DP-203 Develop data processing Practice Question
You are designing a data processing solution for an e-commerce company. The company receives millions of clickstream events per hour from their website and needs to aggregate the data by product category and windowed time intervals for real-time dashboards. You need to minimize latency and cost. Which service should you use?
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 best choice because it is purpose-built for real-time stream processing, supports windowed aggregations, and integrates with Power BI for dashboards. Option A (Azure Databricks Structured Streaming) can handle streaming but is more complex and typically more expensive for simple aggregations. Option B (Azure Data Factory) is for batch data movement, not real-time. Option D (Azure Synapse Pipelines) is for orchestrating data movement, not real-time 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 Databricks Structured Streaming
Why it's wrong here
Azure Databricks Structured Streaming introduces per-micro-batch overhead and requires a running cluster with compute costs even during idle periods, which conflicts with the need to minimise latency and cost for high-volume clickstream aggregation. It is tempting because it excels at complex transformations and stateful aggregations across multiple data sources, making it ideal for scenarios requiring advanced analytics or machine learning on streaming data.
- ✗
Azure Data Factory
Why it's wrong here
Data Factory performs batch orchestration on schedules or triggers, so it cannot aggregate millions of events per hour into windowed intervals with low latency. It suits scheduled ETL between stores; Stream Analytics queries Event Hubs with windowing functions for continuous real-time output.
- ✓
Azure Stream Analytics
Why this is correct
Stream Analytics performs windowed, stateful aggregation over streaming input with built-in tumbling, hopping and sliding windows, aggregating clickstream events by product category in near real time. Its consumption-based pricing keeps cost low for continuous high-volume dashboards.
- ✗
Azure Synapse Pipelines
Why it's wrong here
Synapse Pipelines orchestrate batch movement and transformation, not continuous windowed aggregation over streaming events; they add scheduling latency and compute cost per run. Stream Analytics or Spark Structured Streaming handles tumbling and sliding windows over Event Hubs for real-time dashboards.
Go deeper
Related to this question
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Orchestrate Data Movement and Transformation
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.
Key term
Azure Databricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-203 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-203 exam.