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
Batch Processing vs Streaming Analytics
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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.
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