DP-203 Develop data processing Practice Question
Which THREE factors should you consider when designing a real-time streaming solution using Azure Stream Analytics to process IoT data from thousands of devices?
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
✓
The batch size for output to Azure Synapse Analytics to minimize write transactions.
Batching output to Azure Synapse Analytics reduces write transactions and improves cost efficiency. Option C is correct because windowing functions and late arrival policies are essential for handling out-of-order events in real-time streaming. Option E is correct because streaming units and partitioning determine throughput and scalability for processing IoT data. Option A is incorrect because reference data that changes every few seconds would require frequent reloads, defeating the purpose of static reference data in Stream Analytics. Option D is incorrect because latency requirements for Cosmos DB are not a primary design factor; Stream Analytics can write to Cosmos DB, but the main considerations are throughput and consistency, not latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The need to join input data with reference data that changes every few seconds.
Why it's wrong here
Reference data is static; frequent changes would require a different pattern.
- ✓
The batch size for output to Azure Synapse Analytics to minimize write transactions.
Why this is correct
Batching reduces transaction costs and improves throughput.
- ✓
The windowing function and late arrival policy for handling out-of-order events.
Why this is correct
Windowing and late arrival policies are critical for correct aggregations.
- ✗
The latency requirements for writing output to Azure Cosmos DB for NoSQL.
Why it's wrong here
Although Azure Stream Analytics does support output to Azure Cosmos DB, latency requirements are not a primary design consideration for selecting Cosmos DB as an output; factors like throughput and consistency are more relevant for the streaming job design.
- ✓
The number of streaming units and partitioning scheme for the input Event Hubs.
Why this is correct
Proper partitioning and SU allocation ensure scalability.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
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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JA
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.