Question 266 of 820
DP-900 Describe an analytics workload on Azure Practice Question
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?
⚠ Common exam trap
Many candidates 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.
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.
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.
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 serverless clusters do provide automatic scaling and a serverless compute model, but the scenario explicitly targets Azure Synapse Analytics as the serving layer. Loading into Azure SQL Database would require an extra export step and loses the tight integration, shared metadata, and high-performance query engine that Synapse dedicated SQL pools offer for large-scale analytics workloads.
- ✓
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.
- ✗
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 and monitor pipelines, and its Spark activity runs on either Azure HDInsight or Azure Databricks, not on a serverless Spark compute that Data Factory itself manages. This means you still have to provision, configure, and scale a separate Spark cluster, which does not meet the serverless requirement and adds operational overhead beyond the desired fully managed Synapse experience.
- ✗
Use Azure HDInsight with Apache Spark for transformations and load into Azure Blob Storage.
Why it's wrong here
Azure HDInsight with Apache Spark gives you full control over the Spark environment, but it also requires you to manually manage the cluster lifecycle, including scaling and patching, so it is not serverless. Moreover, loading results into Azure Blob Storage rather than into the Synapse dedicated SQL pool fails the stated analytics target and would force downstream consumers to query files directly instead of using Synapse's indexed, partitioned tables for high-performance analytics.
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 |
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Last reviewed: Jun 11, 2026
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