The answer is Online Analytical Processing (OLAP). This is correct because the scenario involves running complex SQL queries that aggregate millions of rows of historical sales data to identify yearly trends, with no new data being added during analysis—a classic read-intensive, analytical workload. OLAP is specifically designed for such tasks, where large volumes of static data are summarized and queried to support business intelligence and decision-making, contrasting with OLTP’s focus on high-volume transactional writes. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your ability to distinguish workload types based on data usage patterns; a common trap is confusing OLAP with OLTP when the query involves SQL, but remember that OLTP handles real-time inserts and updates, while OLAP handles historical aggregation. A helpful memory tip: think “OLAP for analysis, OLTP for transactions”—or simply, “OLAP reads the past, OLTP writes the present.”
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 data scientist needs to analyze historical sales data to identify yearly trends. They run SQL queries that aggregate millions of rows. No new data is being added during analysis. Which type of data processing workload does this represent?
Why wrong: OLTP is for many small transactions (inserts, updates, deletes), not for complex aggregations on historical data.
B
Online Analytical Processing (OLAP)
OLAP is used for complex queries and aggregations on historical data, which matches the scenario.
C
Batch processing
Why wrong: Batch processing is a method of processing data in bulk at scheduled times, but the scenario describes the type of workload, not the execution method.
D
Stream processing
Why wrong: Stream processing handles real-time data continuously; this scenario involves static historical data.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Online Analytical Processing (OLAP)
This workload is Online Analytical Processing (OLAP) because the data scientist is running complex SQL queries that aggregate millions of rows of historical sales data to identify yearly trends. OLAP is designed for read-intensive, analytical queries that summarize large volumes of static data, which matches the scenario where no new data is being added during analysis.
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.
✗
Online Transaction Processing (OLTP)
Why it's wrong here
OLTP is for many small transactions (inserts, updates, deletes), not for complex aggregations on historical data.
✓
Online Analytical Processing (OLAP)
Why this is correct
OLAP is used for complex queries and aggregations on historical data, which matches the scenario.
Related concept
Read the scenario before looking for a memorised answer.
✗
Batch processing
Why it's wrong here
Batch processing is a method of processing data in bulk at scheduled times, but the scenario describes the type of workload, not the execution method.
✗
Stream processing
Why it's wrong here
Stream processing handles real-time data continuously; this scenario involves static historical data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the distinction between OLTP and OLAP by presenting a scenario with 'SQL queries' and 'aggregation,' leading candidates to mistakenly think any SQL query implies OLTP, when in fact the analytical nature and static dataset clearly indicate OLAP.
Trap categories for this question
Scenario analysis trap
Batch processing is a method of processing data in bulk at scheduled times, but the scenario describes the type of workload, not the execution method.
Detailed technical explanation
How to think about this question
Under the hood, OLAP systems often use columnar storage (e.g., in Azure Synapse or SQL Server Analysis Services) to optimize aggregation queries, scanning only relevant columns rather than entire rows. This contrasts with OLTP's row-based storage, which is optimized for point lookups and transactional integrity via ACID properties. In real-world scenarios, a data scientist might use an OLAP cube or a dedicated analytical store to pre-aggregate data across dimensions like time and product, enabling fast drill-downs without reprocessing raw data.
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: Online Analytical Processing (OLAP) — This workload is Online Analytical Processing (OLAP) because the data scientist is running complex SQL queries that aggregate millions of rows of historical sales data to identify yearly trends. OLAP is designed for read-intensive, analytical queries that summarize large volumes of static data, which matches the scenario where no new data is being added during analysis.
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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