both sides: Azure Synapse Pipelines can copy and transform the batch CSV files from Blob Storage into Azure Synapse SQL, while Azure Stream Analytics consumes real-time data from Event Hubs and writes it to the same Synapse SQL table or staging store via its Synapse Analytics output. Once both datasets land in Synapse, T-SQL queries can join the historical batch data with the near real-time streaming data, and Synapse's built-in dashboards (or Power BI) can refresh close to live. This separates orchestration and stream processing responsibilities cleanly, making it the only option that provides both a managed batch ingestion path and a managed event-processing path feeding one query surface.
- ✗
D) Azure Databricks + Azure Data Lake Storage
Why it's wrong here
While Azure Databricks supports both batch notebooks and Structured Streaming for event data, it is a general-purpose managed Spark platform that requires you to provision clusters, pay for uptime, and write/maintain notebook code; it does not include a fully managed dashboard service or a native integration to instantly publish a refreshable near real-time dashboard to business users. Azure Data Lake Storage is only a highly scalable object store, so combining these two gives you raw storage and processing power but leaves the orchestration, scheduled file ingestion, and visualization/dashboard refresh to be built separately. For a retail scenario that simply needs to blend CSV dumps with Event Hubs events for a dashboard, this pair is far more operational overhead and still missing the turn-key dashboard capability the question implies.
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
Blob storage
Blob storage is a cloud service for storing large amounts of unstructured data, such as text or binary data, like documents, images, and videos.
Key term
Azure Stream Analytics
Azure Stream Analytics is a fully managed, real-time data processing service that analyzes and transforms high volumes of streaming data from various sources to deliver low-latency insights and trigger actions.
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