Question 86 of 820
DP-900 Describe an analytics workload on Azure Practice Question
A data analyst needs to run interactive SQL queries on a large dataset stored as CSV files in Azure Blob Storage. The analyst wants to explore the data using T-SQL without loading the data into a database. Which Azure service should they use?
⚠ Common exam trap
Test-takers frequently confuse Azure Synapse Serverless SQL pool with Azure SQL Database, assuming both require data loading, but the serverless pool is specifically designed for external data querying without ingestion.
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 correct because it allows you to run interactive T-SQL queries directly against CSV files in Azure Blob Storage without loading the data into a database. It uses a pay-per-query model and leverages the OPENROWSET function to query external data in place, making it ideal for ad-hoc exploration of large datasets.
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 SQL Database
Why it's wrong here
Azure SQL Database is a fully managed relational database platform that requires provisioning a database, creating a schema, and importing CSV data into tables before any query can run. It does not natively support T-SQL queries that directly read CSV files from Azure Blob Storage via OPENROWSET or similar file access, and its pricing is based on provisioned DTUs or vCores—a poor fit for cost-effective, ad-hoc exploration of raw files already in storage.
- ✓
Azure Synapse Serverless SQL pool
Why this is correct
Azure Synapse Serverless SQL pool is the correct choice because it allows an analyst to run interactive T-SQL queries directly against CSV files residing in Azure Blob Storage (or Data Lake Gen2) using the OPENROWSET function, with no data loading and no provisioning of infrastructure. Compute is billed per terabyte of data read, so the analyst pays only for the exact amount of data scanned per query, making it ideal for ad-hoc exploration and lightweight reporting on files that are already stored in the cloud.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is primarily an integration and orchestration service for building ETL/ELT pipelines; it copies, injects, and transforms data across many sources and sinks, and while you can define SQL activities inside pipelines, it does not provide an interactive SQL query surface. Running an ad-hoc query on a CSV file would require creating a pipeline, defining linked services, datasets, and a Copy or Lookup activity, which is heavy and not meant for repeated interactive exploration.
- ✗
Azure Stream Analytics
Why it's wrong here
Azure Stream Analytics is built to process live, streaming data—such as events from Azure Event Hubs or IoT Hub—by running a continuously executing SQL-like job over finite time windows. It is not designed for on-demand, interactive queries against static CSV files in Blob Storage; such files would need to be streamed as an input and result jobs that stop, and the cost model reflects a continuously running service, not a pay-per-query analytics engine.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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Last reviewed: Jun 11, 2026
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