- A
Azure Synapse Analytics dedicated SQL pool
Why wrong: Dedicated SQL pool is provisioned compute, not serverless. It charges for the provisioned resources regardless of usage, and does not auto-scale seamlessly.
- B
Azure Databricks with auto-scaling clusters
Azure Databricks offers auto-scaling clusters and serverless compute options that scale down to zero, charging per second for the resources consumed, ideal for batch transformations on data lakes.
- C
Azure Data Factory with mapping data flows
Why wrong: Mapping data flows are serverless and scale automatically, but they are best for ETL transformations with a visual interface. For complex custom logic (e.g., multi-step aggregations and joins), Databricks provides more flexibility.
- D
Azure Stream Analytics
Why wrong: Stream Analytics is designed for real-time stream processing, not batch processing of large volumes of data stored in a data lake.
Quick Answer
The answer is Azure Databricks with auto-scaling clusters, as it provides a serverless Spark environment purpose-built for batch ETL on Azure Data Lake Storage Gen2. This service automatically scales compute resources based on workload demand and charges per second, making it ideal for the described pipeline of aggregations, joins, and Parquet output. On the DP-900 exam, this question tests your understanding of which Azure service offers serverless Apache Spark for batch processing, often contrasting it with Azure Synapse Analytics (which uses dedicated SQL pools or Spark pools that may not be truly serverless) or Azure HDInsight (which requires manual cluster management). A common trap is choosing Azure Data Factory, but that is an orchestration tool, not a Spark compute engine. Memory tip: think "Databricks = Spark + serverless + per-second billing" for any batch ETL scenario involving complex transformations on data lakes.
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 build a batch processing pipeline that transforms large volumes of sales data stored in Azure Data Lake Storage Gen2. The transformations include aggregations and joins, and the output should be stored back in the data lake as Parquet files. The team wants a serverless compute option that automatically scales and charges per second. Which Azure service should they use?
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 Databricks with auto-scaling clusters
Azure Databricks with auto-scaling clusters is the correct choice because it provides a serverless, Apache Spark-based compute platform that automatically scales resources based on workload demand and charges per second (via serverless or low-concurrency modes). It is ideal for batch processing large volumes of data in Azure Data Lake Storage Gen2, supporting complex transformations like aggregations and joins, and can write output directly as Parquet files.
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 Synapse Analytics dedicated SQL pool
Why it's wrong here
Dedicated SQL pool is provisioned compute, not serverless. It charges for the provisioned resources regardless of usage, and does not auto-scale seamlessly.
- ✓
Azure Databricks with auto-scaling clusters
Why this is correct
Azure Databricks offers auto-scaling clusters and serverless compute options that scale down to zero, charging per second for the resources consumed, ideal for batch transformations on data lakes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure Data Factory with mapping data flows
Why it's wrong here
Mapping data flows are serverless and scale automatically, but they are best for ETL transformations with a visual interface. For complex custom logic (e.g., multi-step aggregations and joins), Databricks provides more flexibility.
- ✗
Azure Stream Analytics
Why it's wrong here
Stream Analytics is designed for real-time stream processing, not batch processing of large volumes of data stored in a data lake.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Data Factory mapping data flows (which is also serverless and scales automatically) with a Spark-based batch processing service, but Data Factory charges per execution duration and lacks native Spark API support for complex joins and aggregations at the same performance level as Databricks.
Detailed technical explanation
How to think about this question
Under the hood, Azure Databricks leverages Apache Spark's distributed computing engine to parallelize transformations across a cluster, with auto-scaling adjusting the number of worker nodes based on task queue depth. The serverless option (e.g., Databricks Serverless SQL or Serverless Real-Time Inference) eliminates cluster management overhead, while the per-second billing model (e.g., Databricks Jobs with serverless compute) ensures cost efficiency for variable workloads. In practice, this is critical for pipelines that process terabytes of sales data daily, where idle time between jobs would otherwise incur costs.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Describe an analytics workload on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe an analytics workload on Azure practice questions
Targeted practice on this topic area only
- →
All DP-900 questions
982 questions across all exam domains
- →
Microsoft Azure Data Fundamentals DP-900 study guide
Full concept coverage aligned to exam objectives
- →
DP-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related DP-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe core data concepts practice questions
Practise DP-900 questions linked to Describe core data concepts.
Describe an analytics workload on Azure practice questions
Practise DP-900 questions linked to Describe an analytics workload on Azure.
Identify considerations for relational data on Azure practice questions
Practise DP-900 questions linked to Identify considerations for relational data on Azure.
Describe considerations for working with non-relational data on Azure practice questions
Practise DP-900 questions linked to Describe considerations for working with non-relational data on Azure.
DP-900 fundamentals practice questions
Practise DP-900 questions linked to DP-900 fundamentals.
DP-900 scenario practice questions
Practise DP-900 questions linked to DP-900 scenario.
DP-900 troubleshooting practice questions
Practise DP-900 questions linked to DP-900 troubleshooting.
Practice this exam
Start a free DP-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Databricks with auto-scaling clusters — Azure Databricks with auto-scaling clusters is the correct choice because it provides a serverless, Apache Spark-based compute platform that automatically scales resources based on workload demand and charges per second (via serverless or low-concurrency modes). It is ideal for batch processing large volumes of data in Azure Data Lake Storage Gen2, supporting complex transformations like aggregations and joins, and can write output directly as Parquet files.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 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 data engineering team needs to build a batch ETL pipeline that transforms large volumes of clickstream data stored as CSV files in Azure Data Lake Storage Gen2. The transformations require running distributed Python and Scala code using Apache Spark. The transformed data will be loaded into a data warehouse for reporting. The team wants a serverless compute environment that automatically scales and charges per second. Which Azure service should they use to run the Spark transformations?
medium- ✓ A.Azure Synapse Analytics (Spark pools)
- B.Azure Data Factory
- C.Azure Stream Analytics
- D.Azure Analysis Services
Why A: Azure Synapse Analytics (Spark pools) is the correct choice because it provides a serverless Apache Spark compute environment that automatically scales and charges per second, perfectly matching the requirement for running distributed Python and Scala transformations on large volumes of clickstream data stored in Azure Data Lake Storage Gen2. The service integrates directly with the data lake and can load transformed results into a dedicated SQL pool for data warehouse reporting.
Last reviewed: Jun 11, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.