DP-900 Describe an analytics workload on Azure Practice Question
Your company is developing a new analytics solution to track customer sentiment from social media feeds. The data arrives as a continuous stream of JSON messages. The solution must process the data in near real-time, enrich it with customer profile data stored in Azure Cosmos DB, and then store the results in a data lake for historical analysis. The team wants to use a low-code approach for the data processing logic. You are considering the following architectures: A) Use Azure Event Hubs to ingest the stream, Azure Stream Analytics to process and enrich the data using Cosmos DB as a reference data source, and output to Azure Data Lake Storage Gen2. B) Use Azure IoT Hub to ingest the stream, Azure Databricks to process the data, and write to Azure Blob Storage. C) Use Azure Event Hubs to ingest the stream, Azure Functions to process each message, query Cosmos DB for enrichment, and write to Azure Data Lake Storage Gen2. D) Use Azure Event Hubs to ingest the stream, Azure Data Factory to execute a mapping data flow for enrichment, and write to Azure Data Lake Storage Gen2. Which architecture best meets the requirements of near real-time processing, enrichment, and low-code?
⚠ Common exam trap
Candidates often confuse Azure Stream Analytics with Azure Data Factory, assuming both can handle streaming, but Data Factory is batch-only and cannot process a continuous Event Hubs stream in near real-time.
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
✓
Option A
Azure Stream Analytics provides a low-code, SQL-based approach for near real-time processing, and it can natively enrich streaming data by using Azure Cosmos DB as a reference data source via a JOIN operation. The output is directly written to Azure Data Lake Storage Gen2, meeting all requirements without custom code.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Option A
Why this is correct
Azure Stream Analytics is a fully managed, serverless stream-processing engine that provides a low-code, SQL-based query language in the Azure portal. It supports near real-time ingestion from Event Hubs, IoT Hub, and Blob Storage, and can enrich incoming telemetry with reference data, such as product catalogs or device metadata, via simple JOIN operations. Its sub-minute latency and built-in windowing functions make it the ideal fit for a low-code analytics solution that must track and respond to events as they occur without custom application code.
- ✗
Option C
Why it's wrong here
Azure Functions is a serverless compute service that executes code written in C#, Java, JavaScript, PowerShell, or Python when triggered by events such as new messages arriving on Event Hubs. While it can process streaming events with low latency, building and maintaining the transformation logic requires significant custom code, error handling, and dependency management, so it is not a low-code solution. The requirement for near real-time, low-code analytics points away from Azure Functions and toward a purpose-built stream processor like Stream Analytics.
- ✗
Option D
Why it's wrong here
Azure Data Factory's mapping data flows are a visual, no-code transformation tool, but they are inherently designed for batch ETL/ELT workloads, not for processing data in near real time. Mapping data flows run on Spark clusters and are typically orchestrated on a schedule or through tumbling windows, so they introduce inherent latency and are not event-driven. Thus, while Data Factory can be part of an analytics pipeline, its mapping data flows are the wrong choice for a solution that must track events with sub-minute, low-code stream processing.
- ✗
Option B
Why it's wrong here
Azure Databricks is an Apache Spark-based analytics platform that requires users to write Scala, Python, or Spark SQL code and to configure and manage clusters. Although Databricks does support Structured Streaming and can achieve near real-time processing, it is a code-intensive and operationally complex tool, not a low-code solution. For a development team that wants a fast, low-code analytics pipeline, the operational overhead and programming requirements make Databricks a poor fit compared with Azure Stream Analytics.
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
Mapping data flow
Mapping data flow is the process of visually or programmatically defining how data moves from source to destination, including transformations and processing steps, within Azure data services.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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