This DP-203 practice question tests your understanding of monitor and optimize data storage and 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 have an Azure Data Lake Storage Gen2 account that stores large volumes of parquet files. A reporting application frequently queries a specific subset of data filtered by a 'region' column. To minimize query latency and cost, which optimization should you implement?
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.
Partition the data by region in the folder structure.
Partition elimination reduces data scanned.
Create a clustered index on the region column.
Why wrong: Indexes are not supported in ADLS Gen2.
Compress the parquet files using gzip.
Why wrong: Compression reduces storage but does not reduce scan volume.
Enable hierarchical namespace on the storage account.
Why wrong: Hierarchical namespace organizes files, doesn't optimize queries.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Partition the data by region in the folder structure.
Partitioning the data by region in the folder structure (e.g., /region=NorthAmerica/...) enables Azure Data Lake Storage Gen2 and query engines like Azure Synapse or PolyBase to perform partition pruning. This skips scanning irrelevant files entirely, reducing I/O and query latency while lowering cost by minimizing data processed.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Partition the data by region in the folder structure.
Why this is correct
Partition elimination reduces data scanned.
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.
Create a clustered index on the region column.
Why it's wrong here
Indexes are not supported in ADLS Gen2.
Compress the parquet files using gzip.
Why it's wrong here
Compression reduces storage but does not reduce scan volume.
Enable hierarchical namespace on the storage account.
Why it's wrong here
Hierarchical namespace organizes files, doesn't optimize queries.
Common exam traps
The trap here is that candidates confuse compression (Option C) with partitioning, thinking reducing file size alone minimizes I/O, but without partition pruning the engine still scans all files, negating the benefit.
Detailed technical explanation
Partition pruning works by leveraging Hive-style folder naming (e.g., /region=EU/) that query engines read as metadata to skip irrelevant partitions. In Azure Data Lake Storage Gen2, this is implemented via the hierarchical namespace, which allows efficient directory-level operations. A real-world scenario: a reporting app querying only 'region=US' on a 10 TB dataset with 50 regions would scan only ~200 GB, reducing query time from minutes to seconds and cutting cost proportionally.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Monitor and optimize data storage and processing — This question tests Monitor and optimize data storage and processing — Read the scenario before looking for a memorised answer..
The correct answer is: Partition the data by region in the folder structure. — Partitioning the data by region in the folder structure (e.g., /region=NorthAmerica/...) enables Azure Data Lake Storage Gen2 and query engines like Azure Synapse or PolyBase to perform partition pruning. This skips scanning irrelevant files entirely, reducing I/O and query latency while lowering cost by minimizing data processed.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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