The answer is Azure Data Factory, the fully managed, cloud-based data integration service for creating data-driven workflows. This service is correct because it provides a serverless platform to build, schedule, and orchestrate complex data pipelines, ingesting data from over 90 built-in connectors across on-premises and cloud sources, then transforming it using compute services like Azure Databricks or HDInsight. On the Microsoft Azure Fundamentals AZ-900 exam, this question tests your understanding of core Azure data services, often appearing as a scenario where you must distinguish between tools like Azure Data Factory (for orchestration) and Azure Synapse Analytics (for analytics). A common trap is confusing ADF with Azure Data Lake Storage, which is just a storage layer, not an integration service. Memory tip: think of ADF as the “conductor” of your data orchestra—it moves and transforms data, but doesn’t store it.
AZ-900 Describe Azure architecture and services Practice Question
This AZ-900 practice question tests your understanding of describe azure architecture and services. 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 Azure service provides a fully managed, cloud-based data integration service for creating data-driven workflows?
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 a fully managed, cloud-based data integration service that allows you to create, schedule, and orchestrate data-driven workflows (pipelines). It supports over 90 built-in connectors to ingest, transform, and move data across on-premises and cloud sources, making it the correct choice 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 Synapse Analytics
Why it's wrong here
Synapse Analytics provides analytics capabilities; Data Factory focuses on data movement and transformation pipelines.
✓
Azure Data Factory
Why this is correct
Data Factory is the managed ETL service for creating data-driven pipelines that move and transform data.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Databricks
Why it's wrong here
Databricks provides Spark-based analytics; Data Factory is for orchestrating data movement pipelines.
✗
Azure Stream Analytics
Why it's wrong here
Stream Analytics processes real-time streaming data; Data Factory handles batch ETL data integration.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing Azure Data Factory with Azure Synapse Analytics, as both involve data movement and transformation, but Synapse is primarily a unified analytics platform (data warehouse + big data), not a dedicated integration service for creating workflows.
Detailed technical explanation
How to think about this question
Azure Data Factory uses a JSON-based definition for pipelines and activities, and it can execute transformations via mapping data flows (which run on Spark clusters) or by invoking external compute services like Azure HDInsight or Azure Databricks. A real-world scenario is an ETL pipeline that ingests sales data from an on-premises SQL Server, transforms it using a data flow, and loads it into Azure Synapse for reporting, all orchestrated on a schedule or triggered by an event.
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 AZ-900 question in full detail.
Describe Azure architecture and services — This question tests Describe Azure architecture and services — 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 a fully managed, cloud-based data integration service that allows you to create, schedule, and orchestrate data-driven workflows (pipelines). It supports over 90 built-in connectors to ingest, transform, and move data across on-premises and cloud sources, making it the correct choice for this scenario.
What should I do if I get this AZ-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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