DP-900 Describe an analytics workload on Azure Practice Question
A data analyst needs to run ad-hoc SQL queries on large datasets stored as Parquet files in Azure Data Lake Storage Gen2. The queries are infrequent and the data volume varies. The analyst wants to pay only for the amount of data processed per query and does not want to manage any infrastructure. They also need to create views in T-SQL to simplify queries for Power BI reports. Which Azure service should they use?
⚠ Common exam trap
Many candidates confuse Azure Data Lake Analytics (which also processes data in ADLS Gen2) with a serverless SQL option, but it does not support T-SQL or views, making it unsuitable for the analyst's requirement to create T-SQL views for Power BI.
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 Serverless SQL pool
Azure Synapse Serverless SQL pool is the correct choice because it allows running ad-hoc T-SQL queries directly on Parquet files in Azure Data Lake Storage Gen2 without provisioning any infrastructure. It uses a pay-per-query model, charging only for the amount of data processed, and supports creating T-SQL views that can be used directly by Power BI for simplified reporting.
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 Serverless SQL pool
Why this is correct
Azure Synapse Serverless SQL pool is the correct choice because it lets the analyst run standard T-SQL queries directly against files in Azure Data Lake Storage using the OPENROWSET function or external tables, with no infrastructure to provision. It is truly serverless: compute scales automatically and billing is per-query on data processed, which fits ad hoc exploration of large datasets. It also supports creating views for reuse and connects directly to Power BI, making it a low-friction, cost-effective solution for interactive SQL analysis.
- ✗
Azure Data Lake Analytics
Why it's wrong here
Azure Data Lake Analytics is a deprecated service that required you to write U-SQL, a hybrid language combining SQL syntax with C# expressions, rather than standard T-SQL. It was also job-oriented: you submitted batch jobs that queued and executed on managed compute, which is not the same as running interactive, ad hoc SQL queries. Because it is retired/unavailable to new customers and does not support the T-SQL dialect expected by typical data analysts, it is not a valid answer.
- ✗
Azure HDInsight
Why it's wrong here
Azure HDInsight is an incorrect fit because it is a managed cluster service for open-source frameworks like Hadoop, Spark, or Hive, not a serverless SQL query engine. The analyst would need to provision and maintain a persistent cluster, which incurs continuous compute costs even when no queries are running, and this conflicts with the lightweight, ad hoc SQL scenario. While Hive/Spark can be used for SQL-like queries, HDInsight adds significant cluster-management overhead and does not provide instant, pay-per-query T-SQL access to data lake files.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks primarily uses Spark SQL and DataFrame APIs within notebooks, and while it supports interactive queries, it requires a running cluster with configured compute resources. It is a unified analytics platform rather than a purely serverless SQL service, so for simple ad hoc T-SQL queries over large datasets, it would bring unnecessary complexity and compute management. Databricks is not wrong because it lacks SQL capabilities but because it is a heavier, cluster-dependent tool compared with Azure Synapse Serverless SQL pool's instant, no-cluster query experience.
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
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
Key term
Data lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format until it is needed for analysis.
About these practice questions
One of 820 original DP-900 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.