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Prepare the data →hardMultiple ChoiceObjective-mapped

PL-300 Prepare the data Practice Question

Exhibit

Refer to the exhibit.
```kql
// KQL query in Power Query
let StartDate = datetime(2023-01-01);
let EndDate = datetime(2023-12-31);
TableName
| where Timestamp between (StartDate .. EndDate)
| project-away InternalField
| summarize TotalSales = sum(SalesAmount) by Region
```

You are using the above KQL query as a source in Power Query for a Power BI semantic model. The query runs successfully but takes a long time to execute. You need to improve performance. What should you do?

⚠ Common exam trap

Watch out — candidates often confuse 'query folding' (which applies to SQL-based sources like SQL Server) with the native KQL command execution in Power Query, incorrectly assuming that toggling a folding setting will push the query to Kusto when the correct approach is to use the dedicated 'Run KQL command' option.

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

✓

Use the 'Run KQL command' option in Power Query to pass the query directly.

Using the 'Run KQL command' option in Power Query sends the entire KQL query directly to Azure Data Explorer (or Kusto) for execution, allowing the Kusto engine to process and filter data at the source. This minimizes data transfer and leverages Kusto's optimized query engine, significantly improving performance compared to pulling all data into Power Query for transformation.

Answer analysis

Option-by-option breakdown

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

  • ✓

    Use the 'Run KQL command' option in Power Query to pass the query directly.

    Why this is correct

    Using the 'Run KQL command' option in Power Query sends the Kusto query directly to the Kusto engine, which executes all filtering and aggregation server-side and returns only the final result set. This avoids pulling entire tables into the Power Query mashup engine, minimizes data transfer across the network, and lets Kusto use its native optimizations such as indexing, partitioning, and distributed execution. It is the recommended approach because compute happens at the source, not in Power Query.

  • ✗

    Add additional transformations in Power Query to reduce rows.

    Why it's wrong here

    Adding additional transformations in Power Query to reduce rows is counterproductive because those steps run locally in the mashup engine after the KQL query has already returned the full dataset. The data has already crossed the network and occupies memory, so filtering in Power Query neither reduces the initial payload nor improves refresh performance; it only adds extra processing overhead. To truly reduce data at the source, you must push that logic into the KQL query itself.

  • ✗

    Enable query folding to push the query to the Kusto source.

    Why it's wrong here

    Enabling query folding to push the query to the Kusto source is a misunderstanding of how the Kusto connector works. Query folding is a Power Query mechanism that translates step definitions into source-native queries, but the Kusto connector does not support folding for arbitrary transformations; it only executes the KQL command you provide. Since the KQL query is already pushed down and executed in Kusto, any attempt to enable folding is irrelevant—there is nothing to fold because the native query is already the entry point.

  • ✗

    Disable query folding to improve performance.

    Why it's wrong here

    Disabling query folding to improve performance would have no effect because query folding is not available for this Kusto data source in the first place. There is no folding plan to disable, and the data returned by the KQL command is already the final result; the connector behaves as a pass-through. At best, toggling such a setting is a no-op, and at worst it could interfere with any potential future optimizations, so it provides no performance benefit.

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JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This PL-300 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 PL-300 exam.