Match each data processing term to its definition.
Drag a concept onto its matching description — or click a concept then click the description.
Extract, Transform, Load
Extract, Load, Transform
Processing large volumes of data at scheduled intervals
Processing data in real-time as it arrives
Online Transaction Processing
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Batch processing: Processes large volumes of data at scheduled intervals.
Batch processing handles large volumes at scheduled times, while stream processing handles real-time data. ETL transforms before loading; ELT transforms after loading. Common confusion is swapping ETL and ELT definitions or misapplying batch/stream terms.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Batch processing: Processes large volumes of data at scheduled intervals.
Why this is correct
Batch processing is a data processing model where large volumes of data are collected, stored, and processed together at predetermined intervals, such as nightly runs or hourly jobs. It prioritizes throughput and efficiency over immediacy, and is well-suited for generating daily reports, aggregating historical data, or performing expensive transformations on complete datasets. This contrasts with stream processing, which ingests and processes data continuously as it arrives rather than waiting for a scheduled trigger.
Stream processing: Processes data in real-time as it arrives.
Why this is correct
Stream processing is a continuous data processing paradigm where data is ingested and processed immediately upon arrival, typically event-by-event or within tiny time windows, to produce low-latency results. It is designed for use cases like real-time fraud detection, live dashboard monitoring, and IoT telemetry, where even a few seconds of delay are unacceptable. Unlike batch processing, it never waits for a full dataset to be collected; instead it operates on an unbounded, constantly flowing sequence of events.
ETL: Extract, Transform, Load - transforms data before loading into the target.
Why this is correct
ETL is a data integration approach that extracts data from source systems, transforms it in a separate staging area—cleaning, validating, aggregating, or reformatting it—and then loads the transformed result into the target system such as a data warehouse. This transformation-before-loading sequence ensures that only high-quality, structure-adjusted data enters the destination, but it requires dedicated staging compute and makes iterative schema changes more involved. It is the traditional pipeline model, and its defining characteristic is that transformation occurs before the data reaches the target.
ELT: Extract, Load, Transform - loads raw data into the target, then transforms it.
Why this is correct
ELT is a modern data integration pattern that extracts raw data and loads it directly into the target system, typically a massively parallel processing cloud data warehouse, and then performs transformations in place using SQL or query engines. By deferring transformation until after loading, ELT avoids the bottleneck of an intermediate staging area, preserves raw data for ad-hoc exploration, and leverages the target platform's elastic compute to reshape data on demand. It is increasingly popular in data lakehouse architectures because it accommodates evolving schemas and lets analysts decide how and when to transform the data.
Stream processing: Processes data in batches at scheduled intervals.
Why it's wrong here
This definition incorrectly describes batch processing, not stream processing. Scheduling data to be processed in batches at fixed intervals introduces inherent waiting time and prevents real-time handling, which is the opposite of stream processing. True stream processing is event-driven: as soon as a data event arrives, it is processed immediately, without waiting for a scheduled window or a batch to accumulate. Choosing this incorrect definition could lead an architect to use a batch pipeline for scenarios that demand sub-second latency, such as credit-card fraud detection or equipment failure alerts.
ETL: Extract, Load, Transform - loads raw data before transformation.
Why it's wrong here
This sequence—Extract, Load, Transform—actually defines ELT, not ETL. In ETL, the transform step occurs before data is loaded into the target, using an intermediate staging area to clean and reshape the data. In ELT, raw data is loaded first and transformation happens afterward within the target system's engine. Therefore, this option is a classic term mix-up; using 'ETL' to describe a load-then-transform pipeline misrepresents the pipeline's architecture and gives the wrong order of operations.
Go deeper
Learn chapter
Data Ingestion Patterns: Batch vs Streaming
Key term
ELT
ELT (Extract, Load, Transform) is a data integration process where raw data is extracted from source systems, loaded directly into a target system like a data lake or warehouse, and then transformed as needed for analysis.
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.
About these practice questions
Courseiva writes every DP-900 question from scratch — 851 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 →
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.