The answer is Azure Synapse Serverless SQL pool. This service is the correct choice because it enables direct Parquet querying from Azure Data Lake Storage Gen2 using T-SQL, without requiring any data movement or loading. It leverages a distributed query engine that pushes down computation to the storage layer and exploits Parquet’s columnar format, allowing it to scan petabytes of data and return joined results within seconds. On the Microsoft Azure Data Fundamentals DP-900 exam, this scenario tests your understanding of how serverless SQL pools differ from dedicated SQL pools—the key trap is assuming you need to load data into a database first. Remember the memory tip: “Serverless stays serverless—query in place, no loading space.”
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 financial analytics company has petabytes of transaction data stored as Parquet files in Azure Data Lake Storage Gen2. Data analysts need to run complex SQL queries that join multiple tables and return results within seconds. The company wants to query the data directly without moving it to another store. Which Azure service should they use?
Why wrong: Azure SQL Database is limited in storage capacity and not designed for petabyte-scale analytics or direct querying of data lake files.
B
Azure Synapse Serverless SQL pool
Serverless SQL pool can directly query Parquet files in the data lake using standard T-SQL and scales automatically for large datasets.
C
Azure HDInsight
Why wrong: While HDInsight can process big data, it requires managing clusters and is not a serverless SQL query service.
D
Azure Databricks
Why wrong: Databricks can query data lake files, but it uses Spark SQL or notebooks, not a pure SQL serverless experience, and often requires more setup.
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 querying data directly from Azure Data Lake Storage Gen2 using T-SQL without moving or loading the data. It uses a distributed query engine that can process petabytes of Parquet files and return results in seconds by leveraging pushdown computation and columnar storage formats.
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 SQL Database
Why it's wrong here
Azure SQL Database is limited in storage capacity and not designed for petabyte-scale analytics or direct querying of data lake files.
✓
Azure Synapse Serverless SQL pool
Why this is correct
Serverless SQL pool can directly query Parquet files in the data lake using standard T-SQL and scales automatically for large datasets.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure HDInsight
Why it's wrong here
While HDInsight can process big data, it requires managing clusters and is not a serverless SQL query service.
✗
Azure Databricks
Why it's wrong here
Databricks can query data lake files, but it uses Spark SQL or notebooks, not a pure SQL serverless experience, and often requires more setup.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Synapse Serverless SQL pool with Azure SQL Database, assuming both require data movement, or they overcomplicate the solution by choosing a cluster-based service like HDInsight or Databricks when a serverless query engine is sufficient.
Detailed technical explanation
How to think about this question
Azure Synapse Serverless SQL pool uses a distributed query execution engine that reads data directly from the data lake via the Azure Storage REST API, applying predicate pushdown and partition elimination to minimize data scanned. It supports OPENROWSET and external tables to query Parquet, CSV, and JSON files, and automatically scales compute resources based on query complexity, making it cost-effective for ad-hoc analytics on massive datasets.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
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 Serverless SQL pool — Azure Synapse Serverless SQL pool is correct because it allows querying data directly from Azure Data Lake Storage Gen2 using T-SQL without moving or loading the data. It uses a distributed query engine that can process petabytes of Parquet files and return results in seconds by leveraging pushdown computation and columnar storage formats.
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.
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Question Discussion
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