DP-900 Describe an analytics workload on Azure Practice Question
A company needs to ingest data from an on-premises SQL Server database into Azure SQL Database every hour. During the ingestion, they need to filter out rows where Status = 'Inactive' and convert a date column to a different format. They want a cloud-based, code-free solution that can schedule and orchestrate this task. Which Azure service should they use?
⚠ Common exam trap
It's easy for candidates to confuse Azure Logic Apps with Azure Data Factory because both can schedule and orchestrate tasks, but Logic Apps lacks the native data transformation capabilities (like filtering and date conversion) required for ETL workloads, making Data Factory with Mapping Data Flows the correct choice for code-free data transformation.
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
✓
Azure Data Factory with Mapping Data Flows
Azure Data Factory with Mapping Data Flows is the correct choice because it provides a cloud-based, code-free ETL service that can ingest data from on-premises SQL Server into Azure SQL Database, apply transformations like filtering rows (Status = 'Inactive') and converting date formats, and schedule the task using triggers. Mapping Data Flows run on Spark clusters and allow visual data transformation without writing code, making it ideal for this orchestrated, scheduled ingestion.
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
Azure Logic Apps is a workflow automation service suitable for integrating applications and data, but it is not optimized for high-volume bulk data transformations and lacks the rich data flow capabilities needed for this ETL task.
- ✓
Azure Data Factory with Mapping Data Flows
Why this is correct
Azure Data Factory provides mapping data flows, a visual designer for building data transformations at scale. It integrates with on-premises data via self-hosted integration runtime, supports scheduling, and requires no code, making it the ideal choice.
- ✗
Azure Functions
Why it's wrong here
Azure Functions allows you to write custom code in multiple languages, but it is not a code-free solution. It would require writing code for data extraction, transformation, and loading, which contradicts the requirement for a code-free approach.
- ✗
Azure SQL Database Change Data Capture
Why it's wrong here
Azure SQL Database Change Data Capture (CDC) is a technical feature that logs incremental DML changes (INSERT, UPDATE, DELETE) to dedicated change tables, but it is strictly a change-tracking mechanism, not an ETL or ELT service. It provides no built-in transformation capabilities and no native scheduling or orchestration to move the captured changes into a target like Azure Synapse or Azure Data Lake. To actually ingest and transform that data, you would still need an orchestration tool (e.g., Azure Data Factory) to query the change tables, apply transformations, and load the destination, making it an incomplete solution that adds complexity rather than fulfilling the code-free, scheduled ETL requirement.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
Key term
Column
A column is a vertical set of values in a database table that stores one specific type of attribute for every row.
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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.