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DP-203 Practice Question: You have an Azure Data Lake Storage Gen2 account…
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?
⚠ Common exam trap
Candidates often 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.
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
✓
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.
Answer analysis
Option-by-option breakdown
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.
- ✗
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.
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Senior Network & Security Engineer · founder of Courseiva
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