The answer is to create a view that uses explicit partition pruning by filtering on year, month, and day in the WHERE clause. This is correct because Synapse Serverless SQL does not automatically eliminate partitions when querying partitioned Parquet files; without explicit filters on the partition columns, the engine scans all data across every year, month, and day folder. By defining a view that includes WHERE conditions on year, month, and day for the last 30 days, you force the query engine to read only the relevant partitions, drastically reducing the data scanned and improving both performance and cost. On the DP-203 exam, this scenario tests your understanding of how Serverless SQL differs from Dedicated SQL Pool regarding partition elimination—a common trap is assuming automatic pruning works the same way. Remember the mnemonic: “Explicit filters, explicit wins” to recall that you must always specify partition columns in the WHERE clause for Serverless SQL to prune effectively.
DP-203 Design and implement data storage Practice Question
This DP-203 practice question tests your understanding of design and implement data storage. 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 storage solution for a global e-commerce company. The company's analytics team uses Azure Synapse Serverless SQL to query Parquet files in ADLS Gen2. The data is partitioned by year, month, and day. The team frequently runs queries that aggregate sales by product category across the last 30 days. However, the queries are slow and scanning too much data. What should you do to improve performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a view that uses explicit partition pruning by filtering on year, month, and day in the WHERE clause.
Option C is correct because creating a view with explicit partition pruning by filtering on year, month, and day in the WHERE clause forces Synapse Serverless SQL to read only the relevant partitions (the last 30 days) instead of scanning the entire dataset. This reduces the amount of data scanned, directly improving query performance and lowering cost.
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.
✗
Increase the number of files per partition to improve parallelism.
Why it's wrong here
More files do not help with partition pruning.
✗
Convert the Parquet files to CSV format to reduce file size.
Why it's wrong here
CSV is larger and slower to query than Parquet.
✓
Create a view that uses explicit partition pruning by filtering on year, month, and day in the WHERE clause.
Why this is correct
Serverless SQL can eliminate partitions when filters are on partition columns.
Related concept
Read the scenario before looking for a memorised answer.
✗
Add a new partition by product category to the folder structure.
Why it's wrong here
This would increase the number of partitions and may not be used by the query.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often think adding more partitions (Option D) or changing file formats (Option B) will help, but they fail to recognize that the real bottleneck is the lack of partition pruning in the query itself, not the storage layout.
Detailed technical explanation
How to think about this question
Synapse Serverless SQL uses partition elimination when the WHERE clause includes partition columns (year, month, day) with predicates that can be evaluated at the folder level. Under the hood, the query engine reads the folder structure and skips entire directories that do not match the filter, which is far more efficient than scanning all files and then filtering. In practice, if the folder structure is /year=2024/month=11/day=15, a query with WHERE year = 2024 AND month = 11 AND day >= 15 will only read the relevant folders, but if the filter is on a non-partition column like product_category, all partitions must be scanned.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a view that uses explicit partition pruning by filtering on year, month, and day in the WHERE clause. — Option C is correct because creating a view with explicit partition pruning by filtering on year, month, and day in the WHERE clause forces Synapse Serverless SQL to read only the relevant partitions (the last 30 days) instead of scanning the entire dataset. This reduces the amount of data scanned, directly improving query performance and lowering cost.
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.
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Question Discussion
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