- A
Azure Data Factory with mapping data flows
Azure Data Factory provides schedule-based orchestration and mapping data flows to perform complex transformations without coding. It integrates seamlessly with Azure Synapse Analytics for loading transformed data.
- B
Azure Stream Analytics
Why wrong: Azure Stream Analytics is designed for real-time stream processing, not for scheduled batch transformations of files. It is not suitable for this nightly batch workload.
- C
Azure Databricks
Why wrong: Azure Databricks can transform data using notebooks, but it lacks built-in orchestration for scheduling and monitoring pipelines. Additional setup is needed, making it less efficient than Azure Data Factory for this requirement.
- D
Azure Logic Apps
Why wrong: Azure Logic Apps is for workflow automation and integration, not for heavy data transformations and large-scale ETL. It is not designed to handle complex data cleansing and aggregation operations.
Quick Answer
The answer is Azure Data Factory with mapping data flows. This service is correct because mapping data flows provide a code-free, visual interface for building complex ETL transformations like cleansing, validation, and aggregation, executing them at scale on serverless Azure Databricks clusters without requiring any manual Spark code. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your understanding of which Azure service handles orchestrated, code-free data transformation pipelines for batch workloads—a common scenario where candidates might mistakenly choose Azure Databricks (which requires coding) or Azure Synapse Pipelines (which lacks the dedicated visual transformation designer). The key trap is confusing orchestration with transformation: Data Factory orchestrates the entire pipeline, while mapping data flows handle the actual data shaping. Remember the mnemonic “Map it, don’t code it” to recall that mapping data flows are the no-code transformation engine within Data Factory’s scheduling framework.
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.
A company receives daily sales data from multiple retail stores as CSV files that are uploaded to Azure Blob Storage. The data must be cleansed, validated, and aggregated before being loaded into Azure Synapse Analytics for reporting. The transformations involve complex business logic and must run reliably every night. The company wants a service that can orchestrate and execute the entire pipeline with minimal development effort. Which Azure service should they use?
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 correct because it provides a code-free, visual interface for building complex data transformations (cleansing, validation, aggregation) that can be orchestrated on a schedule. Mapping data flows execute at scale on Azure Databricks clusters without requiring manual Spark code, making it ideal for nightly batch ETL pipelines with minimal development effort.
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 with mapping data flows
Why this is correct
Azure Data Factory provides schedule-based orchestration and mapping data flows to perform complex transformations without coding. It integrates seamlessly with Azure Synapse Analytics for loading transformed data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure Stream Analytics
Why it's wrong here
Azure Stream Analytics is designed for real-time stream processing, not for scheduled batch transformations of files. It is not suitable for this nightly batch workload.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks can transform data using notebooks, but it lacks built-in orchestration for scheduling and monitoring pipelines. Additional setup is needed, making it less efficient than Azure Data Factory for this requirement.
- ✗
Azure Logic Apps
Why it's wrong here
Azure Logic Apps is for workflow automation and integration, not for heavy data transformations and large-scale ETL. It is not designed to handle complex data cleansing and aggregation operations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Data Factory with Azure Logic Apps because both are 'orchestration' services, but Logic Apps is for API/application integration (HTTP, Office 365, etc.) and cannot perform large-scale data transformations or run Spark-based data flows.
Detailed technical explanation
How to think about this question
Mapping data flows in Azure Data Factory translate visual transformations into optimized Spark jobs that run on serverless Databricks clusters, automatically handling partitioning and parallelization. Under the hood, each data flow uses a directed acyclic graph (DAG) of transformation steps that can be debugged with data previews, and the pipeline can trigger the flow on a schedule (e.g., nightly) using tumbling window triggers. A real-world scenario is a retail company that needs to join daily sales CSVs from hundreds of stores, apply business rules like discount calculations, and load the aggregated results into a Synapse dedicated SQL pool—all without writing a single line of code.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DP-900 question test?
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 with mapping data flows — Azure Data Factory with mapping data flows is correct because it provides a code-free, visual interface for building complex data transformations (cleansing, validation, aggregation) that can be orchestrated on a schedule. Mapping data flows execute at scale on Azure Databricks clusters without requiring manual Spark code, making it ideal for nightly batch ETL pipelines with minimal development effort.
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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Same concept, more angles
1 more ways this is tested on DP-900
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 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?
medium- A.Azure Logic Apps
- ✓ B.Azure Data Factory with Mapping Data Flows
- C.Azure Functions
- D.Azure SQL Database Change Data Capture
Why B: 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.
Last reviewed: Jun 11, 2026
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