The correct answer is batch processing and streaming processing. This is because the nightly batch job loading aggregated reports into a data warehouse processes data in large, scheduled chunks at rest, which is the defining characteristic of batch processing, while the real-time analysis of live customer interactions for product recommendations handles data continuously as it arrives, which is the core of streaming processing. On the Microsoft Azure Data Fundamentals DP-900 exam, this distinction tests your understanding of Azure Synapse Analytics for batch workloads versus Azure Stream Analytics or Event Hubs for streaming workloads. A common trap is confusing scheduled batch jobs with near-real-time micro-batch processing, but remember: if data is processed in a single, periodic, high-volume run, it is batch; if it is processed record-by-record as it flows in, it is streaming. A helpful memory tip is to think of batch as a "nightly dump truck" and streaming as a "continuous pipeline."
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 retail company processes historical sales data in a nightly batch job that loads aggregated reports into a data warehouse. Additionally, the company analyzes live customer interactions from their website to provide real-time product recommendations. Which pair of terms correctly describes these two data processing approaches?
Why wrong: OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) describe workload types, not the processing pattern of batch vs. streaming.
B
Batch processing and streaming processing
Batch processing handles data in fixed, scheduled intervals (nightly reports). Streaming processing handles data continuously as it arrives (real-time recommendations). This pair correctly describes the two approaches.
C
Structured data and unstructured data
Why wrong: Structured and unstructured refer to the data format (e.g., tables vs. images), not how the data is processed.
D
Relational and NoSQL
Why wrong: Relational and NoSQL describe database types (e.g., SQL vs. document stores), not processing methods.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Batch processing and streaming processing
The nightly batch job that loads aggregated reports into a data warehouse is a classic example of batch processing, where data is processed in large, scheduled chunks. The real-time analysis of live customer interactions for product recommendations is streaming processing, which handles data continuously as it arrives. Option B correctly pairs these two distinct processing paradigms.
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.
✗
OLTP and OLAP
Why it's wrong here
OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) describe workload types, not the processing pattern of batch vs. streaming.
✓
Batch processing and streaming processing
Why this is correct
Batch processing handles data in fixed, scheduled intervals (nightly reports). Streaming processing handles data continuously as it arrives (real-time recommendations). This pair correctly describes the two approaches.
Related concept
Read the scenario before looking for a memorised answer.
✗
Structured data and unstructured data
Why it's wrong here
Structured and unstructured refer to the data format (e.g., tables vs. images), not how the data is processed.
✗
Relational and NoSQL
Why it's wrong here
Relational and NoSQL describe database types (e.g., SQL vs. document stores), not processing methods.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse OLTP/OLAP (which describe transactional vs. analytical workloads) with processing methods (batch vs. streaming), leading them to incorrectly select Option A.
Detailed technical explanation
How to think about this question
Batch processing typically uses tools like Apache Hadoop MapReduce or Azure Data Factory to process large volumes of data in scheduled intervals, while streaming processing leverages frameworks like Apache Kafka or Azure Stream Analytics to handle unbounded data streams with sub-second latency. A subtle behavior is that streaming systems often use windowing (e.g., tumbling or sliding windows) to aggregate data over time, whereas batch jobs process a fixed dataset at rest.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 and streaming processing — The nightly batch job that loads aggregated reports into a data warehouse is a classic example of batch processing, where data is processed in large, scheduled chunks. The real-time analysis of live customer interactions for product recommendations is streaming processing, which handles data continuously as it arrives. Option B correctly pairs these two distinct processing paradigms.
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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