DP-700 Monitor and Optimize an Analytics Solution Practice Question
You are optimizing a Fabric pipeline that processes data from a high-frequency sensor source. You notice significant data skew during the join operation. Which three strategies should you implement to mitigate this skew?
⚠ Common exam trap
Candidates often suggest simply increasing the cluster size, which is a costly 'brute force' approach that fails to address the underlying data skew causing uneven executor workloads.
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
✓
Apply salting to the skewed join key.
Data skew occurs when one partition or key carries significantly more data than others, causing some executors to work much longer than others. Using salting to redistribute skewed keys, enabling skewed join optimization, or pre-aggregating the skewed dataset are proven techniques to balance the workload across the cluster. Implementing these strategies ensures that no single worker becomes a bottleneck, leading to more predictable execution times and preventing memory failures in large-scale data processing jobs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Apply salting to the skewed join key.
Why this is correct
Salting involves adding a random prefix to the join key, which forces the skewed data to be redistributed across multiple partitions. This prevents a single executor from bearing the entire burden of the skewed key, balancing the processing load more evenly across the entire compute cluster.
- ✓
Enable Skewed Join Optimization in Spark configuration.
Why this is correct
Spark has built-in mechanisms to detect and handle skewed joins automatically. By enabling this configuration, the engine intelligently splits skewed tasks into smaller sub-tasks, processing them in parallel on multiple executors, which significantly reduces the performance impact of uneven data distribution on specific join keys.
- ✓
Pre-aggregate the skewed dataset.
Why this is correct
Reducing the volume of the skewed dataset before joining it with other data is highly effective. By aggregating the skewed table, you shrink the amount of data the join operation needs to process, which naturally minimizes the severity of any existing skew that might have been present.
- ✗
Increase the number of partitions to the maximum.
Why it's wrong here
Blindly increasing partitions can actually degrade performance due to metadata overhead. If the skew is inherent to the data distribution, simply increasing partitions will not resolve the issue. You must focus on redistribution techniques like salting rather than just increasing the volume of tasks for the engine.
- ✗
Change the file format to CSV.
Why it's wrong here
Changing the file format to CSV will not impact how Spark handles joins or data skew. It will likely degrade performance further due to the lack of schema enforcement and the inability to use advanced features like partition pruning or data skipping, which are vital for query efficiency.
About these practice questions
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JA
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-700 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-700 exam.