Question 677 of 846

Develop data processing →hardMultiple ChoiceObjective-mapped

Quick Answer

The correct answer is to create external tables with explicit schema and partition by a frequently filtered column. This works because Azure Synapse serverless SQL can perform partition elimination, a query optimization technique where the engine reads only the subdirectories in Azure Data Lake Storage that match the filter criteria, skipping irrelevant Parquet files entirely. On the DP-203 exam, this concept tests your understanding of how to reduce data scanned in serverless SQL by partitioning external tables, often appearing in scenario-based questions where a query reads too much data due to missing partition pruning. A common trap is assuming file format optimization alone solves the issue, but without partition elimination, the engine still scans all files. Remember the mnemonic: "Partition to prune" — if you partition on a column you frequently filter by, the query engine automatically eliminates non-matching partitions, slashing data scanned and speeding up your serverless SQL queries.

DP-203 Develop data processing Practice Question

This DP-203 practice question tests your understanding of develop data processing. 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.

You are optimizing an Azure Synapse serverless SQL pool query that queries Parquet files in Azure Data Lake Storage. The query takes longer than expected. You notice that the query reads more data than necessary. What is the most effective way to reduce the amount of data scanned?

Question 1hardmultiple choice

Full question →

A
Split large Parquet files into smaller files of 100 MB each
Why wrong: File size does not affect data scanned; serverless SQL pool still reads all rows from the files.
B
Create external tables with explicit schema and partition by a frequently filtered column
External tables with partition elimination allow serverless SQL to skip entire partitions when filters are applied, reducing data scanned.
C
Use SELECT with column pruning to only retrieve necessary columns
Why wrong: Column pruning reduces data returned to client but does not reduce data scanned if the query still reads all columns from the files.
D
Increase the query's resource allocation by using a larger service level objective
Why wrong: Increasing resources does not reduce data scanned; it may speed up processing but not reduce amount read.

Full breakdown with real-world context →

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

✓

Create external tables with explicit schema and partition by a frequently filtered column

Option B is correct because partitioning external tables in Azure Synapse serverless SQL allows the query engine to perform partition elimination, reading only the subdirectories that match the filter criteria. This directly reduces the amount of data scanned from Parquet files in ADLS, addressing the core issue of reading unnecessary data.

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.

✗
Split large Parquet files into smaller files of 100 MB each
Why it's wrong here
File size does not affect data scanned; serverless SQL pool still reads all rows from the files.
✓
Create external tables with explicit schema and partition by a frequently filtered column
Why this is correct
External tables with partition elimination allow serverless SQL to skip entire partitions when filters are applied, reducing data scanned.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use SELECT with column pruning to only retrieve necessary columns
Why it's wrong here
Column pruning reduces data returned to client but does not reduce data scanned if the query still reads all columns from the files.
✗
Increase the query's resource allocation by using a larger service level objective
Why it's wrong here
Increasing resources does not reduce data scanned; it may speed up processing but not reduce amount read.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse column pruning (reducing columns) with partition pruning (reducing rows), or assume that file size optimization alone reduces data volume, when in fact partition elimination is the key technique for minimizing scanned data in serverless SQL pools.

Detailed technical explanation

How to think about this question

Under the hood, Azure Synapse serverless SQL uses a distributed query engine that leverages the Hive-style partitioning metadata (e.g., folder paths like /year=2024/month=01/) to prune partitions at the file system level. When a WHERE clause filters on the partition column, the engine lists only the relevant subdirectories, skipping entire folders of Parquet files. In real-world scenarios, this can reduce data scanned by 90% or more, dramatically improving query performance and cost.

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 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.

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: Create external tables with explicit schema and partition by a frequently filtered column — Option B is correct because partitioning external tables in Azure Synapse serverless SQL allows the query engine to perform partition elimination, reading only the subdirectories that match the filter criteria. This directly reduces the amount of data scanned from Parquet files in ADLS, addressing the core issue of reading unnecessary data.

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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