- A
Azure Synapse Analytics dedicated SQL pool
Why wrong: A dedicated SQL pool requires provisioning a cluster and incurs cost even when not in use. It is not designed for ad-hoc serverless queries over external files.
- B
Azure Synapse Analytics serverless SQL pool
The serverless SQL pool can query data in the data lake directly using T-SQL, scales on demand, and charges per query, fitting the requirement of no provisioning and no data movement.
- C
Azure Databricks
Why wrong: Azure Databricks uses Apache Spark and SQL-like syntax (Spark SQL) rather than standard T-SQL. It requires cluster management and is not a serverless T-SQL solution.
- D
Azure HDInsight
Why wrong: Azure HDInsight is a managed cluster service for big data frameworks (e.g., Spark, Hive). It requires provisioning and does not offer serverless T-SQL querying of data lake files.
Quick Answer
The answer is Azure Synapse Analytics serverless SQL pool. This service is the correct choice because it enables you to run ad-hoc SQL queries directly against Parquet files in Azure Data Lake Storage Gen2 using standard T-SQL syntax, without provisioning any dedicated cluster or moving data into a separate database. It automatically scales compute resources to handle complex joins and aggregations on terabytes of data, charging only for the data processed, which perfectly matches the need for serverless, on-demand querying. On the Microsoft Azure Data Fundamentals DP-900 exam, this scenario tests your understanding of when to use serverless versus dedicated SQL pools—a common trap is choosing a provisioned service like dedicated SQL pool or Azure SQL Database, which require cluster management and data movement. Remember the key distinction: serverless means no provisioning, just pay-per-query. For a quick memory tip, think "serverless SQL for serverless data"—if you don't want to manage servers, use the serverless pool.
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company stores terabytes of historical sales data as Parquet files in Azure Data Lake Storage Gen2. Business analysts need to run ad-hoc SQL queries that involve complex joins and aggregations over this data. They want to avoid provisioning a dedicated cluster or moving data into a separate database. The queries must be executed using standard T-SQL syntax. Which Azure service should they use?
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 (B) is the correct choice because it allows you to query Parquet files directly in Azure Data Lake Storage Gen2 using standard T-SQL syntax without provisioning any dedicated cluster or moving data. It automatically scales compute resources to handle complex joins and aggregations on terabytes of data, charging only for the data processed. This matches the requirement for ad-hoc, serverless querying with familiar T-SQL.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
A dedicated SQL pool requires provisioning a cluster and incurs cost even when not in use. It is not designed for ad-hoc serverless queries over external files.
- ✓
Azure Synapse Analytics serverless SQL pool
Why this is correct
The serverless SQL pool can query data in the data lake directly using T-SQL, scales on demand, and charges per query, fitting the requirement of no provisioning and no data movement.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks uses Apache Spark and SQL-like syntax (Spark SQL) rather than standard T-SQL. It requires cluster management and is not a serverless T-SQL solution.
- ✗
Azure HDInsight
Why it's wrong here
Azure HDInsight is a managed cluster service for big data frameworks (e.g., Spark, Hive). It requires provisioning and does not offer serverless T-SQL querying of data lake files.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'serverless SQL pool' with 'dedicated SQL pool' (Option A), assuming both require provisioning, or they mistakenly think Databricks (Option C) supports standard T-SQL, when it actually uses Spark SQL or Python.
Detailed technical explanation
How to think about this question
Under the hood, Azure Synapse serverless SQL pool uses a distributed query engine that reads Parquet files directly via the Azure Storage REST API, leveraging columnar storage and predicate pushdown to minimize data scanned. It supports external tables and OPENROWSET to query files without schema definition, and its T-SQL compatibility includes window functions, CTEs, and complex joins, making it ideal for data lake exploration. In real-world scenarios, analysts can run queries like 'SELECT ... FROM OPENROWSET(BULK ...)' against years of sales data without any data movement, paying only for the gigabytes of data processed per query.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Questions learners often ask
What does this DP-900 question test?
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Synapse Analytics serverless SQL pool — Azure Synapse Analytics serverless SQL pool (B) is the correct choice because it allows you to query Parquet files directly in Azure Data Lake Storage Gen2 using standard T-SQL syntax without provisioning any dedicated cluster or moving data. It automatically scales compute resources to handle complex joins and aggregations on terabytes of data, charging only for the data processed. This matches the requirement for ad-hoc, serverless querying with familiar T-SQL.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Same concept, more angles
3 more ways this is tested on DP-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A financial services company stores years of market trade data as Parquet files in Azure Data Lake Storage Gen2. The data volume is terabytes and growing rapidly. Data analysts need to run complex SQL queries that join multiple tables (e.g., trades, instruments, counterparties) and return results within seconds. The company also wants to integrate with Power BI for visualization and Azure Data Factory for orchestration of ETL pipelines. Which Azure service should they choose as the primary analytics platform?
hard- A.Azure SQL Database
- ✓ B.Azure Synapse Analytics (serverless SQL pool)
- C.Azure HDInsight with Spark
- D.Azure Analysis Services
Why B: Azure Synapse Analytics serverless SQL pool is the correct choice because it provides a distributed SQL query engine that can directly query Parquet files in Azure Data Lake Storage Gen2 using T-SQL, enabling complex joins across multiple tables with fast performance via automatic query optimization and pushdown computation. It integrates natively with Power BI for visualization and Azure Data Factory for ETL orchestration, making it the ideal primary analytics platform for large-scale, schema-on-read data lake scenarios.
Variation 2. A financial services company needs to run ad-hoc SQL queries on petabytes of data stored in Azure Data Lake Storage without provisioning a dedicated data warehouse. Which Azure service should they use?
hard- ✓ A.Azure Synapse Analytics serverless SQL pool
- B.Azure Analysis Services
- C.Azure SQL Database
- D.Azure Data Lake Storage
Why A: Azure Synapse Analytics serverless SQL pool is the correct choice because it allows you to run ad-hoc SQL queries directly against data in Azure Data Lake Storage without provisioning any dedicated compute resources. It uses a pay-per-query model, automatically scaling compute to handle petabytes of data, making it ideal for intermittent, exploratory workloads.
Variation 3. A retail company stores historical sales data from multiple stores in Azure Data Lake Storage Gen2 as CSV files. They need to run complex SQL queries that join and aggregate data across multiple files to generate weekly sales reports. They want a serverless query service that can directly query the data in the lake without loading it into a separate database. Which Azure service should they use?
medium- A.Azure SQL Database
- ✓ B.Azure Synapse Serverless SQL pool
- C.Azure Stream Analytics
- D.Azure Data Factory
Why B: Azure Synapse Serverless SQL pool is the correct choice because it provides a serverless, on-demand SQL query engine that can directly query CSV files stored in Azure Data Lake Storage Gen2 using T-SQL syntax. It supports complex joins and aggregations across multiple files without requiring data movement or loading into a separate database, making it ideal for ad-hoc reporting on data lakes.
Last reviewed: Jun 11, 2026
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