The answer is batch processing. This is the correct choice because the pipeline collects all orders from the previous hour and processes them as a single, scheduled job at the start of each hour, rather than handling each order as it arrives. In batch processing, data is gathered over a fixed time window—here, one hour—and then transformed in bulk, making it ideal for non-real-time, high-volume tasks like hourly sales aggregation. On the Microsoft Azure Data Fundamentals DP-900 exam, this scenario tests your ability to distinguish batch from stream processing, where stream processing would handle data continuously, such as updating sales totals with every new order. A common trap is confusing scheduled jobs with real-time processing; remember that if data waits for a timer before being processed, it is batch. For a memory tip, think of a baker who bakes all the cookies from orders placed in the last hour at once—that is batch, not baking each cookie the moment it is ordered.
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.
An e-commerce company runs a data pipeline that reads all orders from the previous hour, aggregates total sales per product category, and writes the results to a reporting database. The pipeline executes at the start of every hour. Which type of data processing workload does this pipeline represent?
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 pipeline reads all orders from the previous hour, aggregates total sales per product category, and writes results to a reporting database at the start of every hour. This is a classic batch processing workload because data is collected over a fixed time window (one hour) and processed as a single, scheduled job, not continuously. Batch processing is ideal for non-real-time, high-volume data transformations like hourly sales aggregation.
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.
✓
Batch processing
Why this is correct
The pipeline processes a batch of data (hourly orders) on a schedule, which is batch processing.
Related concept
Read the scenario before looking for a memorised answer.
✗
Stream processing
Why it's wrong here
Streaming processes data in real time as it's generated, not in scheduled batches.
✗
Transactional processing
Why it's wrong here
Transactional processing handles individual CRUD operations, not scheduled aggregations.
✗
Interactive processing
Why it's wrong here
Interactive processing supports user-initiated queries, not automated scheduled jobs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse scheduled batch processing with stream processing because both can handle time-windowed aggregations, but batch processes data in discrete, scheduled chunks while stream processes data continuously as it arrives.
Detailed technical explanation
How to think about this question
Under the hood, batch processing frameworks like Apache Spark or Azure Data Factory read data from a source (e.g., a database or blob storage), apply transformations such as grouping and aggregation using operations like GROUP BY or reduceByKey, and write the output to a destination. A subtle behavior is that batch jobs often require checkpointing or watermarking to handle late-arriving data, ensuring consistency in the aggregated results. In real-world scenarios, this pattern is common for generating daily or hourly sales reports in e-commerce, where latency of a few minutes is acceptable.
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 — This pipeline reads all orders from the previous hour, aggregates total sales per product category, and writes results to a reporting database at the start of every hour. This is a classic batch processing workload because data is collected over a fixed time window (one hour) and processed as a single, scheduled job, not continuously. Batch processing is ideal for non-real-time, high-volume data transformations like hourly sales aggregation.
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.
About these practice questions
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Variation 1. A logistics company ingests GPS coordinates from delivery trucks in real-time to update a live tracking dashboard. They also run a nightly job to aggregate the day's deliveries into a report stored in Azure SQL Database. Which statement correctly describes the data processing types used for these two workloads?
easy
✓ A.GPS ingestion is stream processing; nightly aggregation is batch processing.
B.GPS ingestion is batch processing; nightly aggregation is stream processing.
C.Both workloads are examples of stream processing.
D.Both workloads are examples of batch processing.
Why A: Option A is correct because the real-time ingestion of GPS coordinates from delivery trucks is a classic stream processing workload, where data is processed continuously as it arrives with low latency. The nightly aggregation of daily deliveries into a report stored in Azure SQL Database is a batch processing workload, where data is processed in bulk at scheduled intervals. Azure Stream Analytics is commonly used for the streaming ingestion, while Azure SQL Database or Azure Synapse Analytics can handle the batch aggregation.
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Question Discussion
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