The answer is Azure Data Factory and Azure Databricks. Azure Data Factory is the correct primary service because it is a cloud-based ETL and data integration service that provides built-in connectors for both on-premises SQL Server, via a self-hosted integration runtime, and Azure Synapse Analytics, allowing you to create, schedule, and orchestrate data pipelines that move and transform data between these sources. Azure Databricks complements this by offering a collaborative Apache Spark-based analytics platform that can perform complex transformations on the data as it moves through the pipeline. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your understanding of core data integration and processing services, often appearing as a scenario where you must distinguish between orchestration tools like Data Factory and compute engines like Databricks, with a common trap being to select only one service when the question asks for two. A helpful memory tip is to think of Data Factory as the “conductor” that moves data and Databricks as the “engine” that transforms it.
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. 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.
Which TWO Azure services can be used to build a data pipeline that moves data from on-premises SQL Server to Azure Synapse Analytics?
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 (A) is correct because it is a cloud-based ETL and data integration service that provides built-in connectors for both on-premises SQL Server (via self-hosted integration runtime) and Azure Synapse Analytics, enabling you to create, schedule, and orchestrate data pipelines that move and transform data between these sources.
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 Data Factory
Why this is correct
Data Factory can copy data from on-premises SQL Server to Synapse via self-hosted integration runtime.
Related concept
Read the scenario before looking for a memorised answer.
✓
Azure Databricks
Why this is correct
Databricks can read from on-premises SQL Server using JDBC and write to Synapse.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Machine Learning
Why it's wrong here
Azure Machine Learning is for building and deploying ML models, not ETL pipelines.
✗
Azure Stream Analytics
Why it's wrong here
Stream Analytics is for real-time streaming, not batch movement from on-premises.
✗
Azure Analysis Services
Why it's wrong here
Analysis Services is a semantic modeling service, not a data pipeline tool.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Databricks (a data engineering and analytics platform) with a pure pipeline orchestration service, but it is correct in this context because it can read from on-prem SQL Server via JDBC and write to Synapse using the Spark Synapse connector, making it a valid alternative for building the data pipeline.
Detailed technical explanation
How to think about this question
Under the hood, Azure Data Factory uses a self-hosted integration runtime (SHIR) to connect to on-premises SQL Server over port 1433 (default SQL Server port) via TCP/IP, while the cloud-side copy activity writes data into Azure Synapse Analytics using PolyBase or the COPY statement for high-throughput ingestion. A real-world scenario is migrating a nightly sales transaction dump from a local SQL Server instance to a Synapse dedicated SQL pool for next-day reporting, where Data Factory handles incremental loads via watermark columns or change tracking.
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 an analytics workload on Azure — This question tests Describe an analytics workload on Azure — 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 (A) is correct because it is a cloud-based ETL and data integration service that provides built-in connectors for both on-premises SQL Server (via self-hosted integration runtime) and Azure Synapse Analytics, enabling you to create, schedule, and orchestrate data pipelines that move and transform data between these sources.
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.
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Question Discussion
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