- A
A: Azure Data Factory
Azure Data Factory supports scheduled pipelines, PolyBase integration for high-speed loading into Synapse, and incremental copy patterns, making it the ideal orchestration tool.
- B
B: Azure Stream Analytics
Why wrong: Azure Stream Analytics is designed for real-time stream processing (e.g., from Event Hubs or IoT Hub), not for batch loading from files.
- C
C: Azure Databricks
Why wrong: While Databricks can process data in ADLS, it does not natively orchestrate scheduled PolyBase loads to Synapse without additional configuration and lacks a built-in scheduler for daily jobs.
- D
D: Azure Logic Apps
Why wrong: Logic Apps are designed for enterprise workflow automation with connectors, but they are not optimized for high-throughput data movement or PolyBase-based loading.
Quick Answer
The answer is Azure Data Factory (ADF). ADF is the correct choice because it provides native orchestration and scheduling for building a pipeline that performs an Azure Data Factory Synapse incremental load with PolyBase, enabling efficient parallel data transfer from CSV files in ADLS Gen2 into an Azure Synapse dedicated SQL pool. On the DP-900 exam, this scenario tests your understanding of how ADF serves as the integration and management layer for data movement, distinguishing it from tools like Synapse Pipelines or SSIS that lack the same direct PolyBase sink configuration for incremental loads. A common trap is confusing ADF with Azure Synapse Analytics itself—remember that Synapse is the storage and compute engine, while ADF is the orchestrator that schedules and executes the load. Memory tip: think of ADF as the "conductor" that uses PolyBase as the "high-speed train" to move data efficiently on a recurring schedule.
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. A key principle to apply: azure Data Factory orchestrates data movement and transformation.. 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 uses Azure Synapse Analytics dedicated SQL pool for its data warehouse. Every day, they need to incrementally load 100 GB of new sales data from CSV files stored in Azure Data Lake Storage Gen2 (ADLS Gen2). The load should use PolyBase for efficient parallel data transfer and must be orchestrated on a recurring schedule. Which Azure service should they use to create and manage this pipeline?
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
A: Azure Data Factory
Azure Data Factory (ADF) is the correct choice because it provides native orchestration and scheduling capabilities for data pipelines. It supports PolyBase as a sink to load data into Azure Synapse dedicated SQL pool in parallel, and it can directly read CSV files from ADLS Gen2. ADF's built-in triggers allow you to schedule the daily incremental load without additional coding.
Key principle: Azure Data Factory orchestrates data movement and transformation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
A: Azure Data Factory
Why this is correct
Azure Data Factory supports scheduled pipelines, PolyBase integration for high-speed loading into Synapse, and incremental copy patterns, making it the ideal orchestration tool.
Related concept
Azure Data Factory orchestrates data movement and transformation.
- ✗
B: Azure Stream Analytics
Why it's wrong here
Azure Stream Analytics is designed for real-time stream processing (e.g., from Event Hubs or IoT Hub), not for batch loading from files.
- ✗
C: Azure Databricks
Why it's wrong here
While Databricks can process data in ADLS, it does not natively orchestrate scheduled PolyBase loads to Synapse without additional configuration and lacks a built-in scheduler for daily jobs.
- ✗
D: Azure Logic Apps
Why it's wrong here
Logic Apps are designed for enterprise workflow automation with connectors, but they are not optimized for high-throughput data movement or PolyBase-based loading.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Azure Databricks as a pipeline orchestrator, but it lacks native scheduling and PolyBase integration, whereas Azure Data Factory is the dedicated service for building and managing data pipelines with PolyBase support.
Detailed technical explanation
How to think about this question
PolyBase in Azure Synapse uses the T-SQL CREATE EXTERNAL TABLE AS SELECT (CETAS) or INSERT...SELECT commands to load data in parallel from external file sources. ADF leverages this by using the 'Copy Activity' with the PolyBase sink setting, which automatically stages data in Azure Blob Storage and then invokes PolyBase to load it into the dedicated SQL pool. This approach avoids the single-threaded bottleneck of traditional BULK INSERT and achieves throughput up to 1.2 GBps per node.
KKey Concepts to Remember
- Azure Data Factory orchestrates data movement and transformation.
- ADF's Copy Data activity supports PolyBase for high-speed loading into Synapse.
- ADF pipelines can be scheduled for recurring execution.
- ADF integrates with ADLS Gen2 and Azure Synapse Analytics.
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
Azure Data Factory orchestrates data movement and transformation.
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.
Review azure Data Factory orchestrates data movement and transformation., then practise related DP-900 questions on the same topic to reinforce the concept.
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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 — Azure Data Factory orchestrates data movement and transformation..
What is the correct answer to this question?
The correct answer is: A: Azure Data Factory — Azure Data Factory (ADF) is the correct choice because it provides native orchestration and scheduling capabilities for data pipelines. It supports PolyBase as a sink to load data into Azure Synapse dedicated SQL pool in parallel, and it can directly read CSV files from ADLS Gen2. ADF's built-in triggers allow you to schedule the daily incremental load without additional coding.
What should I do if I get this DP-900 question wrong?
Review azure Data Factory orchestrates data movement and transformation., then practise related DP-900 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Azure Data Factory orchestrates data movement and transformation.
About these practice questions
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Same concept, more angles
2 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 uses Azure Synapse Analytics dedicated SQL pool for its data warehouse. Every night, they need to load 500 GB of new sales data from CSV files stored in Azure Data Lake Storage Gen2. The loading process must be automated, scheduled, and include error handling (e.g., skip corrupt rows and log them). Which Azure service should be used to orchestrate this load pipeline?
medium- ✓ A.Azure Data Factory
- B.Azure Stream Analytics
- C.Azure HDInsight
- D.Azure Logic Apps
Why A: Azure Data Factory (ADF) is the correct choice because it is a cloud-based ETL and data orchestration service that supports scheduled execution, error handling (e.g., skipping corrupt rows via fault tolerance settings in the Copy activity), and native integration with Azure Data Lake Storage Gen2 and Azure Synapse dedicated SQL pool. ADF can automate the nightly 500 GB load using a trigger, and its mapping data flows or Copy activity can log errors to a separate file or table, meeting the requirement for automated, scheduled, and error-tolerant ingestion.
Variation 2. A company has a data warehouse in Azure Synapse Analytics dedicated SQL pool. They need to load new sales data every night from a CSV file stored in Azure Data Lake Storage Gen2. The load process must be automated, scheduled, and have error handling for failed loads. Which Azure service should they use to orchestrate this process?
medium- ✓ A.Azure Data Factory
- B.Azure Stream Analytics
- C.Azure Databricks
- D.Azure Logic Apps
Why A: Azure Data Factory is the correct choice because it is a cloud-based ETL service designed specifically for orchestrating and automating data movement and transformation at scale. It supports scheduled triggers, native connectors to Azure Data Lake Storage Gen2 and Azure Synapse Analytics, and built-in error handling via retry policies and failure activities, making it ideal for nightly CSV file loads.
Last reviewed: Jun 11, 2026
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