- A
Use OPENROWSET with a wildcard path that includes all partitions and then apply a WHERE clause.
Why wrong: This would scan all partitions before filtering.
- B
Create an external table with partition definition and query with a WHERE clause on the partition column.
Why wrong: External tables with partitioning do support partition elimination, but the question asks for the best approach among options; OPENROWSET is more direct.
- C
Create a view using PolyBase external table and query with a date filter.
Why wrong: PolyBase is not the recommended approach for serverless SQL pool.
- D
Use OPENROWSET with a file path that limits to the last 7 days' partitions and use a WHERE clause on the date column.
Limiting the file path reduces the files scanned, and the WHERE clause further filters.
Quick Answer
The answer is to use OPENROWSET with a file path that limits to the last 7 days' partitions and a WHERE clause on the date column. This approach minimizes data scanned by leveraging partition pruning at the file system level, where the path pattern like '/date=2025-03-*/hour=*/*.parquet' instructs the serverless SQL pool to read only the relevant Parquet files, while the WHERE clause further filters rows within those files. On the DP-203 exam, this tests your understanding of how serverless SQL pools differ from dedicated pools—partition pruning here relies on folder structure, not metadata indexes, making path-based filtering critical for cost and performance. A common trap is assuming a WHERE clause alone suffices; without restricting the file path, the engine still scans all partitions before filtering. Remember the tip: “Path first, filter second” to ensure you prune before you scan.
DP-203 Develop data processing Practice Question
This DP-203 practice question tests your understanding of develop 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.
You are designing a data processing solution using Azure Synapse Analytics serverless SQL pool. You have Parquet files in Azure Data Lake Storage Gen2 partitioned by date and hour. You need to query the data for the last 7 days efficiently, minimizing the amount of data scanned. Which approach should you use?
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
Use OPENROWSET with a file path that limits to the last 7 days' partitions and use a WHERE clause on the date column.
Option D is correct because using OPENROWSET with a file path that restricts to the last 7 days' partitions (e.g., '/date=2025-03-*/hour=*/*.parquet') minimizes data scanned by only reading the relevant Parquet files. Applying a WHERE clause on the date column further filters rows within those files, leveraging partition pruning at the file system level. This approach avoids scanning all partitions, which is critical for cost and performance in serverless SQL pool.
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.
- ✗
Use OPENROWSET with a wildcard path that includes all partitions and then apply a WHERE clause.
Why it's wrong here
This would scan all partitions before filtering.
- ✗
Create an external table with partition definition and query with a WHERE clause on the partition column.
Why it's wrong here
External tables with partitioning do support partition elimination, but the question asks for the best approach among options; OPENROWSET is more direct.
- ✗
Create a view using PolyBase external table and query with a date filter.
Why it's wrong here
PolyBase is not the recommended approach for serverless SQL pool.
- ✓
Use OPENROWSET with a file path that limits to the last 7 days' partitions and use a WHERE clause on the date column.
Why this is correct
Limiting the file path reduces the files scanned, and the WHERE clause further filters.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse serverless SQL pool's behavior with dedicated SQL pool's partition elimination, assuming that a WHERE clause on a partition column in an external table will automatically prune files, when in fact serverless SQL pool requires explicit path restriction in OPENROWSET to avoid scanning all partitions.
Detailed technical explanation
How to think about this question
Under the hood, serverless SQL pool uses OPENROWSET to read files directly from ADLS Gen2 via the Hadoop Distributed File System (HDFS) connector, and partition pruning relies on the file path pattern specified in the BULK parameter. When you specify a path like '/date=2025-03-*/hour=*/*.parquet', the query engine only lists and reads files matching that pattern, reducing I/O. In contrast, a WHERE clause on a virtual column (like 'date') after a wildcard path still requires scanning all files because the engine cannot infer partition boundaries from the data alone.
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.
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
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this DP-203 question test?
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use OPENROWSET with a file path that limits to the last 7 days' partitions and use a WHERE clause on the date column. — Option D is correct because using OPENROWSET with a file path that restricts to the last 7 days' partitions (e.g., '/date=2025-03-*/hour=*/*.parquet') minimizes data scanned by only reading the relevant Parquet files. Applying a WHERE clause on the date column further filters rows within those files, leveraging partition pruning at the file system level. This approach avoids scanning all partitions, which is critical for cost and performance in serverless SQL pool.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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
2 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. You are designing a data processing solution in Azure Synapse Analytics that uses serverless SQL pools to query Parquet files in Azure Data Lake Storage Gen2. The files are partitioned by year and month. You need to optimize query performance and reduce data scanned. What should you do?
medium- A.Use CREATE EXTERNAL TABLE AS SELECT (CETAS) to create new external tables.
- B.Use OPENROWSET with the DATA_SOURCE parameter.
- ✓ C.Create views that filter on partition columns.
- D.Increase the number of files per partition.
Why C: Option C is correct because serverless SQL pools in Azure Synapse Analytics support partition elimination only when queries use views or inline queries that explicitly filter on partition columns (e.g., year, month) in the WHERE clause. This allows the pool to skip scanning irrelevant partitions, reducing data scanned and improving performance. Creating views that encapsulate these filters ensures consistent partition pruning across queries.
Variation 2. You are designing a data processing solution using Azure Synapse Analytics serverless SQL pool. The solution must query data stored in Parquet files in Azure Data Lake Storage Gen2. The queries are ad-hoc and vary greatly. Which feature should you use to optimize query performance for frequently accessed data partitions?
medium- A.Implement workload management to prioritize queries.
- ✓ B.Use OPENROWSET with explicit file path filtering.
- C.Enable result-set caching on the serverless SQL pool.
- D.Create materialized views on the Parquet files.
Why B: Option C is correct because the OPENROWSET function with file path filtering can prune partitions by specifying the path to specific folders. Option A is wrong because materialized views are not supported in serverless SQL pool. Option B is wrong because result-set caching is for dedicated SQL pool. Option D is wrong because workload management is for dedicated SQL pool.
Last reviewed: Jun 24, 2026
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