The answer is C — implement result-set caching and set the cache to expire every 5 minutes. Result-set caching in Azure Synapse Analytics stores the exact output of a query in the dedicated SQL pool’s cache, so when the same dashboard query runs again, Synapse returns the cached results instantly without re-scanning the underlying fact table. This directly addresses the performance degradation caused by growing data volumes, and by setting the cache expiration to match the dashboard’s 5-minute refresh interval, you ensure fresh data without increasing DWUs or moving data. On the DP-203 exam, this scenario tests your understanding of caching as a low-overhead performance technique versus more resource-intensive options like materialized views or redistribution; a common trap is choosing a materialized view, but remember that result-set caching requires zero maintenance and works for any exact query repetition. Memory tip: “Cache the exact, match the refresh” — if your query runs identically on a fixed schedule, result-set caching is your instant-win lever.
DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
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.
You are a data engineer for a large e-commerce company. You have an Azure Synapse Analytics dedicated SQL pool that stores transactional data. The pool is currently at DWU1000c. You have a critical dashboard that runs a complex query every 5 minutes. The query scans a large fact table partitioned by date. The query performance is degrading over time as data accumulates. You need to improve performance without increasing DWUs or changing the dashboard query. You also need to minimize data movement overhead. You have the following options:
A. Create a columnstore index on the fact table with a partition alignment.
B. Create a materialized view that aggregates the data at the partition level.
C. Implement result-set caching and set the cache to expire every 5 minutes.
D. Redistribute the fact table using hash distribution on the date column.
Which option should you choose?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Implement result-set caching and set the cache to expire every 5 minutes.
Option C is correct because result-set caching stores the exact query results and can serve the dashboard query instantly if the underlying data has not changed. Since the query runs every 5 minutes, setting the cache expiration to 5 minutes ensures fresh data. Option A is wrong because the table likely already has a columnstore index (default in Synapse). Option B is wrong because materialized views require maintenance and may not match the exact query. Option D is wrong because hash distribution on date can cause data skew and does not reduce scan overhead as effectively as caching.
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.
✗
Redistribute the fact table using hash distribution on the date column.
Why it's wrong here
Hash distribution on date can cause skew and does not reduce scan overhead.
✗
Create a materialized view that aggregates the data at the partition level.
Why it's wrong here
Materialized views require maintenance and may not match the exact query.
✗
Create a columnstore index on the fact table with a partition alignment.
Why it's wrong here
The table likely already has a columnstore index; this may not provide additional benefit.
✓
Implement result-set caching and set the cache to expire every 5 minutes.
Why this is correct
Result-set caching stores query results and can serve repeated queries quickly.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
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.
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: Implement result-set caching and set the cache to expire every 5 minutes. — Option C is correct because result-set caching stores the exact query results and can serve the dashboard query instantly if the underlying data has not changed. Since the query runs every 5 minutes, setting the cache expiration to 5 minutes ensures fresh data. Option A is wrong because the table likely already has a columnstore index (default in Synapse). Option B is wrong because materialized views require maintenance and may not match the exact query. Option D is wrong because hash distribution on date can cause data skew and does not reduce scan overhead as effectively as caching.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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