Question 313 of 982

Describe core data concepts →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is that the first workload is stream processing and the second is batch processing. This distinction hinges on the nature of the data and the processing cadence: stream processing handles continuous, unbounded data flows with low-latency computations—like calculating moving averages from a real-time message queue every minute—while batch processing works with bounded, static datasets on a scheduled, high-latency basis, such as transforming daily CSV files for monthly reporting. On the Microsoft Azure Data Fundamentals DP-900 exam, this concept tests your ability to match Azure services (like Azure Stream Analytics for streaming vs. Azure Data Factory or Azure Synapse pipelines for batch) to workload requirements. A common trap is confusing micro-batch processing (e.g., every minute) with true batch; remember that if data arrives continuously and results must update a live dashboard, it’s stream processing. Memory tip: “Streams are never-ending rivers; batches are frozen lakes you process once.”

DP-900 Describe core data concepts Practice Question

This DP-900 practice question tests your understanding of describe core data concepts. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

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?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.

Question 1hardmultiple choice

Study the full Python automation breakdown →

A
First workload: Stream processing, Second workload: Batch processing
Real-time stock trade analysis with moving averages is a classic stream processing workload (low latency, continuous). End-of-day CSV file processing is batch processing (scheduled, bulk).
B
First workload: Batch processing, Second workload: Stream processing
Why wrong: This reverses the definitions: batch processes data at rest in bulk, while stream processes data in motion. The first workload is stream, not batch; the second is batch, not stream.
C
First workload: OLTP, Second workload: OLAP
Why wrong: 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).
D
First workload: Transactional processing, Second workload: Analytical processing
Why wrong: Transactional processing involves CRUD operations on individual rows. The first workload is analytical (calculating aggregates) on a stream, not transactional. The second is batch transformation, which is more ETL than pure analytical querying.

Full breakdown with real-world context →

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

Option A is correct because 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).

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

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
Real-time stock trade analysis with moving averages is a classic stream processing workload (low latency, continuous). End-of-day CSV file processing is batch processing (scheduled, bulk).
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
First workload: Batch processing, Second workload: Stream processing
Why it's wrong here
This reverses the definitions: batch processes data at rest in bulk, while stream processes data in motion. The first workload is stream, not batch; the second is batch, not stream.
✗
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
Transactional processing involves CRUD operations on individual rows. The first workload is analytical (calculating aggregates) on a stream, not transactional. The second is batch transformation, which is more ETL than pure analytical querying.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that 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).

Detailed technical explanation

How to think about this question

Stream processing engines like Apache Kafka Streams or Azure Stream Analytics operate on unbounded data streams using event-time windows (e.g., tumbling windows for 1-minute moving averages) and maintain state for aggregations. Batch processing frameworks like Azure Data Factory or Apache Spark (in batch mode) read bounded datasets (e.g., CSV files), apply transformations in stages, and write to a data warehouse using techniques like bulk insert or PolyBase for efficient loading. A subtle behavior: stream processing may use exactly-once semantics via checkpointing (e.g., Kafka offsets), while batch processing often relies on idempotent writes to handle retries.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.

TExam Day Tips

Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this DP-900 question test?

Describe core data concepts — This question tests Describe core data concepts — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: First workload: Stream processing, Second workload: Batch processing — Option A is correct because 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).

What should I do if I get this DP-900 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

Are there clue words in this question I should notice?

Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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