- A
Increase the MAXDOP setting in the query.
Why wrong: Increasing MAXDOP may cause resource contention and does not improve performance for partition pruning.
- B
Convert the Parquet files to CSV format for faster parsing.
Why wrong: Converting to CSV would degrade performance because CSV is row-based and slower for analytical queries compared to columnar Parquet.
- C
Create materialized views on the external tables.
Why wrong: Materialized views are not supported in serverless SQL pool; they are a feature of dedicated SQL pool.
- D
Partition the Parquet files by date and use partition pruning in the query.
Partitioning by date enables partition pruning, reducing data scanned and improving query performance.
Question 220 of 851
Improve Query Performance on Parquet Files in Synapse Serverless SQL Pool with Partition Pruning
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. A key principle to apply: partition pruning. 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.
You are designing a data processing solution using Azure Synapse Analytics serverless SQL pool. The solution will query data stored in Parquet files in Azure Data Lake Storage Gen2. You need to ensure that the queries are optimized for performance. Which action should you take?
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 Parquet files by date and use partition pruning in the query.
Option D is correct because partitioning Parquet files by a commonly filtered column, such as date, allows Azure Synapse serverless SQL pool to perform partition pruning, which eliminates scanning unnecessary partitions and reduces the amount of data read. Option A is incorrect because increasing MAXDOP (maximum degree of parallelism) can lead to resource contention and may not improve query performance in serverless SQL pool. Option B is incorrect because Parquet is a columnar format optimized for analytics and is more efficient than CSV for querying large datasets. Option C is incorrect because materialized views are not supported in serverless SQL pool; they are only available in dedicated SQL pool.
Key principle: Partition pruning
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the MAXDOP setting in the query.
Why it's wrong here
Increasing MAXDOP may cause resource contention and does not improve performance for partition pruning.
- ✗
Convert the Parquet files to CSV format for faster parsing.
Why it's wrong here
Converting to CSV would degrade performance because CSV is row-based and slower for analytical queries compared to columnar Parquet.
- ✗
Create materialized views on the external tables.
Why it's wrong here
Materialized views are not supported in serverless SQL pool; they are a feature of dedicated SQL pool.
- ✓
Partition the Parquet files by date and use partition pruning in the query.
Why this is correct
Partitioning by date enables partition pruning, reducing data scanned and improving query performance.
Related concept
Partition pruning
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
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Partition pruning
- Serverless SQL pool
- Parquet format
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
Partition pruning
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.
Review partition pruning, then practise related DP-203 questions on the same topic to reinforce the concept.
- →
Secure, monitor, and optimize data storage and data processing — study guide chapter
Learn the concepts, then practise the questions
- →
Secure, monitor, and optimize data storage and data processing practice questions
Targeted practice on this topic area only
- →
All DP-203 questions
851 questions across all exam domains
- →
Microsoft Azure Data Engineer Associate DP-203 study guide
Full concept coverage aligned to exam objectives
- →
DP-203 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related DP-203 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Secure, monitor, and optimize data storage and data processing practice questions
Practise DP-203 questions linked to Secure, monitor, and optimize data storage and data processing.
Design and develop data processing practice questions
Practise DP-203 questions linked to Design and develop data processing.
Design and implement data security practice questions
Practise DP-203 questions linked to Design and implement data security.
Monitor and optimize data storage and processing practice questions
Practise DP-203 questions linked to Monitor and optimize data storage and processing.
Design and implement data storage practice questions
Practise DP-203 questions linked to Design and implement data storage.
Develop data processing practice questions
Practise DP-203 questions linked to Develop data processing.
DP-203 fundamentals practice questions
Practise DP-203 questions linked to DP-203 fundamentals.
DP-203 scenario practice questions
Practise DP-203 questions linked to DP-203 scenario.
DP-203 troubleshooting practice questions
Practise DP-203 questions linked to DP-203 troubleshooting.
Practice this exam
Start a free DP-203 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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 — Partition pruning.
What is the correct answer to this question?
The correct answer is: Partition the Parquet files by date and use partition pruning in the query. — Option D is correct because partitioning Parquet files by a commonly filtered column, such as date, allows Azure Synapse serverless SQL pool to perform partition pruning, which eliminates scanning unnecessary partitions and reduces the amount of data read. Option A is incorrect because increasing MAXDOP (maximum degree of parallelism) can lead to resource contention and may not improve query performance in serverless SQL pool. Option B is incorrect because Parquet is a columnar format optimized for analytics and is more efficient than CSV for querying large datasets. Option C is incorrect because materialized views are not supported in serverless SQL pool; they are only available in dedicated SQL pool.
What should I do if I get this DP-203 question wrong?
Review partition pruning, then practise related DP-203 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Partition pruning
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 →
Keep practising
More DP-203 practice questions
- You are designing a data storage solution for IoT sensor data. The data is written thousands of times per second and req…
- A data processing job in Azure Synapse Analytics writes results to a table in the dedicated SQL pool. After a failure, t…
- A multinational corporation uses Azure Data Lake Storage Gen2 to store petabytes of parquet files partitioned by date an…
- A company ingests streaming data from IoT devices into Azure Event Hubs. The data must be processed in near real-time to…
- Which THREE factors should be considered when choosing between Azure Stream Analytics and Azure Databricks for a real-ti…
- You are designing a data lake on Azure Data Lake Storage Gen2. The data will be used by both batch processing (Spark) an…
Last reviewed: Jun 21, 2026
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.