DP-900 Describe core data concepts Practice Question
A retail company captures real-time sensor data from IoT devices to detect anomalies and predict equipment failures. The data must be processed immediately as it arrives. Which type of data processing workload best describes this scenario?
⚠ Common exam trap
Microsoft often tests the distinction between batch and streaming by describing a scenario with 'immediate' or 'real-time' requirements, and candidates mistakenly choose batch processing because they overlook the latency constraint.
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
✓
Streaming processing
B is correct because streaming processing is designed for continuous, real-time data ingestion and immediate analysis, which matches the requirement to process sensor data as it arrives. Technologies like Azure Stream Analytics or Apache Kafka enable low-latency processing of IoT data streams to detect anomalies and predict failures without batching.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Batch processing
Why it's wrong here
Batch processing is wrong for this use case because it collects and stores data for a predefined interval (for example, hourly, nightly, or after a threshold) before running a processing job. This introduces latency that may be seconds, minutes, or hours, making it impossible to react to sensor anomalies in real time. Even if the batch interval is short, it does not provide a continuous event stream, so it cannot meet the requirement for immediate analysis and alerting.
When this WOULD be correct
A question where a company collects daily sales data from stores and runs end-of-day reports to analyze trends and inventory needs. The data is processed in scheduled batches, not in real time.
- ✓
Streaming processing
Why this is correct
Streaming processing is the correct choice because it ingests and analyzes data continuously as it arrives, rather than waiting for a complete dataset. For real-time IoT sensor feeds, services like Azure Stream Analytics can process event streams with sub-second latency, applying time-windowed aggregations, filters, and anomaly detection logic to trigger immediate alerts. This supports proactive failure prediction and operational monitoring, which is impossible with store-then-process approaches.
- ✗
Online transaction processing (OLTP)
Why it's wrong here
OLTP is incorrect because it is optimized for high-volume, high-concurrency transactional workloads such as order capture, inventory updates, or bank transfers, where ACID guarantees and row-level integrity are paramount. Processing IoT sensor streams requires low-latency, continuous analytics over time-ordered events, not discrete read/write transactions with record locking. An OLTP database would struggle with the unbounded, append-only nature of streaming data and would not offer the windowed aggregations or pattern detection needed here.
When this WOULD be correct
A question describing a retail company's point-of-sale system that must record each customer purchase immediately and reliably, ensuring data integrity for inventory updates and financial records.
- ✗
Data warehousing
Why it's wrong here
Data warehousing is not suitable here because it is designed for storing and analyzing structured, historical data aggregated over time, not for acting on live telemetry. A warehouse typically requires ETL/ELT batch loads, schema-on-write modeling, and dimensional star schemas, all of which introduce minutes-to-hours of delay. While it can hold IoT data for long-term reporting, it cannot provide the immediate anomaly detection and real-time response the scenario demands.
When this WOULD be correct
A company needs to consolidate sales data from multiple stores over the past year for trend analysis and reporting. The data is loaded periodically and queried for business intelligence.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Streaming processingCorrect answer▾
Why this is correct
Streaming processing is the correct choice because it ingests and analyzes data continuously as it arrives, rather than waiting for a complete dataset. For real-time IoT sensor feeds, services like Azure Stream Analytics can process event streams with sub-second latency, applying time-windowed aggregations, filters, and anomaly detection logic to trigger immediate alerts. This supports proactive failure prediction and operational monitoring, which is impossible with store-then-process approaches.
✗Batch processingWrong answer — click to see why▾
Why this is wrong here
Batch processing processes data in large, scheduled chunks, not immediately as it arrives. The scenario requires real-time processing of sensor data for immediate anomaly detection, which batch processing cannot provide.
★ When this WOULD be the correct answer
A question where a company collects daily sales data from stores and runs end-of-day reports to analyze trends and inventory needs. The data is processed in scheduled batches, not in real time.
Why candidates choose this
Candidates may confuse batch processing with any data processing that involves large volumes of data, overlooking the real-time requirement in the scenario.
✗Online transaction processing (OLTP)Wrong answer — click to see why▾
Why this is wrong here
OLTP is designed for managing transactional data (e.g., order processing) with ACID guarantees, not for real-time processing of continuous sensor data streams for anomaly detection.
★ When this WOULD be the correct answer
A question describing a retail company's point-of-sale system that must record each customer purchase immediately and reliably, ensuring data integrity for inventory updates and financial records.
Why candidates choose this
Candidates may confuse 'real-time' with 'online' processing, assuming OLTP handles immediate data, but OLTP focuses on transactions, not streaming analytics.
✗Data warehousingWrong answer — click to see why▾
Why this is wrong here
Data warehousing is designed for storing and analyzing historical, structured data from multiple sources, not for processing real-time streaming data from IoT devices.
★ When this WOULD be the correct answer
A company needs to consolidate sales data from multiple stores over the past year for trend analysis and reporting. The data is loaded periodically and queried for business intelligence.
Why candidates choose this
Candidates may associate data warehousing with analytics and reporting, mistakenly thinking it can handle real-time data processing.
Analysis generated from the official DP-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
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
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.