DP-900 Describe an analytics workload on Azure Practice Question
A data engineer needs to transform large datasets stored in Azure Data Lake Storage Gen2 using Python and Apache Spark. They want a serverless compute option that automatically scales and requires no cluster management. Which Azure service should they use?
⚠ Common exam trap
A common mix-up: candidates confuse 'serverless' with 'interactive clusters' in Azure Databricks, assuming that Databricks offers a serverless option (which it does not for interactive clusters), or they mistakenly think a dedicated SQL pool can run Spark transformations.
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 Synapse Analytics serverless Spark pool
Azure Synapse Analytics serverless Spark pool is correct because it provides a serverless Apache Spark compute environment that automatically scales based on workload demand and requires no cluster management. This aligns perfectly with the requirement to transform large datasets in Azure Data Lake Storage Gen2 using Python and Spark without provisioning or managing infrastructure.
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
Azure Synapse Analytics dedicated SQL pool is a relational data warehouse engine that runs T-SQL queries on provisioned, always-on dedicated compute nodes. It is optimized for structured data warehousing workloads such as large-scale aggregations and joins, but it cannot execute Spark code or perform Spark-based data transformations. Its resource model also incurs cost even when idle, making it a poor fit for on-demand transformation of files in Azure Data Lake Storage Gen2.
- ✗
Azure Databricks with interactive clusters
Why it's wrong here
Azure Databricks with interactive clusters can run Spark jobs against Azure Data Lake Storage Gen2, but the clusters are user-managed: you must create, size, start, stop, and carefully monitor them, and they often remain provisioned while idle, accruing compute charges. This operational overhead is substantial compared with fully serverless options that auto-provision and auto-terminate resources. Therefore, while capable, interactive clusters are not the most cost-effective or hands-off choice for ad-hoc transformations in this scenario.
- ✓
Azure Synapse Analytics serverless Spark pool
Why this is correct
Azure Synapse Analytics serverless Spark pool is a fully managed, serverless Spark environment that automatically provisions and scales compute on demand without requiring any cluster setup or management. You can submit PySpark, Scala, or Spark SQL jobs directly against files in Azure Data Lake Storage Gen2, and you pay only for the seconds the job executes. This makes it the ideal service for ad-hoc, large-scale data transformations with zero infrastructure overhead.
- ✗
Azure Data Factory with MapReduce
Why it's wrong here
Azure Data Factory is a cloud data integration and orchestration service, not a data transformation engine that natively runs Spark or MapReduce code. MapReduce is a legacy distributed processing paradigm from Hadoop eras and is not supported as a serverless compute model within ADF; you would need to orchestrate external HDInsight or other clusters to run such jobs. Consequently, it does not provide the managed, serverless Spark capability required to transform large datasets directly in Azure Data Lake Storage Gen2.
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 |
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Data Lake Storage Gen2
Data Lake Storage Gen2 is a cloud-based storage service that combines a scalable data lake with enterprise-grade file system capabilities for big data analytics.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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Senior Network & Security Engineer · founder of Courseiva
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