The correct answers are stream processing and batch processing. This combination, known as Lambda Architecture, is essential because stream processing ingests and analyzes real-time sales data as it arrives, enabling immediate reactions to transactions, while batch processing handles large volumes of historical data at scheduled intervals to generate comprehensive business intelligence reports. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your understanding of how to architect solutions that serve both operational and analytical needs, often appearing in scenarios requiring real-time dashboards alongside monthly sales summaries. A common trap is choosing only one model, but remember that real-time and historical analytics are distinct requirements that demand both. Memory tip: think of it as "now and then"—stream for the now, batch for the then.
DP-900 Describe core data concepts Practice Question
This DP-900 practice question tests your understanding of describe core data concepts. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is designing a data solution for a retail application. The solution must support real-time analytics on streaming sales data, and also provide historical reports for business intelligence. Which TWO data processing models should be combined to meet these requirements?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Batch processing
Batch processing (B) is correct because it is used to process large volumes of historical sales data at scheduled intervals, enabling the generation of comprehensive business intelligence reports. Stream processing (E) is correct because it handles real-time data ingestion and analytics on streaming sales data, allowing the application to react instantly to sales events. Combining these two models (often called a Lambda architecture) meets both the real-time and historical reporting requirements.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Distributed processing
Why it's wrong here
Distributed processing is a computing model, not a specific data processing pattern for the described requirements.
✓
Batch processing
Why this is correct
Batch processing is used for periodic processing of large volumes of historical data, suitable for business intelligence reports.
Related concept
Read the scenario before looking for a memorised answer.
✗
Data lake storage
Why it's wrong here
Data lake storage is a storage concept, not a data processing model.
✗
Transactional database
Why it's wrong here
Transactional databases are for OLTP, not for the described analytical workloads.
✓
Stream processing
Why this is correct
Stream processing handles real-time data streams, enabling real-time analytics on sales data.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'distributed processing' (a general architecture) with a specific processing model, or they mistakenly think a transactional database can handle real-time analytics on streaming data, when in fact it is optimized for single-row transactions, not continuous data streams.
Detailed technical explanation
How to think about this question
In practice, a Lambda architecture uses stream processing (e.g., Apache Kafka with Azure Stream Analytics) for real-time views and batch processing (e.g., Azure Synapse Pipelines) for periodic recomputation of historical aggregates. The two paths are merged at query time to provide a unified view, but this introduces complexity in handling late-arriving data and maintaining consistency between the speed and batch layers. A real-world retail scenario might use stream processing to detect fraud during checkout while batch processing calculates monthly sales trends.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Describe core data concepts — This question tests Describe core data concepts — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Batch processing — Batch processing (B) is correct because it is used to process large volumes of historical sales data at scheduled intervals, enabling the generation of comprehensive business intelligence reports. Stream processing (E) is correct because it handles real-time data ingestion and analytics on streaming sales data, allowing the application to react instantly to sales events. Combining these two models (often called a Lambda architecture) meets both the real-time and historical reporting requirements.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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