- A
Use Azure Databricks with a serverless cluster for transformations and load into Azure SQL Database.
Why wrong: Azure Databricks can run Spark transformations but the target in the scenario is Azure Synapse Analytics, not Azure SQL Database. Synapse offers better integration for large-scale analytics.
- B
Use Azure Synapse Analytics serverless Spark pools for transformations and load into the Synapse dedicated SQL pool.
Synapse Analytics provides serverless Spark pools that automatically scale and can read from ADLS Gen2. The transformed data can be loaded into the dedicated SQL pool for high-performance queries, all within a single integrated service.
- C
Use Azure Data Factory with a Spark activity to run transformations and load into Azure Synapse Analytics.
Why wrong: Azure Data Factory can orchestrate pipelines and run Spark activities on HDInsight or Databricks, but it does not provide a serverless Spark compute itself. It would require managing a separate Spark cluster.
- D
Use Azure HDInsight with Apache Spark for transformations and load into Azure Blob Storage.
Why wrong: HDInsight requires managing a cluster (non-serverless) and the target should be Azure Synapse Analytics, not Blob Storage. This option does not meet the serverless requirement.
Quick Answer
The answer is to use Azure Synapse Analytics serverless Spark pools for transformations and then load into the Synapse dedicated SQL pool. This combination is correct because serverless Spark pools allow the team to run Apache Spark transformations on the Parquet files without provisioning or managing any clusters, directly addressing the need for a serverless compute option. The transformed data is then loaded into the dedicated SQL pool, which is specifically designed for high-performance reporting and large-scale analytics workloads. On the DP-900 exam, this question tests your understanding of how serverless and dedicated resources work together within Azure Synapse Analytics; a common trap is confusing serverless SQL pools with serverless Spark pools, but remember that Spark is for transformation code, while the dedicated SQL pool is for optimized storage and query performance. A helpful memory tip: “Spark transforms, Dedicated reports” — serverless Spark handles the heavy lifting of code, and the dedicated pool handles the fast querying.
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 is building a batch analytics pipeline. Raw clickstream data is stored as Parquet files in Azure Data Lake Storage Gen2. The team needs to transform the data using Apache Spark (Python code) and then load the results into Azure Synapse Analytics for high-performance reporting. They want to use a serverless compute option for Spark to avoid managing clusters. Which combination of Azure services should they use for the transformation and loading?
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
Use Azure Synapse Analytics serverless Spark pools for transformations and load into the Synapse dedicated SQL pool.
Option B is correct because Azure Synapse Analytics serverless Spark pools provide a serverless compute option for running Apache Spark transformations without managing clusters, and the transformed data can be directly loaded into the Synapse dedicated SQL pool for high-performance reporting. This combination meets all requirements: serverless Spark for transformations, and Synapse dedicated SQL pool for optimized analytics workloads.
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.
- ✗
Use Azure Databricks with a serverless cluster for transformations and load into Azure SQL Database.
Why it's wrong here
Azure Databricks can run Spark transformations but the target in the scenario is Azure Synapse Analytics, not Azure SQL Database. Synapse offers better integration for large-scale analytics.
- ✓
Use Azure Synapse Analytics serverless Spark pools for transformations and load into the Synapse dedicated SQL pool.
Why this is correct
Synapse Analytics provides serverless Spark pools that automatically scale and can read from ADLS Gen2. The transformed data can be loaded into the dedicated SQL pool for high-performance queries, all within a single integrated service.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Azure Data Factory with a Spark activity to run transformations and load into Azure Synapse Analytics.
Why it's wrong here
Azure Data Factory can orchestrate pipelines and run Spark activities on HDInsight or Databricks, but it does not provide a serverless Spark compute itself. It would require managing a separate Spark cluster.
- ✗
Use Azure HDInsight with Apache Spark for transformations and load into Azure Blob Storage.
Why it's wrong here
HDInsight requires managing a cluster (non-serverless) and the target should be Azure Synapse Analytics, not Blob Storage. This option does not meet the serverless requirement.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Azure Synapse Analytics serverless Spark pools (which are serverless) with Azure Data Factory's Spark activity (which requires a managed cluster), or assume that any Spark service (like HDInsight) can be serverless, when only Synapse serverless Spark pools and Databricks serverless clusters offer true serverless compute.
Trap categories for this question
Scenario analysis trap
Azure Databricks can run Spark transformations but the target in the scenario is Azure Synapse Analytics, not Azure SQL Database. Synapse offers better integration for large-scale analytics.
Detailed technical explanation
How to think about this question
Azure Synapse Analytics serverless Spark pools are built on Apache Spark and automatically scale compute resources based on workload, eliminating cluster management. The dedicated SQL pool in Synapse uses massively parallel processing (MPP) architecture to distribute data across 60 distributions, enabling sub-second query performance on large datasets. In practice, the team would write PySpark code in a Synapse notebook, transform the Parquet data, and use the Synapse connector to write results directly to the dedicated SQL pool via PolyBase or COPY INTO for efficient bulk loading.
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
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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: Use Azure Synapse Analytics serverless Spark pools for transformations and load into the Synapse dedicated SQL pool. — Option B is correct because Azure Synapse Analytics serverless Spark pools provide a serverless compute option for running Apache Spark transformations without managing clusters, and the transformed data can be directly loaded into the Synapse dedicated SQL pool for high-performance reporting. This combination meets all requirements: serverless Spark for transformations, and Synapse dedicated SQL pool for optimized analytics workloads.
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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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.
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