DP-900 Describe an analytics workload on Azure Practice Question
A company uses Azure Data Factory to run a pipeline that copies new orders from an on-premises SQL Server database to Azure Data Lake Storage every hour. After the data is in the data lake, an Azure Databricks notebook transforms it and loads it into Azure Synapse Analytics for reporting. Which type of data processing does the hourly copy operation represent?
⚠ Common exam trap
A common mix-up: candidates confuse scheduled data movement (batch) with real-time streaming, especially when the pipeline runs frequently (e.g., every hour), but the key distinction is that batch processes data in discrete intervals, not continuously as it arrives.
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
✓
Batch processing
The hourly copy operation from on-premises SQL Server to Azure Data Lake Storage is a classic batch processing pattern: data is collected over a fixed time interval (1 hour) and processed as a single unit. Azure Data Factory orchestrates this scheduled, non-continuous transfer, which aligns with batch processing's definition of handling data in discrete, periodic chunks rather than in real-time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Real-time streaming
Why it's wrong here
Real-time streaming processes data continuously as events arrive, typically in milliseconds to seconds, using services such as Azure Event Hubs, IoT Hub, or Stream Analytics. An hourly scheduled copy activity explicitly waits for an interval, materializes a snapshot of the source data, and transfers a bounded set of records. Azure Data Factory does not provide event-at-a-time ingestion or continuous processing semantics, so it cannot deliver the low-latency, always-on behavior that real-time streaming implies.
When this WOULD be correct
A scenario where data must be ingested and processed with minimal latency (e.g., under 1 second) as it is generated, such as streaming IoT sensor data into Azure Event Hubs and then into Azure Stream Analytics for real-time dashboards.
- ✓
Batch processing
Why this is correct
The hourly copy operation runs on a schedule, pulling a set of source records accumulated since the previous run and moving them as one discrete data chunk. This is the classic batch processing model used in Azure Data Factory for ETL/ELT workloads, where data is processed in fixed, configurable intervals rather than continuously or on demand. The copy activity treats the entire dataset as a bounded unit, with timeout, retry, and concurrency semantics designed for bulk transfer, not per-record interaction.
- ✗
Interactive query
Why it's wrong here
Interactive query refers to on-demand, user-driven querying where a person submits a query and waits for results, such as with Azure Synapse Serverless SQL, Azure Data Explorer, or Azure SQL queries. A scheduled Azure Data Factory pipeline has no interactive user session; it is triggered automatically by a schedule and executes a predefined copy task. The pipeline does not expose a query engine to end users, so classifying it as interactive query would confuse infrastructure-driven data movement with self-service query infrastructure.
When this WOULD be correct
A question describing a user running ad-hoc SQL queries against data in Azure Data Lake Storage using Azure Synapse Serverless SQL to explore sales trends would make interactive query the correct answer.
- ✗
Transactional processing
Why it's wrong here
Transactional processing, also known as OLTP, is characterized by small, frequent, durable read/write operations that satisfy ACID guarantees, such as order entry, inventory updates, or banking transactions in a database like Azure SQL Database. An Azure Data Factory copy activity performs bulk reads and writes of large datasets, but it does not coordinate row-level transactions across the source and destination and has no distributed transaction manager. The consistency model, performance profile, and even the pricing of ADF copy are completely different from transactional processing, so this option is incorrect.
When this WOULD be correct
A question describing a system that processes individual sales transactions (e.g., order placement, payment) in real-time with rollback capabilities would make transactional processing 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.
✓Batch processingCorrect answer▾
Why this is correct
The hourly copy operation runs on a schedule, pulling a set of source records accumulated since the previous run and moving them as one discrete data chunk. This is the classic batch processing model used in Azure Data Factory for ETL/ELT workloads, where data is processed in fixed, configurable intervals rather than continuously or on demand. The copy activity treats the entire dataset as a bounded unit, with timeout, retry, and concurrency semantics designed for bulk transfer, not per-record interaction.
✗Real-time streamingWrong answer — click to see why▾
Why this is wrong here
The hourly copy operation processes data in fixed intervals (every hour), not continuously as data arrives, so it is batch processing, not real-time streaming.
★ When this WOULD be the correct answer
A scenario where data must be ingested and processed with minimal latency (e.g., under 1 second) as it is generated, such as streaming IoT sensor data into Azure Event Hubs and then into Azure Stream Analytics for real-time dashboards.
Why candidates choose this
Candidates may confuse 'hourly' with 'real-time' because the pipeline runs frequently, but real-time streaming requires continuous, low-latency processing, not scheduled intervals.
✗Interactive queryWrong answer — click to see why▾
Why this is wrong here
Interactive query typically refers to on-demand, ad-hoc analysis of data using tools like Azure Synapse Serverless SQL or Azure Databricks SQL, not scheduled, periodic data movement.
★ When this WOULD be the correct answer
A question describing a user running ad-hoc SQL queries against data in Azure Data Lake Storage using Azure Synapse Serverless SQL to explore sales trends would make interactive query the correct answer.
Why candidates choose this
Candidates may confuse the interactive nature of Databricks notebooks later in the pipeline with the copy operation itself, or think that any query against data is interactive.
✗Transactional processingWrong answer — click to see why▾
Why this is wrong here
Transactional processing typically involves ACID-compliant, real-time operations on individual transactions, whereas the hourly copy of new orders is a scheduled, bulk data movement without transactional guarantees.
★ When this WOULD be the correct answer
A question describing a system that processes individual sales transactions (e.g., order placement, payment) in real-time with rollback capabilities would make transactional processing correct.
Why candidates choose this
Candidates may confuse the movement of 'orders' data with transactional processing, not realizing that the batch copy does not handle individual transactions with ACID properties.
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?”
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Azure Databricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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