DP-900 Describe core data concepts Practice Question
You are designing a data pipeline for a social media analytics platform. The pipeline needs to ingest posts from multiple sources (Twitter, Facebook) in real time, transform the data by adding sentiment scores, and store the results in a data store for later analysis. The transformation logic is simple and can be expressed as a SQL query. You want to minimize coding effort. Which Azure service should you use for the transformation step?
⚠ Common exam trap
Candidates often confuse Azure Data Factory (batch ETL) with real-time stream processing, or assume Azure Functions is simpler for SQL-like transformations, but Stream Analytics is the only service that combines real-time ingestion, SQL-based transformation, and minimal coding effort.
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 data processing with SQL-like query language, allowing you to transform streaming data (e.g., from Twitter and Facebook) by adding sentiment scores using simple SQL expressions without writing custom code. It integrates natively with Azure Event Hubs or IoT Hub for ingestion and outputs to Azure SQL Database, Cosmos DB, or Blob Storage for analysis, minimizing coding effort.
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 Data Factory
Why it's wrong here
Azure Data Factory is a cloud-based ETL service focused on orchestrating scheduled, batch data movement and transformation through pipelines. It can copy data from many sources to many sinks, but it does not process individual streaming events in real time; instead, it typically runs on a time-based trigger (e.g., every 15 minutes) or file-arrival event. Although ADF can invoke external compute like Azure Databricks to process near-real-time data, its native data-flow transformation engine is not designed for continuous SQL queries over live social media feeds.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks is an Apache Spark-based unified analytics platform suited for large-scale data engineering, data science, and machine learning workloads. To achieve streaming, you would have to write Spark Structured Streaming queries in Scala, Python, or Spark SQL, then provision and manage clusters with appropriate worker configurations—far more complexity than a simple social media metric pipeline requires. While it is powerful and flexible, using it solely for straightforward SQL transformations over a stream is overkill and introduces operational overhead, cluster scaling costs, and debugging latency.
- ✗
Azure Functions
Why it's wrong here
Azure Functions is a serverless compute service that executes event-triggered code in languages such as C#, Java, Python, or PowerShell. To handle a streaming social media feed, you would attach a Function to an Event Hubs trigger and write custom imperative code to parse, filter, and aggregate each payload, including manual state management for time-windowed operations. Functions do not expose a SQL query interface, so the solution would be inherently code-first, not low-code, and would shift the burden of exactly-once semantics and windowing to the developer.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics is a fully managed stream-processing service that queries live data using a SQL-like language without requiring custom code. It reads from high-throughput sources such as Event Hubs or IoT Hub, applies temporal windows, filters, joins, and aggregates, and writes results to Power BI, Azure SQL, Cosmos DB, or Data Lake Storage. Its declarative model and built-in time handling make it the natural choice for low-latency social media analytics, letting you continuously compute metrics like mentions, sentiment, or trending hashtags in near-real time.
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Related to this question
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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
IoT Hub
IoT Hub is a managed cloud service in Azure that acts as a central message hub for secure, bidirectional communication between IoT devices and the cloud.
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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.