DP-900 Describe an analytics workload on Azure Practice Question
Which TWO Azure services can be used to perform interactive data analytics on large datasets without managing infrastructure? (Choose two.)
⚠ Common exam trap
Watch out — candidates often confuse Azure Data Lake Storage Gen2 as an analytics service rather than a storage service, or mistake Azure Data Factory's orchestration capabilities for interactive querying, leading them to select options that do not provide direct interactive analytics.
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 SQL pool
Azure Synapse Analytics Serverless SQL pool (A) is correct because it lets you run T-SQL queries directly against files in Azure Data Lake Storage using a pay-per-query, on-demand model with no cluster provisioning or infrastructure management, making it ideal for interactive exploration of large datasets. Azure Databricks (C) is correct because it provides an Apache Spark-based, fully managed analytics platform where the service handles cluster provisioning, scaling, and maintenance, enabling interactive notebooks and SQL analytics on large datasets without the user managing infrastructure. Azure SQL Database (B) is a managed relational database for transactional/OLTP workloads rather than an interactive big-data analytics service, so it does not fit. Azure Data Factory (D) is a data integration and orchestration service for pipelines and ETL/ELT movement, not an interactive analytics engine. Azure Data Lake Storage Gen2 (E) is a storage layer for holding data, not a compute/analytics service that performs interactive queries.
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 Serverless SQL pool
Why this is correct
Azure Synapse Analytics Serverless SQL pool is a PaaS analytics engine that lets you run interactive T-SQL queries directly against files in Azure Data Lake Storage, without provisioning or managing any infrastructure. Because it charges per terabyte of data processed and scales automatically, it is well suited for exploratory and on-demand analytical workloads. Its ability to query diverse file formats like Parquet, CSV, and JSON in place makes it a true interactive analytics service.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is an as-a-service relational database engine optimized for online transaction processing (OLTP), not for interactive analytical queries over large-scale data warehouses or data lakes. Its provisioned compute and storage limits make it expensive and inefficient for scanning billions of rows, and it lacks native integration with file formats like Parquet for ad-hoc serverless analysis. Therefore, while you can run some queries, it is not designed as an interactive analytics service.
- ✓
Azure Databricks
Why this is correct
Azure Databricks is a managed Apache Spark platform that offers an interactive workspace with notebooks and shared clusters, allowing data scientists and analysts to run ad-hoc queries, data exploration, and machine learning workloads at scale. It can read from many sources including Azure Data Lake Storage and provides instant feedback through multi-language support for SQL, Python, Scala, and R. This fits the definition of interactive analytics because users can iteratively query and visualize large datasets without traditional ETL.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is a cloud-based data integration and orchestration service used to create pipelines that ingest, transform, and move data between more than 100 supported sources and destinations. It does not host a query engine or interactive workspace; instead, it triggers activities that can invoke analytics services like Synapse or Databricks. Since it does not execute analytical queries itself, it cannot itself be used to perform interactive analytics.
- ✗
Azure Data Lake Storage Gen2
Why it's wrong here
Azure Data Lake Storage Gen2 is a highly scalable storage service that combines a hierarchical namespace with low-cost capacity for structured and unstructured big data. Although it is commonly used as the data repository for analytics solutions, it does not provide a compute or query engine, nor does it expose an interactive notebook or SQL endpoint. It is an inert data store, so it cannot perform interactive analytics without an external processing service.
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
Azure Data Lake Storage Gen2 Hierarchical Namespace
Key term
Relational database
A relational database organizes data into tables with rows and columns, where each table relates to others using unique keys, allowing efficient storage, retrieval, and manipulation of structured information.
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.
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Senior Network & Security Engineer · founder of Courseiva
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