- A
Partition the data in the data lake and use partition elimination in queries.
Partition elimination allows the query to skip irrelevant partitions, reducing data scanned.
- B
Enable result-set caching for the serverless SQL pool.
Why wrong: Result-set caching speeds up repeated queries but does not reduce data scanned for the first execution.
- C
Enable auto-optimize on the Delta Lake tables.
Why wrong: Auto-optimize is a Delta Lake feature not applicable to serverless SQL pool queries on Parquet/CSV.
- D
Create materialized views on the serverless SQL pool.
Why wrong: Materialized views improve performance for repeated queries but do not reduce the amount of data scanned when the view is created.
- E
Use file pruning by specifying file paths in the OPENROWSET query.
File pruning allows the query to read only the necessary files, reducing data scanned.
Partition Elimination and File Pruning for Serverless SQL Pool Performance
This DP-203 practice question tests your understanding of secure, monitor, and optimize data storage and data processing. 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.
Your team uses Azure Synapse Analytics serverless SQL pool to query data in Azure Data Lake Storage Gen2. You notice that queries are running slower than expected. You need to improve query performance by reducing the amount of data scanned. Which two features should you implement? (Select two.)
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
Partition the data in the data lake and use partition elimination in queries.
Options A and E are correct. Partition elimination (A) reduces data scanned by skipping irrelevant partitions. File pruning (E) reduces data scanned by reading only specified files via OPENROWSET. Option B is wrong because result-set caching caches query results but does not reduce the amount of data scanned on the first execution. Option C is wrong because auto-optimize is a feature for Delta Lake tables and is not applicable to serverless SQL pool queries. Option D is wrong because materialized views are not supported in serverless SQL pool, and even if they were, they would not reduce the data scanned by the original query.
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.
- ✓
Partition the data in the data lake and use partition elimination in queries.
Why this is correct
Partition elimination allows the query to skip irrelevant partitions, reducing data scanned.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable result-set caching for the serverless SQL pool.
Why it's wrong here
Result-set caching speeds up repeated queries but does not reduce data scanned for the first execution.
- ✗
Enable auto-optimize on the Delta Lake tables.
Why it's wrong here
Auto-optimize is a Delta Lake feature not applicable to serverless SQL pool queries on Parquet/CSV.
- ✗
Create materialized views on the serverless SQL pool.
Why it's wrong here
Materialized views improve performance for repeated queries but do not reduce the amount of data scanned when the view is created.
- ✓
Use file pruning by specifying file paths in the OPENROWSET query.
Why this is correct
File pruning allows the query to read only the necessary files, reducing data scanned.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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.
Detailed technical explanation
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.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
What to study next
Got this wrong? Here's your next step.
Identify which DP-203 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Secure, monitor, and optimize data storage and data processing — study guide chapter
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FAQ
Questions learners often ask
What does this DP-203 question test?
Secure, monitor, and optimize data storage and data processing — This question tests Secure, monitor, and optimize data storage and data processing — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Partition the data in the data lake and use partition elimination in queries. — Options A and E are correct. Partition elimination (A) reduces data scanned by skipping irrelevant partitions. File pruning (E) reduces data scanned by reading only specified files via OPENROWSET. Option B is wrong because result-set caching caches query results but does not reduce the amount of data scanned on the first execution. Option C is wrong because auto-optimize is a feature for Delta Lake tables and is not applicable to serverless SQL pool queries. Option D is wrong because materialized views are not supported in serverless SQL pool, and even if they were, they would not reduce the data scanned by the original query.
What should I do if I get this DP-203 question wrong?
Identify which DP-203 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on DP-203
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Your Azure Synapse Analytics workspace uses serverless SQL pools for ad-hoc querying. Users report that queries are slow. You examine the execution plan and see that the query scans multiple partitions in the openrowset. What is the best way to improve performance?
hard- A.Increase the MAXDOP setting
- B.Create materialized views on the external tables
- C.Partition the underlying data by a frequently filtered column
- ✓ D.Add a WHERE clause on the partition column
Why D: In serverless SQL pools, performance is improved by file pruning, which reduces the amount of data scanned. Adding a WHERE clause on the partition column allows the query engine to skip irrelevant partitions, thus reducing scan size. Option A is incorrect because MAXDOP controls parallelism, not data pruning. Option B is incorrect because materialized views are not supported in serverless SQL pools. Option C is incorrect because partitioning the underlying data helps, but the question asks for the best way to improve performance given the current query behavior; adding a WHERE clause on the partition column is the most direct and effective solution.
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Last reviewed: Jun 21, 2026
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