The answer is Azure Databricks with serverless SQL warehouses. This is the correct choice because serverless SQL warehouses provide a fully managed compute layer that automatically scales to transform data directly in Azure Data Lake Storage Gen2, eliminating the need to provision or manage any clusters. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your understanding of serverless compute options for data transformation—a common trap is confusing Azure Synapse Serverless SQL, which is optimized for querying, not transforming, data lake files. Remember that Databricks serverless SQL warehouses are purpose-built for ad-hoc ETL and transformation workloads without infrastructure overhead. A useful memory tip: think “Databricks does the dirty work of transformation, while Synapse Serverless just looks at the data.”
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 uses Azure Data Lake Storage Gen2 to store raw data files. Data engineers need to transform this data using a serverless approach without managing infrastructure. Which Azure service should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Databricks with serverless SQL warehouses.
Option D is correct because Azure Databricks with serverless SQL warehouses provides a fully serverless compute option for transforming data stored in Azure Data Lake Storage Gen2. It eliminates infrastructure management by automatically scaling compute resources based on workload demands, making it ideal for ad-hoc transformations without provisioning clusters.
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 it's wrong here
Data Factory data flows are serverless but more for ETL orchestration, not ad-hoc transformations.
✗
Azure HDInsight with Apache Spark clusters.
Why it's wrong here
HDInsight requires managing cluster lifecycle.
✗
Azure Synapse Analytics with serverless SQL pool.
Why it's wrong here
Serverless SQL pool is serverless but limited to SQL-based transformations.
✓
Azure Databricks with serverless SQL warehouses.
Why this is correct
Serverless SQL warehouses provide on-demand compute without infrastructure management.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'serverless SQL pool' in Synapse with a general-purpose transformation tool, but it is limited to SQL-based queries and lacks the flexibility of Databricks for complex, multi-step data engineering pipelines.
Detailed technical explanation
How to think about this question
Azure Databricks serverless SQL warehouses use a shared, multi-tenant compute layer that auto-scales from zero to thousands of nodes within seconds, leveraging Photon engine for high-performance query execution on Delta Lake tables. Under the hood, it decouples compute from storage, allowing transformations to run directly on data in ADLS Gen2 via the Delta format, which provides ACID transactions and schema enforcement. In a real-world scenario, a data engineer can run a Python or SQL transformation script on billions of rows without ever configuring a cluster, paying only for the compute seconds consumed.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
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 Databricks with serverless SQL warehouses. — Option D is correct because Azure Databricks with serverless SQL warehouses provides a fully serverless compute option for transforming data stored in Azure Data Lake Storage Gen2. It eliminates infrastructure management by automatically scaling compute resources based on workload demands, making it ideal for ad-hoc transformations without provisioning clusters.
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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Question Discussion
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