The answer is Azure Synapse Analytics with serverless Spark pools because it provides a fully managed, serverless Apache Spark environment that automatically scales and eliminates the need to manage clusters. This service directly supports transforming large datasets stored in Azure Data Lake Storage Gen2 using Python code with Spark, making it the ideal choice for teams that require no-cluster-management and automatic scaling. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your understanding of how Azure Synapse Analytics offers integrated, on-demand Spark compute without provisioning overhead—a common trap is confusing it with Azure Databricks, which also runs Spark but requires cluster configuration. Remember that “serverless Spark pools” is a specific feature within Synapse Analytics, not a standalone service. A helpful memory tip: think of “Synapse Spark” as “Synapse Spins up automatically”—no pools to pre-configure, just code and scale.
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 data engineering team needs to transform large datasets stored in Azure Data Lake Storage Gen2 using Apache Spark with Python code. They want a fully managed service that provides serverless Spark pools, meaning no clusters to manage and automatic scaling. Which Azure service should they use?
Why wrong: HDInsight requires managing clusters; it is not serverless.
B
Azure Databricks
Why wrong: Databricks can have serverless compute but is a separate platform; in Azure, it is managed but not the same as Azure Synapse's serverless Spark.
C
Azure Synapse Analytics with serverless Spark pools
Correct. Azure Synapse Analytics provides serverless Apache Spark pools that scale automatically and charge per use.
D
Azure Machine Learning
Why wrong: Azure Machine Learning is for ML model training and deployment, not general data transformation.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Synapse Analytics with serverless Spark pools
Azure Synapse Analytics with serverless Spark pools is the correct choice because it provides a fully managed, serverless Apache Spark environment that automatically scales and eliminates the need to manage clusters. This service directly supports transforming large datasets in Azure Data Lake Storage Gen2 using Python code with Spark, meeting the team's requirement for a no-cluster-management, auto-scaling solution.
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 HDInsight
Why it's wrong here
HDInsight requires managing clusters; it is not serverless.
✗
Azure Databricks
Why it's wrong here
Databricks can have serverless compute but is a separate platform; in Azure, it is managed but not the same as Azure Synapse's serverless Spark.
✓
Azure Synapse Analytics with serverless Spark pools
Why this is correct
Correct. Azure Synapse Analytics provides serverless Apache Spark pools that scale automatically and charge per use.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Machine Learning
Why it's wrong here
Azure Machine Learning is for ML model training and deployment, not general data transformation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Databricks as the only serverless Spark option, but Azure Synapse Analytics also offers serverless Spark pools that are fully managed and integrated with Azure Data Lake Storage Gen2, making it the correct answer for this specific scenario.
Detailed technical explanation
How to think about this question
Azure Synapse Analytics serverless Spark pools use Apache Spark 3.x and are integrated with the Synapse SQL engine, allowing seamless querying of transformed data via T-SQL. Under the hood, the service leverages Azure's infrastructure to dynamically allocate executors based on workload, with a default timeout of 30 minutes of idle before auto-pausing to save costs. A real-world scenario is a team processing terabytes of IoT sensor data in ADLS Gen2, where the serverless pool automatically scales from 5 to 50 nodes during peak loads without any manual intervention.
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 Synapse Analytics with serverless Spark pools — Azure Synapse Analytics with serverless Spark pools is the correct choice because it provides a fully managed, serverless Apache Spark environment that automatically scales and eliminates the need to manage clusters. This service directly supports transforming large datasets in Azure Data Lake Storage Gen2 using Python code with Spark, meeting the team's requirement for a no-cluster-management, auto-scaling solution.
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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