DP-900 Describe core data concepts Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

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  Online Transaction Processing (OLTP)

  Why it's wrong here

  These systems are optimized for high-volume, low-latency writes and reads of individual records (e.g., order transactions), not for scanning and aggregating years of historical data. OLTP stores enforce ACID properties and maintain a normalized current state, so complex analytical queries over large time ranges would cause severe lock contention and performance degradation. The scenario is a classic analytical read-heavy workload, not a transactional OLTP workload.

• ✓

  Online Analytical Processing (OLAP)

  Why this is correct

  This is the correct classification because OLAP is designed specifically for multidimensional, historical analysis—slicing, dicing, drilling down, and rolling up across dimensions such as time, region, and product. Data is typically stored in columnar, denormalized schemas (star or snowflake) that make full-table scans and aggregations fast, even on billions of rows. A data scientist analyzing historical sales trends matches this analytical workload precisely.

• ✗

  Batch processing

  Why it's wrong here

  Batch processing is an execution methodology—running jobs on collected data at scheduled or periodic intervals—rather than a workload type. While historical sales data is often processed in batches as part of an ETL pipeline, the underlying workload that serves the analysis is still OLAP. The question asks what kind of processing best describes the analysis itself, not how the data happens to be delivered to the analytical system.

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  Stream processing

  Why it's wrong here

  Stream processing is built for unbounded data that arrives continuously and needs near-real-time analysis (e.g., IoT telemetry, click streams, fraud detection). Historical sales data is finite, at rest, and already stored, so it is not an incoming event stream. Applying stream processing would be architecturally wrong because the data is not being generated in real time, and the analysis requires iterative exploration over long historical spans.