DP-900 Describe core data concepts Practice Question
A retail company uploads daily sales data from all stores to Azure Blob Storage at midnight. They then run a series of data transformations using Azure Data Factory on a scheduled trigger at 2:00 AM. This processing pattern is best described as:
⚠ Common exam trap
A common mix-up: candidates confuse scheduled data movement with stream processing, but the key differentiator is the time delay and the processing of a complete dataset in one job rather than individual events as they occur.
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 pattern is batch processing because the sales data is collected in Azure Blob Storage over a period (daily) and then processed as a group at a scheduled time (2:00 AM) using Azure Data Factory. Batch processing is designed for high-volume, periodic data loads where latency is acceptable, and the transformation job runs on a complete dataset rather than individual records.
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
This scenario perfectly fits batch processing because the daily sales data from all stores is accumulated over a fixed period and then processed as a single, scheduled bulk job. Batch jobs such as nightly ETL pipelines in Azure Data Factory or scheduled Spark jobs in Azure Databricks ingest a finite, predefined dataset and transform it in one go, making it ideal for periodic reporting and analytics.
- ✗
Stream processing
Why it's wrong here
Stream processing is incorrect because it handles data continuously and in near real-time as individual events or small micro-batches arrive, using services like Azure Stream Analytics or Event Hubs. The daily upload is a one-time bulk transfer that is processed after the fact, not a constant event stream requiring sub-second latency, so it does not meet the definition of stream processing.
- ✗
Transactional processing
Why it's wrong here
Transactional processing, or OLTP, is wrong because it focuses on individual row-level operations that are executed and committed immediately, with ACID guarantees to maintain data integrity for high-concurrency, low-latency workloads. A daily bulk upload of sales records is a scheduled data movement and transformation task, not a series of single-record business transactions like placing an order or updating inventory.
- ✗
Interactive query
Why it's wrong here
Interactive query is not the right answer because it refers to on-demand, ad-hoc querying where users explore data using tools like Azure Synapse Serverless or Power BI and get immediate results. The scenario describes an automated, scheduled processing pipeline that runs without user interaction, which is fundamentally different from an interactive session where a human poses queries and waits for live responses.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Trigger
In Azure data services, a trigger is a predefined automatic action that initiates a process when a specific event occurs, such as data arriving or a schedule being met.
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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