Serverless Data Transformation Services in Azure
Which TWO Azure services can be used to perform large-scale data transformation and processing in a serverless manner?
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.
⚠ Common exam trap
A common mix-up: candidates 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.
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 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.
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 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.
- ✓
Azure Data Factory
Why this is correct
Data Factory is a serverless data integration service.
- ✗
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.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Data lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format until it is needed for analysis.
Key term
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
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Same concept, more angles
1 more way 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. Which TWO Azure services can be used to perform data transformation in a serverless manner? (Choose two.)
easy- A.Azure Databricks with Apache Spark
- ✓ B.Azure Synapse serverless SQL pool
- ✓ C.Azure Data Factory mapping data flows
- D.Azure SQL Database
- E.Azure HDInsight with Hive
Why 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. Azure Data Factory mapping data flows (Option C) is also correct. Mapping data flows provide a visual, code-free way to transform data at scale. While they leverage Spark clusters under the hood, these clusters are fully managed by Azure Data Factory. Users define the transformations, and ADF provisions, scales, and terminates the compute resources automatically, abstracting away server management. This aligns with the serverless paradigm where you pay for execution time and data processed, not for provisioned servers.
JA
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.