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Monitor and Optimize an Analytics SolutionmediumMultiple ChoiceObjective-mapped

DP-700 Monitor and Optimize an Analytics Solution Practice Question

You are monitoring a Spark notebook in Microsoft Fabric that is taking longer than expected to process a large dataset. You notice that a single executor is processing significantly more data than others. Which tool or feature should you use to identify the specific partition causing this data skew?

⚠ Common exam trap

Candidates often select general monitoring dashboards or capacity apps, overlooking built-in development features specifically designed to diagnose runtime data skew in Spark jobs.

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

The Spark Advisor and Spark UI

Spark Advisor is integrated directly into Fabric notebooks to provide real-time performance suggestions. It analyzes job execution patterns and can detect common issues like data skew or sub-optimal file sizes. By using the Spark UI alongside Advisor, engineers can pinpoint the exact stage and partition that is causing a bottleneck in the distributed processing pipeline.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Fabric Monitoring Hub

    Why it's wrong here

    The Monitoring Hub provides a high-level overview of job statuses and durations across the entire tenant or workspace. While it can tell you that a job failed or is running slowly, it lacks the deep internal metrics of Spark execution, such as task-level data distribution or executor-specific memory pressure details.

  • The Spark Advisor and Spark UI

    Why this is correct

    The Spark UI provides a detailed breakdown of stages, tasks, and executors, allowing you to see the exact bytes read by each partition. Combined with Spark Advisor, it offers actionable recommendations to resolve skew, such as using salting techniques or repartitioning the data to ensure a more even distribution of work.

  • The Capacity Metrics App

    Why it's wrong here

    This application tracks CU consumption at the item level but does not look inside the Spark engine to see how tasks are distributed across executors. It is useful for understanding the cost and capacity impact of the notebook but cannot help with internal code optimization or identifying data skew within a job.

  • The OneLake Explorer

    Why it's wrong here

    OneLake Explorer is a tool for viewing files and folders in OneLake from a local machine. It does not provide any execution metrics or performance data for Spark jobs. Using it would only allow you to see the physical files, not how they are being processed during a live notebook execution.

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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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