Quick Answer
The correct answer is Azure Data Factory and Azure Synapse Serverless SQL pool, as both enable large-scale serverless data transformation and processing without requiring you to provision or manage any underlying compute infrastructure. Azure Data Factory achieves this through its Mapping Data Flows, which allow you to visually design and execute data transformations at scale using a pay-per-execution model, while Azure Synapse Serverless SQL pool lets you run T-SQL queries directly against data in Azure Data Lake or Blob Storage, charging only for the amount of data scanned. On the DP-900 exam, this question tests your understanding of which services fit the “serverless” model for transformation tasks—a common trap is confusing Azure Databricks (which uses provisioned clusters) or Azure Stream Analytics (which is real-time, not batch transformation). Remember the memory tip: “Factory for flow, Synapse for SQL”—if you need to orchestrate and transform data without servers, think Data Factory; if you need to query data in place without a dedicated warehouse, think Synapse Serverless SQL pool.
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 perform large-scale data transformation and processing in a serverless manner?
Azure Analysis Services
Why wrong: Analysis Services is for semantic modeling, not transformations.
Azure Synapse Serverless SQL pool
Serverless SQL pool is serverless for querying data.
Azure Data Factory
Data Factory is a serverless data integration service.
Azure Databricks
Why wrong: Databricks requires cluster provisioning, not serverless.
Azure SQL Database
Why wrong: SQL Database is not serverless for large-scale transformations.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Azure Synapse Serverless SQL pool
Azure Synapse Serverless SQL pool (Option B) is correct because it allows you to run T-SQL queries over data stored in Azure Data Lake or Blob Storage without provisioning any dedicated compute resources, paying only for the data processed. Azure Data Factory (Option C) is correct because it provides a serverless orchestration and data integration service that can execute data transformation activities (like Mapping Data Flows) at scale without managing underlying infrastructure.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Azure Analysis Services
Why it's wrong here
Analysis Services is for semantic modeling, not transformations.
Azure Synapse Serverless SQL pool
Why this is correct
Serverless SQL pool is serverless for querying data.
Related concept
Read the scenario before looking for a memorised answer.
Azure Data Factory
Why this is correct
Data Factory is a serverless data integration service.
Related concept
Read the scenario before looking for a memorised answer.
Azure Databricks
Why it's wrong here
Databricks requires cluster provisioning, not serverless.
Azure SQL Database
Why it's wrong here
SQL Database is not serverless for large-scale transformations.
Common exam traps
The trap here is that candidates often confuse 'serverless' with 'fully managed' or 'PaaS', leading them to select Azure Databricks or Azure SQL Database, which still require explicit compute provisioning or cluster management, unlike the truly serverless models of Synapse Serverless SQL pool and Data Factory.
Detailed technical explanation
Azure Synapse Serverless SQL pool uses a distributed query engine that reads data directly from Parquet, Delta, CSV, or JSON files in Azure Storage, leveraging pushdown predicates and statistics to optimize performance without any persistent storage. Azure Data Factory's Mapping Data Flows execute transformations on Spark clusters that are automatically spun up and torn down per execution, with billing based on the number of Data Flow Debug hours or activity runs. Both services align with the serverless paradigm by abstracting infrastructure management and scaling resources dynamically based on workload demand.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
The correct answer is: Azure Synapse Serverless SQL pool — Azure Synapse Serverless SQL pool (Option B) is correct because it allows you to run T-SQL queries over data stored in Azure Data Lake or Blob Storage without provisioning any dedicated compute resources, paying only for the data processed. Azure Data Factory (Option C) is correct because it provides a serverless orchestration and data integration service that can execute data transformation activities (like Mapping Data Flows) at scale without managing underlying infrastructure.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
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. Which TWO Azure services can be used to perform data transformation in a serverless manner? (Choose two.)
easyWhy B: Azure Synapse serverless SQL pool (Option B) is correct because it allows you to query and transform data directly from data lake files (e.g., Parquet, CSV) using T-SQL without provisioning any dedicated compute resources. It uses a pay-per-query model, making it inherently serverless for data transformation tasks.
Last reviewed: Jun 24, 2026
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