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.

• ✓

  OLTP for real-time transactions, OLAP for nightly reports

  Why this is correct

  OLTP systems are optimized for high-concurrency, low-latency row-level inserts and updates, making them the correct engine for real-time balance changes and per-customer transactions. OLAP systems, by contrast, use columnar storage and aggregation-oriented query planning to handle complex analytical queries over large historical datasets, which matches the nightly reporting load. Using OLTP for reports would cause contention with transaction processing, while using OLAP for transactions would suffer from high write latency and poor point-update performance.

• ✗

  Batch processing for transactions, Stream processing for reports

  Why it's wrong here

  This option mistakes arrival pattern for workload classification. Customer transactions are processed individually and immediately, which is online transaction processing, not batch processing; batch would defer updates and break real-time balance accuracy. Nightly reports are generated by a scheduled batch job that reads OLAP tables, but classifying that as 'stream processing' is wrong because stream processing implies continuous, low-latency event handling, not scheduled aggregate queries. The scenario is fundamentally about OLTP versus OLAP, not batch versus stream.

  When this WOULD be correct

  If the question described a system where transactions are collected in batches and processed later (e.g., end-of-day settlement) and reports are generated from a continuous stream of data (e.g., real-time dashboards), then option B would be correct.

• ✗

  OLAP for transactions, OLTP for reports

  Why it's wrong here

  This option inverts the two workload archetypes. OLAP engines, such as columnstore or MPP databases, are designed for full scans and aggregations, so writing individual transactions in real time would introduce unacceptable per-row insert latency and lock contention. OLTP systems enforce ACID transactions and fast indexed lookups, which are ill-suited for scanning millions of historical records to produce nightly reports. The reverse pairing would cripple both the customer-facing transaction path and the reporting workload.

  When this WOULD be correct

  A question where a system uses OLAP for real-time dashboards on historical data and OLTP for batch updates to a data warehouse would make this option correct.

• ✗

  ETL for transactions, ELT for reports

  Why it's wrong here

  ETL and ELT describe data integration pipeline architectures—how source data is extracted, transformed, and loaded into a warehouse—not the processing workload that runs during the day or at night. A bank might use ETL to populate its nightly OLAP reports, but the transaction system itself is not an ETL process; it is an OLTP application. Choosing ETL/ELT confuses the data movement mechanism with the online/analytical processing paradigm, so it cannot classify how individual customer transactions and batch reports are executed.

  When this WOULD be correct

  A question asks: 'A data warehouse team extracts data from a source system, transforms it, and loads it into a staging area before moving to the warehouse. Which process does this describe?' Then ETL would be correct.

Option-by-option analysis

Why each answer is right or wrong

Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-900 exam frequently reuses these exact scenarios with slightly different constraints.

Analysis generated from the official DP-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”

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