DP-900 Describe core data concepts Practice Question
A financial analytics company has two distinct data processing workloads. The first workload ingests real-time stock trade data from a message queue, calculates moving averages every minute, and updates a dashboard for traders. The second workload receives daily CSV files containing end-of-day trade summaries, transforms them using Python scripts, and loads the results into a data warehouse for monthly reporting. Which statement correctly characterizes these workloads?
⚠ Common exam trap
A common mix-up: candidates confuse 'real-time' with 'transactional processing' (OLTP) or 'analytical processing' (OLAP), when the correct distinction is between stream processing (continuous, low-latency) and batch processing (scheduled, high-latency).
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
✓
First workload: Stream processing, Second workload: Batch processing
The first workload processes real-time stock trade data from a message queue and calculates moving averages every minute, which is a classic stream processing pattern (continuous, low-latency data ingestion and computation). The second workload handles daily CSV files with end-of-day summaries, transforms them with Python scripts, and loads results into a data warehouse for monthly reporting, which is a classic batch processing pattern (scheduled, high-latency processing of bounded data sets).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
First workload: Stream processing, Second workload: Batch processing
Why this is correct
This is correct because the first workload requires continuous, low-latency computation over an unbounded sequence of stock trade events; calculating moving averages demands real-time windowing and stateful aggregation as each event arrives, which is the defining characteristic of stream processing (e.g., Apache Kafka, Azure Stream Analytics). The second workload processes a finite, already-completed CSV file generated at end of day, a classic batch job executed on a schedule with high throughput and no requirement for sub-second latency; this distinguishes it as batch processing (e.g., Azure Data Factory, Azure Databricks).
- ✗
First workload: Batch processing, Second workload: Stream processing
Why it's wrong here
This is incorrect because it swaps the two workload types. The first workload is not batch processing: a moving average over live stock trades cannot wait for a scheduled job to run on all accumulated data, as that would destroy the real-time nature of the analysis; stream processing computes results incrementally on sliding/tumbling windows. The second workload is not stream processing: an end-of-day CSV file is a bounded, finite dataset at rest, processed as a discrete job, not an unbounded continuous feed demanding event-driven, low-latency logic.
- ✗
First workload: OLTP, Second workload: OLAP
Why it's wrong here
OLTP (Online Transaction Processing) handles transactional operations (INSERT, UPDATE, DELETE). The first workload is a real-time analytical calculation, not OLTP. The second is batch ETL, not OLAP (which is typically ad-hoc querying on aggregated data).
- ✗
First workload: Transactional processing, Second workload: Analytical processing
Why it's wrong here
This is incorrect because neither workload is transactional. Transactional processing (OLTP) handles individual CRUD operations — INSERT, UPDATE, DELETE — with ACID guarantees, whereas the first workload performs real-time analytical aggregations (moving averages) over a stream, which is analytical in nature but not OLTP. The second workload is batch ETL/transformation of CSV data into a data store, not analytical processing (OLAP), which typically involves ad-hoc interactive queries over aggregated historical data in a warehouse; it is a batch data movement/cleansing task rather than user-facing analytical querying.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Stream processing
Stream processing is a data processing method that continuously analyzes and acts on data in real time as it arrives, rather than storing it first and processing it later.
Key term
Data warehouse
A data warehouse is a central repository that stores large amounts of structured data from multiple sources, optimized for querying and analysis rather than day-to-day transactions.
About these practice questions
Courseiva writes every DP-900 question from scratch — 820 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-900 exam.