DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You manage an Azure Data Lake Storage Gen2 account containing a large volume of JSON logs. Users frequently query only the last seven days of data, but each query scans the entire dataset, causing high costs and slow response times. You need to reduce the amount of data scanned by queries without changing the data format or moving the data. What should you do?
⚠ Common exam trap
The trap here is assuming that enabling hierarchical namespace or converting to a columnar format automatically reduces scanned data, when the real benefit comes from physically partitioning the data and filtering on the partition key.
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 date into separate folders and update queries to filter on the date path.
Partitioning the dataset by date and aligning queries with the partition path allows the query engine to prune unnecessary folders, dramatically reducing the bytes scanned. This approach keeps the original JSON format and location, satisfying the constraints. It directly addresses the root cause: lack of data organization that forces full scans.
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 date into separate folders and update queries to filter on the date path.
Why this is correct
Partitioning data by date into folders, such as year=2024/month=10/day=01, enables query engines to skip irrelevant folders when a query includes a filter on the partition column. This reduces the amount of data scanned, lowering cost and improving performance. Because the data format remains JSON and the files stay in the same account, no format conversion or data movement is required.
- ✗
Enable hierarchical namespace on the storage account.
Why it's wrong here
Enabling a hierarchical namespace is a property of the storage account that allows directory operations and POSIX permissions, but it does not by itself reduce the amount of data scanned by queries. To achieve partition pruning, you must physically organize data into folders and reference those folders in queries. Simply enabling the feature does not change how existing files are laid out or how queries filter them.
- ✗
Convert the JSON files to Parquet format.
Why it's wrong here
Converting to Parquet can improve compression and columnar read performance, but the scenario explicitly requires that the data format remain unchanged. Additionally, even with Parquet, without partitioning or predicate pushdown, queries may still scan the entire dataset. Partitioning is the direct solution to reduce scanned data while keeping JSON.
- ✗
Increase the throughput of the storage account.
Why it's wrong here
Increasing throughput raises the maximum bandwidth and transaction rate available to the storage account, but it does not reduce the volume of data that a query engine must read. The query will still scan all files, incurring the same cost and latency. Throughput scaling addresses performance bottlenecks under heavy concurrent load, not inefficient data layout.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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