- A
Use external tables in the dedicated SQL pool to query the data lake directly.
Why wrong: External tables in a dedicated SQL pool allow querying data in the lake, but the dedicated pool's compute resources are always running (unless paused), incurring costs even for sporadic ad-hoc queries.
- B
Create a serverless SQL pool endpoint to query the data lake directly.
Serverless SQL pool is a pay-per-query service that auto-scales and charges only for the data processed. It supports full T-SQL and is ideal for ad-hoc, exploratory queries on data lake files.
- C
Load the raw data into the dedicated SQL pool before querying.
Why wrong: Loading raw data into the dedicated pool would increase storage and compute costs for data that is only queried occasionally. This approach is inefficient for ad-hoc analysis.
- D
Use Azure Data Explorer to query the data lake.
Why wrong: Azure Data Explorer is optimized for interactive analytics on large volumes of streaming and time-series data, but it uses a Kusto query language (KQL), not T-SQL, and is not the best fit for general T-SQL ad-hoc queries.
Quick Answer
The correct approach is to create a serverless SQL pool endpoint to query the data lake directly. This is because serverless SQL pool is built specifically for ad-hoc T-SQL queries on data lake files like Parquet, charging only for the data processed per query rather than for provisioned compute. Unlike a dedicated SQL pool, which requires ongoing payment for reserved resources even when idle, serverless SQL pool aligns perfectly with variable-frequency exploratory workloads, minimizing costs while supporting full T-SQL syntax. On the DP-900 exam, this scenario tests your understanding of when to use serverless versus dedicated SQL pools—a common trap is assuming all analytics need dedicated compute. Remember: if the workload is ad-hoc and cost-sensitive, think “serverless for the lake, dedicated for the warehouse.” A helpful mnemonic is “Serverless Saves Sporadic Spend.”
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 services company uses Azure Synapse Analytics to process large volumes of transaction data. They have a dedicated SQL pool (formerly SQL DW) that ingests curated, aggregated data nightly from a data lake. Data analysts need to run ad-hoc, exploratory T-SQL queries on raw transaction data stored as Parquet files in Azure Data Lake Storage Gen2. These queries vary widely in complexity and frequency. The company wants to minimize costs for these ad-hoc queries while still using full T-SQL capabilities. Which approach should they recommend?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Create a serverless SQL pool endpoint to query the data lake directly.
Serverless SQL pool in Azure Synapse Analytics is designed for ad-hoc, on-demand querying of data lake files (like Parquet) without provisioning or paying for dedicated compute resources. It supports full T-SQL syntax and charges only for the data processed per query, making it cost-effective for exploratory workloads with variable complexity and frequency.
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.
- ✗
Use external tables in the dedicated SQL pool to query the data lake directly.
Why it's wrong here
External tables in a dedicated SQL pool allow querying data in the lake, but the dedicated pool's compute resources are always running (unless paused), incurring costs even for sporadic ad-hoc queries.
- ✓
Create a serverless SQL pool endpoint to query the data lake directly.
Why this is correct
Serverless SQL pool is a pay-per-query service that auto-scales and charges only for the data processed. It supports full T-SQL and is ideal for ad-hoc, exploratory queries on data lake files.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Load the raw data into the dedicated SQL pool before querying.
Why it's wrong here
Loading raw data into the dedicated pool would increase storage and compute costs for data that is only queried occasionally. This approach is inefficient for ad-hoc analysis.
- ✗
Use Azure Data Explorer to query the data lake.
Why it's wrong here
Azure Data Explorer is optimized for interactive analytics on large volumes of streaming and time-series data, but it uses a Kusto query language (KQL), not T-SQL, and is not the best fit for general T-SQL ad-hoc queries.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse external tables in a dedicated SQL pool with serverless SQL pool, assuming both are equally cost-effective, but they overlook that dedicated SQL pool incurs fixed compute costs regardless of usage, while serverless SQL pool is truly pay-per-query.
Detailed technical explanation
How to think about this question
Serverless SQL pool uses a distributed query engine that reads data directly from Azure Data Lake Storage Gen2 via the PolyBase connector, supporting formats like Parquet, Delta, and CSV. It leverages pushdown computation to the storage layer when possible, and billing is based on the amount of data scanned (per TB), with a built-in 1 TB daily free tier for reporting purposes. This makes it ideal for data lake exploration where query patterns are unpredictable.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: Create a serverless SQL pool endpoint to query the data lake directly. — Serverless SQL pool in Azure Synapse Analytics is designed for ad-hoc, on-demand querying of data lake files (like Parquet) without provisioning or paying for dedicated compute resources. It supports full T-SQL syntax and charges only for the data processed per query, making it cost-effective for exploratory workloads with variable complexity and frequency.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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