Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A financial services company uses a dedicated SQL pool in Azure Synapse Analytics to run large-scale analytical queries. During peak hours, complex aggregations consume excessive resources, causing slower performance for other users. The company needs to ensure that critical scheduled management reports always receive guaranteed resources and complete within a predictable timeframe, while less important ad-hoc queries do not interfere. Which feature should they implement to manage query resource allocation?
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
Result set caching
Result set caching stores query results in the database to speed up repeated queries. It does not guarantee resources for specific queries or prevent resource contention, as it only helps if the same query is run again.
B
Distractor review
Columnstore indexes
Columnstore indexes improve query performance by compressing and storing data column-wise, which speeds up analytical queries. However, they do not provide resource isolation or guarantee resources for specific workloads.
C
Distractor review
Table distribution
Table distribution determines how data is spread across the compute nodes (e.g., hash, round-robin, replicated). While it can improve query performance by reducing data movement, it does not dynamically allocate resources among concurrent queries.
D
Best answer
Workload management
Workload management in Azure Synapse Analytics includes workload classification and workload groups. It allows administrators to assign queries to different resource classes based on importance, ensuring critical queries get guaranteed resources and isolation from other workloads.
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: Workload management — Workload management in Azure Synapse Analytics allows you to classify incoming queries into workload groups with predefined resource limits and importance levels. By assigning critical reports to a workload group with high importance and a minimum resource guarantee, you ensure they always get the resources they need, while less important queries are restricted. Result set caching improves repeated query performance but does not allocate resources. Columnstore indexes improve query performance for analytical queries but do not provide resource isolation. Table distribution is a design choice for data placement, not for runtime resource management.
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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