Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A financial services company runs critical end-of-day reports in an Azure Synapse Analytics dedicated SQL pool. These reports require guaranteed resource allocation and must complete within a fixed time window. However, ad-hoc analytical queries from data scientists often consume resources, causing contention and delaying the critical reports. Which feature should the company implement to ensure the critical reports always receive sufficient resources?
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
Best answer
A. Create a workload group for the critical reports with a high importance setting and assign a minimum percentage of resources.
Correct. Workload groups with importance and resource allocation ensure that critical queries get priority and guaranteed resources, preventing ad-hoc queries from starving them.
B
Distractor review
B. Enable result set caching on all queries to reduce execution time.
Incorrect. Result set caching improves repeat query performance but does not guarantee resources or provide isolation from concurrent workloads. Critical reports could still be delayed if the cache is not hit.
C
Distractor review
C. Implement materialized views for the aggregations used in the critical reports.
Incorrect. Materialized views can speed up complex aggregations but do not provide resource guarantees. They do not prevent other queries from competing for resources.
D
Distractor review
D. Use hash distribution for the fact tables to improve query parallelism.
Incorrect. Table distribution strategies improve query performance but do not provide workload isolation or resource guarantees. They affect data movement, not resource allocation at the query level.
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: A. Create a workload group for the critical reports with a high importance setting and assign a minimum percentage of resources. — Azure Synapse Analytics dedicated SQL pool supports workload management through workload groups and classification. By creating a workload group for critical reports with high importance and a minimum percentage of resources (e.g., via resource class or per-request resource allocation), the system reserves capacity for those queries. Result set caching and materialized views improve performance but do not guarantee resource isolation. Data distribution affects data layout, not query scheduling.
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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