Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
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?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Distractor review
Use external tables in the dedicated SQL pool to query the data lake directly.
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
Best answer
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
Distractor review
Load the raw data into the dedicated SQL pool before querying.
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
Distractor review
Use Azure Data Explorer to query the data lake.
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 trap
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Technical deep dive
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Related practice questions
Related DP-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
Question 1
A data engineer needs to process streaming data from IoT devices and store the results in Azure Data Lake Storage for long-term analytics. The data must be processed in near real-time to detect anomalies and trigger alerts. Which Azure service should the engineer use for stream processing?
Question 2
A data engineer needs to query data stored in CSV files in Azure Data Lake Storage Gen2 using T-SQL in Azure Synapse Analytics, without loading the data into the database. Which feature should they use?
Question 3
A data engineer needs to process raw clickstream data from multiple websites that is stored in Azure Blob Storage as JSON files. The processing must run automatically every hour, transform the data into a structured format for reporting, and handle schema changes in the source data without manual intervention. Which Azure service should be used?
Question 4
A data engineer is designing a data lake architecture in Azure. They plan to first ingest raw data from various sources into a landing zone in Azure Data Lake Storage Gen2. Then they will clean, validate, and deduplicate that data in a second zone. Finally, they will create aggregated, business-ready datasets in a third zone for analysts. This layered approach is known as which architecture?
Question 5
A data engineer needs to transform large datasets stored in Azure Data Lake Storage Gen2 using Python and Apache Spark. They want a serverless compute option that automatically scales and requires no cluster management. Which Azure service should they use?
Question 6
A company collects customer feedback forms. Each form contains always-present fields like CustomerID and SubmissionDate, but also a free-text Comments field and optional fields like Rating or ProductCategory that vary between forms. How should this data be classified?
FAQ
Questions learners often ask
What does this DP-900 question test?
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. — Azure Synapse serverless SQL pool is a pay-per-query service that can directly query files in the data lake without loading data. This is ideal for ad-hoc exploratory queries because you only pay for the data processed. Dedicated SQL pool requires provisioning fixed resources and is more cost-effective for predictable, high-volume workloads, not for sporadic ad-hoc queries. Creating external tables in the dedicated pool still incurs dedicated pool compute costs. Azure Data Explorer is for time-series and log analytics, not for general T-SQL queries on Parquet files.
What should I do if I get this DP-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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