DP-900 Describe an analytics workload on Azure Practice Question
A retail company analyzes customer purchase patterns. Every night, they run a batch job that aggregates millions of transactions from the past day into summary tables for reporting. Which type of data processing workload best describes this nightly job?
⚠ Common exam trap
A common mix-up: candidates confuse 'batch processing' with 'transactional processing' because both involve databases, but batch processing is designed for high-volume, scheduled analytics (OLAP), not for real-time, row-by-row operations (OLTP).
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
✓
Batch processing
This nightly job processes a large volume of transactions accumulated over the past day in a single, scheduled run, which is the defining characteristic of batch processing. In Azure, this workload would typically be implemented using Azure Synapse Analytics or Azure Data Factory to orchestrate the aggregation of millions of rows into summary tables for reporting, without requiring immediate output.
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 this is correct
Batch processing is the correct fit because the nightly job processes a large, accumulated volume of purchase records at a scheduled time, using a bounded dataset. This aligns with the classic batch pattern of ingesting data over a period, then running a job (e.g., Azure Data Factory or Spark) to aggregate and analyze patterns offline without requiring sub-second latency.
- ✗
Real-time processing
Why it's wrong here
Real-time processing is incorrect because it would imply analyzing each purchase event the moment it occurs, with latency in milliseconds or seconds, to support immediate decisions. The described nightly batch runs on a fixed schedule and handles data that has already accumulated during the day, so it is not real-time; it is a classic example of deferred, scheduled processing rather than immediate, continuous handling.
When this WOULD be correct
A question describing a system that monitors credit card transactions for fraud and must flag suspicious activity within milliseconds would make real-time processing correct.
- ✗
Streaming processing
Why it's wrong here
Streaming processing is incorrect because it handles unbounded, continuous data flows as each event occurs, enabling near-real-time analytics (e.g., Azure Stream Analytics) with low latency. The scenario explicitly describes a one-time nightly schedule, not an always-on pipeline that reacts to each customer purchase immediately, so streaming's event-by-event processing model does not match the batch workload.
When this WOULD be correct
A question describing a system that continuously ingests and processes transactions as they occur (e.g., fraud detection on credit card swipes) would make streaming processing the correct answer.
- ✗
Transactional processing
Why it's wrong here
Transactional processing is incorrect because it focuses on individual online transactions, such as order placements or updates, using OLTP systems that enforce ACID guarantees for row-level writes. The nightly analysis is a read-only, large-scale aggregation over many transactions, which is characteristic of OLAP workloads (e.g., using Azure Synapse or PolyBase), not the high-concurrency, write-optimized scope of transactional processing.
When this WOULD be correct
A question describing a system that processes individual sales transactions as they occur, ensuring ACID properties for each purchase, would make transactional processing correct.
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.
✓Batch processingCorrect answer▾
Why this is correct
Batch processing is the correct fit because the nightly job processes a large, accumulated volume of purchase records at a scheduled time, using a bounded dataset. This aligns with the classic batch pattern of ingesting data over a period, then running a job (e.g., Azure Data Factory or Spark) to aggregate and analyze patterns offline without requiring sub-second latency.
✗Real-time processingWrong answer — click to see why▾
Why this is wrong here
The nightly job processes data in large batches once per day, not continuously or with low latency, so it is not real-time processing.
★ When this WOULD be the correct answer
A question describing a system that monitors credit card transactions for fraud and must flag suspicious activity within milliseconds would make real-time processing correct.
Why candidates choose this
Candidates may confuse 'nightly' with 'real-time' because they think of the job running every night as a scheduled, recurring process, but real-time requires immediate processing.
✗Streaming processingWrong answer — click to see why▾
Why this is wrong here
Streaming processing handles data continuously as it arrives, but this job runs nightly on already-collected data, making it batch, not streaming.
★ When this WOULD be the correct answer
A question describing a system that continuously ingests and processes transactions as they occur (e.g., fraud detection on credit card swipes) would make streaming processing the correct answer.
Why candidates choose this
Candidates may confuse 'streaming' with any large-scale data processing, or think that processing millions of transactions implies a stream, missing the scheduled batch trigger.
✗Transactional processingWrong answer — click to see why▾
Why this is wrong here
Transactional processing handles individual, real-time transactions (e.g., order placement), not nightly aggregation of millions of past transactions into summary tables.
★ When this WOULD be the correct answer
A question describing a system that processes individual sales transactions as they occur, ensuring ACID properties for each purchase, would make transactional processing correct.
Why candidates choose this
Candidates may confuse 'transactional' with any data processing involving transactions, not realizing it specifically refers to OLTP systems that handle individual, real-time operations.
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 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.
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Written by Johnson Ajibi, MSc IT Security
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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.