DP-900 Describe core data concepts Practice Question
A retail chain collects sales data from all its stores at the end of each business day by exporting CSV files from each store's database. The data is then combined and analyzed to generate daily sales reports. Which type of data processing does this describe?
⚠ Common exam trap
Many candidates confuse 'daily export' with 'real-time' because they think 'daily' implies frequent updates, but batch processing is defined by the scheduled, non-continuous nature of the data collection and processing, not the frequency.
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 describes batch processing because sales data is collected from each store at the end of the business day, exported as CSV files, and then combined and analyzed in a scheduled, non-continuous manner. Batch processing is ideal for large volumes of data that are processed at periodic intervals, such as daily sales reports, rather than requiring immediate action.
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 executes data transformation and loading as discrete, scheduled jobs that operate on a finite set of data accumulated over time. In this scenario, store sales data is uploaded at the end of each business day, and an Azure Data Factory pipeline runs on a fixed schedule to transform and load it into Azure Synapse Analytics. This matches a typical ETL batch pattern, providing predictable, cost-efficient processing while trading off latency — results are ready the next morning, not instantly.
- ✗
Real-time processing
Why it's wrong here
Real-time processing ingests and reacts to data immediately as events occur, aiming for sub-second or near-second latency using services like Azure Stream Analytics. In this scenario, data is aggregated and uploaded only once per day from each store, so there is no continuous event flow to react to. Even if the pipeline ran every five minutes, it would still be a micro-batch; the source's daily cadence rules out true real-time processing.
When this WOULD be correct
A question describing a system that processes credit card transactions as they occur, generating fraud alerts within milliseconds, would have real-time processing as the correct answer.
- ✗
Stream processing
Why it's wrong here
Stream processing operates on an unbounded, continuously generated sequence of records, such as clicking events, sensor data, or log telemetry, rather than on finite files exported periodically. The retail chain's stores produce daily end-of-day exports that are copied to Azure Data Lake as discrete datasets, not as an infinite interleaved event stream. Stream processing would require event-capture infrastructure like Azure Event Hubs, and it would not align with the batch upload pattern described.
When this WOULD be correct
A question describing a system that ingests sales transactions from store point-of-sale systems continuously throughout the day and updates dashboards or alerts immediately would make stream processing correct.
- ✗
Interactive query
Why it's wrong here
Interactive query capabilities, such as Azure Synapse SQL serverless or Azure Data Explorer, allow analysts to run ad-hoc, user-initiated queries and receive results within seconds or minutes on demand. The scenario, however, describes a scheduled, automated data movement pipeline that runs overnight without user interaction. Interactive query is designed for exploration and reporting on already-prepared data, not for orchestrating a recurring ETL load, so it does not fit the described workflow.
When this WOULD be correct
If the question described a data analyst running SQL queries directly against a database to explore sales data and generate reports on the fly, without a predefined schedule, then interactive query would be 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 executes data transformation and loading as discrete, scheduled jobs that operate on a finite set of data accumulated over time. In this scenario, store sales data is uploaded at the end of each business day, and an Azure Data Factory pipeline runs on a fixed schedule to transform and load it into Azure Synapse Analytics. This matches a typical ETL batch pattern, providing predictable, cost-efficient processing while trading off latency — results are ready the next morning, not instantly.
✗Real-time processingWrong answer — click to see why▾
Why this is wrong here
The data is collected at the end of each business 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 processes credit card transactions as they occur, generating fraud alerts within milliseconds, would have real-time processing as the correct answer.
Why candidates choose this
Candidates may confuse 'daily' with 'real-time' because they think of modern data systems, but the key is the scheduled, non-continuous nature of the data collection.
✗Stream processingWrong answer — click to see why▾
Why this is wrong here
Stream processing handles data continuously as it arrives, but here data is collected at the end of each day in batches from CSV exports, not processed in real-time as events occur.
★ When this WOULD be the correct answer
A question describing a system that ingests sales transactions from store point-of-sale systems continuously throughout the day and updates dashboards or alerts immediately would make stream processing correct.
Why candidates choose this
Candidates may confuse 'stream processing' with any data that flows from multiple sources, or think that daily exports imply a continuous stream of data from stores.
✗Interactive queryWrong answer — click to see why▾
Why this is wrong here
Interactive query implies ad-hoc, on-demand analysis of data, but the scenario describes a scheduled, automated process that combines data at the end of each day, which is batch processing.
★ When this WOULD be the correct answer
If the question described a data analyst running SQL queries directly against a database to explore sales data and generate reports on the fly, without a predefined schedule, then interactive query would be correct.
Why candidates choose this
Candidates may confuse the act of generating reports with interactive querying, not recognizing that the scheduled, automated nature of the process defines it as batch 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
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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