The answer is to create statistics on external tables in serverless SQL pool, specifically on columns used in joins, filters, and aggregations. This is correct because the serverless SQL pool’s query optimizer relies on statistics to estimate row counts and data distribution; without them, it defaults to inefficient full scans and poor join strategies, especially against Parquet files where predicate pushdown and partition elimination are critical. On the DP-203 exam, this tests your understanding of performance tuning in Azure Synapse Analytics—a common trap is assuming external tables inherit statistics like regular tables, or that indexing is the solution. Instead, remember that serverless SQL pool uses a pay-per-query model, so manual statistics creation is your primary lever for cost and speed. Memory tip: “No stats, slow Parquet—create them to connect the dots.”
DP-203 Design and implement data storage Practice Question
This DP-203 practice question tests your understanding of design and implement data storage. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
A company uses Azure Synapse Analytics serverless SQL pool to query data in ADLS Gen2. Users report that queries against Parquet files are slow. What should you recommend to improve query performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create external tables with statistics on relevant columns.
In Azure Synapse serverless SQL pool, external tables do not automatically have statistics. Without statistics, the query optimizer cannot generate efficient execution plans, leading to poor performance on Parquet files. Creating statistics on relevant columns enables the optimizer to estimate cardinality and choose better join and filter strategies, significantly improving query speed.
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.
✓
Create external tables with statistics on relevant columns.
Why this is correct
Statistics help the optimizer prune data and improve query performance.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create clustered columnstore indexes on the external tables.
Why it's wrong here
Serverless SQL pool does not support indexes on external tables.
✗
Convert the Parquet files to CSV format for faster reads.
Why it's wrong here
CSV is larger and slower to read than Parquet.
✗
Partition the data into many small files.
Why it's wrong here
Too many small files increase metadata overhead and slow queries.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse external table capabilities with dedicated SQL pool features, assuming that indexes like columnstore can be applied to external tables, or that file format changes (CSV) or file count adjustments are the primary performance levers, when in fact statistics are the critical missing piece for serverless SQL pool optimization.
Detailed technical explanation
How to think about this question
Serverless SQL pool uses a distributed query engine that relies on file-level statistics to prune partitions and estimate row counts. Statistics are stored as metadata in the database and are created via CREATE STATISTICS on columns of external tables. Without statistics, the engine defaults to full scans and suboptimal join orders. In practice, creating statistics on frequently filtered or joined columns (e.g., date, region) can reduce query times by orders of magnitude, especially when dealing with large Parquet datasets.
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 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.
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 external tables with statistics on relevant columns. — In Azure Synapse serverless SQL pool, external tables do not automatically have statistics. Without statistics, the query optimizer cannot generate efficient execution plans, leading to poor performance on Parquet files. Creating statistics on relevant columns enables the optimizer to estimate cardinality and choose better join and filter strategies, significantly improving query speed.
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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