DP-900 Describe core data concepts Practice Question
Exhibit
Storage account: mysalesdata Container: transactions Blob: orders.csv Metadata: Content-Type: text/csv DateCreated: 2025-01-15
Refer to the exhibit. A data engineer needs to query the orders.csv file using Azure Synapse Serverless SQL. What is the most efficient way to access this data?
⚠ Common exam trap
Candidates often confuse PolyBase (which is for dedicated SQL pools) with Serverless SQL's OPENROWSET, or assume that data must be moved to a database before querying, missing the serverless paradigm of query-in-place.
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 OPENROWSET in Serverless SQL
Azure Synapse Serverless SQL is designed for on-demand querying of data stored in data lakes without provisioning storage. The OPENROWSET function with the BULK option allows direct querying of CSV files using T-SQL, making it the most efficient method for ad-hoc analysis of the orders.csv file without data movement or schema management.
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 PolyBase to create external table
Why it's wrong here
PolyBase is a data virtualization feature that creates external tables over Azure Storage, but in Azure Synapse Analytics it is implemented in the dedicated SQL pool—not in the serverless SQL engine. To use it, you must first provision a dedicated SQL pool (billed per second), create an external data source, an external file format, and credential objects, then define an external table before querying. That setup is unnecessary for a quick ad-hoc query against order files, because serverless SQL's OPENROWSET can read the same files directly with no metadata creation.
- ✓
Use OPENROWSET in Serverless SQL
Why this is correct
OPENROWSET is a T-SQL function available in the built-in serverless SQL endpoint that reads files directly from Azure Data Lake or Blob storage without loading them into a database. You can query CSV, Parquet, JSON, and Delta Lake files by specifying a path and optional WITH clause for schema; the engine processes only the requested data and you pay only for the data scanned. This is the optimal choice for an ad-hoc query because no compute pool, external table, or pipeline must be provisioned beforehand.
- ✗
Copy data to Azure SQL Database using ADF
Why it's wrong here
Using Azure Data Factory Copy Activity to move order data into Azure SQL Database means you are building a data ingestion pipeline that creates a destination table, executes a copy job, and then requires maintenance if the source files are updated. The data would be a point-in-time snapshot, so any new files in the storage account would not be visible until you rerun the pipeline. For an immediate ad-hoc query, this adds latency and incurs costs for ADF activity and SQL DB storage, making it far less efficient than querying the source files directly.
- ✗
Load data into a dedicated SQL pool
Why it's wrong here
A dedicated SQL pool is a provisioned cluster that charges per second regardless of query activity; before querying data you must either load it with PolyBase/COPY statements or create external tables, both of which require a running pool and careful distribution design. Loading order data into a dedicated pool is a heavy operation that is justified only for large, repeated, low-latency analytical workloads, not for one-off queries. This approach adds provisioning time, load time, and ongoing compute cost, whereas serverless SQL handles the same query instantly without any infrastructure.
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
Schema
A schema is a blueprint or logical structure that defines how data is organized, stored, and accessed in a database or information system.
About these practice questions
This DP-900 question is part of Courseiva's 820-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. 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.