DP-900 Describe an analytics workload on Azure Practice Question
A retail company runs a nightly job that reads all sales transactions from the previous day from an operational database, aggregates them by product category and store location, and writes the summary results into a data warehouse for reporting. Which type of data processing workload does this nightly job represent?
⚠ Common exam trap
Candidates often confuse 'scheduled' or 'periodic' processing with stream processing, but the key differentiator is that batch processing works on a bounded dataset (all data from the previous day) while stream processing works on an unbounded, continuous flow of data.
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
✓
B. Batch processing
The nightly job processes data in discrete, scheduled batches—reading all sales transactions from the previous day, aggregating them, and writing results to a data warehouse. This is the classic definition of batch processing, where data is collected over a period and processed together in a single job run. In Azure, this workload maps to services like Azure Data Factory or Azure Synapse Pipelines executing scheduled pipelines.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A. Real-time processing
Why it's wrong here
Real-time processing implies that data is consumed and reacted to as soon as it arrives, with latency measured in seconds or milliseconds using event-driven architectures, in-memory compute, or change-data-capture pipelines. A nightly job that starts at a predetermined hour and processes a full snapshot of the day's data is not continuous or low-latency; it is inherently a delayed, batch-oriented operation, so calling it real-time processing is technically incorrect.
When this WOULD be correct
A question describing a system that processes sales transactions instantly as they occur, updating dashboards in real-time, would make real-time processing correct.
- ✓
B. Batch processing
Why this is correct
Batch processing is engineered for high-throughput, scheduled execution over bounded datasets. The nightly job reads the full retail dataset at a set time, performs aggregations or transformations, and writes results to a data warehouse or analytical store — a classic batch ETL/ELT pattern. It accepts multi-hour latency in exchange for efficient resource utilization and full-data accuracy, making it the appropriate model for this workload.
- ✗
C. Stream processing
Why it's wrong here
Stream processing continuously consumes unbounded data sequences, using techniques like event-time windows, watermarks, and stateful operators to derive insights from data in motion. The nightly job, by contrast, processes a fixed, finite dataset that is complete before the job starts — a bounded batch, not an open-ended stream. There is no notion of arriving events or windowing in this scenario, so stream processing does not describe the workload.
When this WOULD be correct
A question describing a system that continuously ingests sales transactions as they occur and updates dashboards or alerts within seconds would make stream processing correct.
- ✗
D. Transactional processing
Why it's wrong here
Transactional processing (OLTP) is designed to handle individual business operations — such as inserting an order or updating inventory — with ACID guarantees like atomicity and isolation. Each transaction operates on a small number of rows and must complete immediately to keep the operational database consistent. The nightly job instead scans and aggregates large historical volumes, which is an analytical (OLAP) workload with no per-transaction consistency requirements, so transactional processing is not applicable.
When this WOULD be correct
A question describing a system that processes each sales transaction immediately (e.g., updating inventory and generating a receipt) 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.
✓B. Batch processingCorrect answer▾
Why this is correct
Batch processing is engineered for high-throughput, scheduled execution over bounded datasets. The nightly job reads the full retail dataset at a set time, performs aggregations or transformations, and writes results to a data warehouse or analytical store — a classic batch ETL/ELT pattern. It accepts multi-hour latency in exchange for efficient resource utilization and full-data accuracy, making it the appropriate model for this workload.
✗A. Real-time processingWrong answer — click to see why▾
Why this is wrong here
The job runs nightly and processes data from the previous day, which is a scheduled, non-continuous operation on a complete dataset, not real-time.
★ When this WOULD be the correct answer
A question describing a system that processes sales transactions instantly as they occur, updating dashboards in real-time, would make real-time processing correct.
Why candidates choose this
Candidates may confuse 'nightly' with 'real-time' because they think of the job as happening regularly, but real-time implies immediate processing, not scheduled batches.
✗C. Stream processingWrong answer — click to see why▾
Why this is wrong here
The job runs nightly and processes data from the previous day in a single batch, not continuously as it arrives. Stream processing handles data in real-time or near-real-time as it is generated.
★ When this WOULD be the correct answer
A question describing a system that continuously ingests sales transactions as they occur and updates dashboards or alerts within seconds would make stream processing correct.
Why candidates choose this
Candidates may confuse 'nightly job' with continuous processing, or think that aggregating data implies streaming, but the key is the scheduled, discrete batch of data from the past day.
✗D. Transactional processingWrong answer — click to see why▾
Why this is wrong here
Transactional processing handles individual transactions (e.g., order placement) with ACID guarantees, not nightly aggregation of historical data.
★ When this WOULD be the correct answer
A question describing a system that processes each sales transaction immediately (e.g., updating inventory and generating a receipt) would make transactional processing correct.
Why candidates choose this
Candidates may confuse the term 'transactional' with any data processing involving business transactions, not realizing it specifically refers to OLTP systems that handle real-time, individual 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
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Key term
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
Key term
Data
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