Azure Data Factory is the correct choice because it is a cloud-based ETL and data orchestration service built to handle complex, schedule-driven pipelines, and it natively supports incremental hourly data loads from on-premises SQL Server via a self-hosted integration runtime. This allows you to ingest only the new or changed sales transactions each hour, minimizing latency and resource usage. On the Microsoft Azure Data Fundamentals DP-900 exam, this scenario tests your understanding of Azure Data Factory’s role as the primary orchestration tool for data movement and transformation, often contrasted with services like Azure Synapse Pipelines or Azure Databricks—though ADF is the dedicated orchestrator. A common trap is confusing Azure Data Factory with Azure Synapse Analytics itself; remember that Synapse is the destination for reporting, while ADF handles the orchestration and incremental loading. Memory tip: think of ADF as the “hourly conductor” that picks up only the new data from your SQL Server train and delivers it to Synapse.
DP-900 Describe core data concepts Practice Question
This DP-900 practice question tests your understanding of describe core data concepts. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
You are designing a data pipeline that ingests sales transactions from an on-premises SQL Server database into Azure Synapse Analytics for reporting. The data must be processed incrementally every hour with minimal latency. Which Azure service should you use to orchestrate the pipeline?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Data Factory
Azure Data Factory (ADF) is the correct choice because it is a cloud-based ETL and data orchestration service designed specifically for building complex, schedule-driven pipelines. It natively supports incremental data loading from on-premises SQL Server via self-hosted integration runtime, and can trigger pipelines on an hourly schedule with minimal latency, making it ideal for this scenario.
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.
✗
Azure Logic Apps
Why it's wrong here
Logic Apps is for lightweight workflows and integrations, not for high-throughput data pipelines.
✗
Azure Databricks
Why it's wrong here
Databricks is for big data processing and analytics, not a dedicated orchestration service.
✗
Azure Functions
Why it's wrong here
Functions is for event-driven code, not for orchestrating complex data movement.
✓
Azure Data Factory
Why this is correct
Azure Data Factory is purpose-built for ETL and data orchestration, supporting incremental loads from on-premises.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse orchestration services with compute or processing services, assuming Azure Databricks or Azure Functions can handle scheduling and data movement, when in fact Azure Data Factory is the dedicated PaaS orchestrator for such pipelines.
Detailed technical explanation
How to think about this question
Azure Data Factory uses a self-hosted integration runtime to securely connect to on-premises SQL Server via port 1433 (default SQL Server port) and supports change tracking or watermark columns for incremental loads. Under the hood, ADF translates pipeline activities into T-SQL queries executed against the source database, enabling efficient delta extraction without full table scans. In a real-world scenario, you would configure a tumbling window trigger for hourly execution and use a lookup activity to store the last watermark value, ensuring only new or modified rows are transferred.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
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: Azure Data Factory — Azure Data Factory (ADF) is the correct choice because it is a cloud-based ETL and data orchestration service designed specifically for building complex, schedule-driven pipelines. It natively supports incremental data loading from on-premises SQL Server via self-hosted integration runtime, and can trigger pipelines on an hourly schedule with minimal latency, making it ideal for this scenario.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data engineer needs to load data from an on-premises SQL Server database to Azure Synapse Analytics every hour with minimal latency. Which Azure service should they use?
easy
A.Azure Databricks
✓ B.Azure Data Factory
C.Azure SQL Database
D.Azure HDInsight
Why B: Azure Data Factory (ADF) is the correct choice because it provides a fully managed, code-free ETL service that can connect to on-premises SQL Server via self-hosted integration runtime, and load data into Azure Synapse Analytics with low latency using a scheduled trigger (e.g., every hour). ADF supports incremental data loading and parallel copy activities, minimizing latency while handling the required frequency.
Last reviewed: Jun 24, 2026
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