DP-900 Describe an analytics workload on Azure Practice Question
A retail company runs a nightly process that reads all sales transactions from the previous day, 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 best describes this nightly process?
⚠ Common exam trap
Candidates often confuse the destination (data warehousing) with the processing workload, or mistake a scheduled nightly aggregation for stream processing because they see 'data' and 'processing' without recognizing the batch window.
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
The nightly process reads all sales transactions from the previous day, aggregates them, and writes summary results into a data warehouse. This is a classic batch processing workload because data is collected over a period (the entire previous day), processed in a single offline job, and the output is stored for later reporting. Batch processing is ideal for high-volume, non-real-time transformations like nightly ETL (Extract, Transform, Load) jobs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Online Transaction Processing (OLTP)
Why it's wrong here
Online Transaction Processing (OLTP) is incorrect because it refers to a workload pattern for managing many small, concurrent transactions (INSERT, UPDATE, DELETE) that support operational applications, emphasizing ACID properties and sub-second response times. The nightly job is a read-intensive, large-scale aggregation over historical data, not a series of user-facing transactional operations. OLTP is about maintaining the latest state of a system, whereas the nightly process is analytical and writes summary results, not the day-to-day transaction stream.
When this WOULD be correct
A question describing a system that records individual sales transactions as they occur, with high concurrency and immediate data consistency, such as a point-of-sale system or an e-commerce checkout process, would have OLTP as the correct answer.
- ✓
Batch processing
Why this is correct
Batch processing is the correct classification because the nightly job operates on a finite, large volume of data that has accumulated over a full day (e.g., all sales transactions). It runs on a fixed schedule, is non-interactive, and prioritizes high throughput over low latency, producing aggregated results for reporting. This is the classic ETL/ELT pattern for offline analytics, where the entire dataset is processed in one job rather than incrementally as events occur.
- ✗
Stream processing
Why it's wrong here
Stream processing is incorrect because it is designed to handle unbounded, continuous data flows in near real-time, processing each event (or micro-batch) as it arrives with latencies of seconds or milliseconds. The nightly job instead processes a bounded daily snapshot on a fixed schedule, which is the opposite of the event-driven, always-on nature of streaming. Unlike batch, stream processing does not wait for a period to elapse; it reacts to data in motion, whereas the described job is explicitly time-triggered.
When this WOULD be correct
Stream processing would be correct for a question describing a system that continuously ingests and processes sales transactions as they occur (e.g., real-time fraud detection or live dashboard updates).
- ✗
Data warehousing
Why it's wrong here
Data warehousing is incorrect because it is not a processing workload type but rather a storage and querying architecture for integrated, historical data used in reporting and analytics. A nightly batch job may load or transform data into a data warehouse, but the processing itself is batch processing; the warehouse is the destination or serving layer, not the method of execution. Confusing the two conflates where data resides with how data is processed, which is a key distinction in data workload classification.
When this WOULD be correct
A question asks: 'Which Azure service is used to store historical data for reporting and analysis from multiple sources?' Here, data warehousing (e.g., Azure Synapse Analytics) would be correct as the storage solution.
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 classification because the nightly job operates on a finite, large volume of data that has accumulated over a full day (e.g., all sales transactions). It runs on a fixed schedule, is non-interactive, and prioritizes high throughput over low latency, producing aggregated results for reporting. This is the classic ETL/ELT pattern for offline analytics, where the entire dataset is processed in one job rather than incrementally as events occur.
✗Online Transaction Processing (OLTP)Wrong answer — click to see why▾
Why this is wrong here
The nightly process reads historical data and writes aggregated results, which is a batch operation, not real-time transaction processing. OLTP is designed for high-volume, low-latency transactions like order entry, not for periodic aggregation of historical data.
★ When this WOULD be the correct answer
A question describing a system that records individual sales transactions as they occur, with high concurrency and immediate data consistency, such as a point-of-sale system or an e-commerce checkout process, would have OLTP as the correct answer.
Why candidates choose this
Candidates may confuse the data source (sales transactions) with the processing type, assuming that any system handling transactions is OLTP, without recognizing that the processing pattern (nightly batch aggregation) defines the workload.
✗Stream processingWrong answer — click to see why▾
Why this is wrong here
The nightly process reads all sales transactions from the previous day, not in real-time, and processes them as a single batch, which is batch processing, not stream processing.
★ When this WOULD be the correct answer
Stream processing would be correct for a question describing a system that continuously ingests and processes sales transactions as they occur (e.g., real-time fraud detection or live dashboard updates).
Why candidates choose this
Candidates may confuse 'nightly process' with continuous data flow, or think that any data processing involving transactions is streaming, overlooking the batch nature of the scheduled run.
✗Data warehousingWrong answer — click to see why▾
Why this is wrong here
Data warehousing is a storage and querying system for analytics, not a processing workload. The nightly process is a batch processing job that loads data into the warehouse, but the process itself is batch, not data warehousing.
★ When this WOULD be the correct answer
A question asks: 'Which Azure service is used to store historical data for reporting and analysis from multiple sources?' Here, data warehousing (e.g., Azure Synapse Analytics) would be correct as the storage solution.
Why candidates choose this
Candidates confuse the destination (data warehouse) with the processing type, thinking that any work involving a data warehouse is 'data warehousing' rather than recognizing the batch nature of the nightly aggregation.
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
Batch processing
Batch processing is a method of running high-volume, repetitive data jobs where a group of transactions is collected, processed together automatically, and then results are produced without real-time user interaction.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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