DP-900 Describe core data concepts Practice Question
A manufacturing company stores two types of data: (1) real-time sensor readings from production machines used to monitor current machine status, and (2) historical daily production summaries used by managers to identify trends over months. Which statement accurately describes these workloads?
⚠ Common exam trap
Test-takers frequently confuse OLTP with real-time and OLAP with batch, but OLTP can be real-time (e.g., sensor inserts) and OLAP can be batch (e.g., daily summaries), so the key distinction is transactional vs. analytical processing, not timing.
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
✓
Sensor readings are an OLTP workload; daily summaries are an OLAP workload.
Real-time sensor readings involve frequent, small inserts and point lookups (typical of an OLTP workload), while historical daily summaries are aggregated data used for trend analysis over months (typical of an OLAP workload). OLTP systems handle high-volume transactional operations, whereas OLAP systems support complex queries and aggregations on large historical datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Sensor readings are an OLAP workload; daily summaries are an OLTP workload.
Why it's wrong here
This inverts the two workload types. OLAP is designed for complex analytical queries and aggregations over large, historical datasets, not for high-frequency, low-latency writes. Sensor readings arrive continuously as discrete, high-volume transactions that update a time-series store, a paradigm that aligns with OLTP (online transaction processing). Daily summaries are the result of analytical transformations over those raw readings, which is the essence of OLAP. Thus, labeling sensor ingestion as OLAP and summary generation as OLTP mischaracterizes the access patterns.
- ✓
Sensor readings are an OLTP workload; daily summaries are an OLAP workload.
Why this is correct
Sensor readings represent OLTP because every reading is an atomic transaction: frequent, small inserts that require fast response times, often with ACID guarantees, and typically involving a single sensor or a small batch at a time. Daily summaries represent OLAP because they are produced by aggregating millions of sensor readings into totals, averages, or trends—long-running, complex analytical queries that support decision-making. This distinction aligns with the classic separation between transactional systems that capture operations and analytical systems that support reporting.
- ✗
Sensor readings are a NoSQL workload; daily summaries are a relational workload.
Why it's wrong here
The categorization into NoSQL vs. relational is orthogonal to the OLTP/OLAP distinction. Sensor readings could be stored in a relational database (e.g., Azure SQL Database) or a NoSQL store (e.g., Azure Cosmos DB) depending on schema flexibility, scale, and access patterns; the workload is still OLTP if the dominant operation is point writes. Similarly, daily summaries could be built in a relational data warehouse or via a NoSQL pipeline, but that does not make them 'relational'—they are analytical aggregates, and the correct classification is OLAP. The option wrongly conflates storage technology with workload type.
- ✗
Sensor readings are a batch workload; daily summaries are a real-time workload.
Why it's wrong here
This option reverses the actual real-time and batch dynamics. Sensor readings are streamed into the system in near real-time—think IoT devices emitting data every second—so the ingestion workload is real-time streaming, not batch. Daily summaries, on the other hand, are computed periodically (e.g., every 24 hours) by running batch jobs over collected data, so they represent a batch workload. Calling sensor readings batch implies they are processed in large chunks with latency, while calling daily summaries real-time ignores the scheduled, aggregated nature of the computation. The workload classification should match the ingestion and processing cadence, which is opposite to what this option states.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
OLAP
OLAP (Online Analytical Processing) is a computing approach that enables users to quickly and interactively analyze multidimensional data from multiple perspectives for business intelligence and decision support.
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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