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 retail company needs to run complex SQL queries on petabytes of historical sales data stored in Parquet files in Azure Data Lake Storage Gen2. They want a solution that provides fast query performance without managing infrastructure, and they prefer a pay-per-query pricing model. Which Azure service should they use?
Why wrong: Dedicated SQL pool requires provisioning and paying for compute resources even when not in use, and it is not pay-per-query.
B
Azure SQL Database
Why wrong: Azure SQL Database is designed for transactional workloads and is not optimized for petabyte-scale data lake queries with pay-per-query pricing.
C
Azure Synapse Serverless SQL pool
Serverless SQL pool is ideal for querying data lakes with a pay-per-query model and no infrastructure management.
D
Azure HDInsight with Hive
Why wrong: HDInsight requires managing a cluster and is not a serverless solution; it also does not offer pay-per-query pricing.
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 petabytes of Parquet files in Azure Data Lake Storage Gen2 using T-SQL without provisioning any infrastructure, and it charges per terabyte of data processed (pay-per-query). This matches the requirements for fast query performance on historical sales data with a serverless, consumption-based pricing model.
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
Dedicated SQL pool requires provisioning and paying for compute resources even when not in use, and it is not pay-per-query.
✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is designed for transactional workloads and is not optimized for petabyte-scale data lake queries with pay-per-query pricing.
✓
Azure Synapse Serverless SQL pool
Why this is correct
Serverless SQL pool is ideal for querying data lakes with a pay-per-query model and no infrastructure management.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure HDInsight with Hive
Why it's wrong here
HDInsight requires managing a cluster and is not a serverless solution; it also does not offer pay-per-query pricing.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Synapse Analytics dedicated SQL pool (provisioned, always-on) with the serverless SQL pool (pay-per-query), or assume that Azure SQL Database can handle big data analytics on Parquet files, when it is designed for OLTP workloads and lacks native support for querying external data lakes without additional services like PolyBase.
Detailed technical explanation
How to think about this question
Azure Synapse Serverless SQL pool uses a distributed query engine that reads data directly from Azure Data Lake Storage Gen2 via the OPENROWSET function or external tables, leveraging the Parquet format's columnar storage and predicate pushdown to minimize data scanned. It processes only the data read (not the entire dataset), and pricing is based on the amount of data processed per query (currently $5 per TB), making it cost-effective for ad-hoc analytics on large historical datasets. A subtle behavior is that performance can degrade if queries are not optimized with proper file partitioning or if the data is highly nested, as the engine must parse complex schemas.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 petabytes of Parquet files in Azure Data Lake Storage Gen2 using T-SQL without provisioning any infrastructure, and it charges per terabyte of data processed (pay-per-query). This matches the requirements for fast query performance on historical sales data with a serverless, consumption-based pricing model.
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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