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 files. Users report that direct read operations from the data lake are slow, and you observe high egress costs. You need to optimize read performance and reduce cost for analytical queries that frequently filter on a specific timestamp column and select a subset of columns. What should you do?
⚠ Common exam trap
The trap here is assuming that simply moving data to a higher-performance storage tier or increasing compute resources will solve read performance and cost issues, without addressing the inefficient data format and lack of partitioning.
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
✓
Convert the files to Parquet format and partition the data by the timestamp column.
Converting JSON to Parquet reduces storage size and enables columnar reads, while partitioning by the frequently filtered timestamp column allows the query engine to skip irrelevant data. Together, these changes minimize the data scanned and transferred, improving read performance and lowering egress costs. Other options either do not address the root cause or introduce unnecessary expense.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Convert the files to Parquet format and partition the data by the timestamp column.
Why this is correct
Parquet is a columnar format that enables column pruning and predicate pushdown, reducing I/O and cost. Partitioning by the timestamp column further limits the data scanned when queries filter on that column. This directly addresses slow reads and high egress by minimizing the amount of data transferred and processed.
- ✗
Enable Azure Storage analytics logging and review the logs to identify slow queries.
Why it's wrong here
Storage analytics logging records access patterns and performance metrics, but it does not improve read performance or reduce egress costs. It is a diagnostic tool, not an optimization. While useful for troubleshooting, it does not change the format or partitioning of the data, so the underlying performance issue remains.
- ✗
Increase the number of partitions in the Azure Synapse Analytics dedicated SQL pool that reads the data.
Why it's wrong here
The scenario focuses on optimizing reads from the data lake itself, not the SQL pool. Increasing partitions in the SQL pool may improve query performance after data is loaded, but it does not reduce egress costs or improve direct reads from the lake. The bottleneck is the data format and layout in the lake.
- ✗
Move the data to a premium block blob storage account with a higher throughput tier.
Why it's wrong here
Premium block blob storage offers lower latency and higher throughput, but it is significantly more expensive and does not reduce egress costs. Moreover, it does not address the inefficiency of reading JSON files; the data format and lack of partitioning still cause excessive I/O and cost for analytical queries.
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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
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.