The answer is to use the 'Run KQL command' option in Power Query to pass the query directly. This is correct because it pushes the entire KQL query execution to the Azure Data Explorer (Kusto) engine, which performs filtering, aggregation, and data reduction at the source. By leveraging Kusto's optimized query engine, you minimize the volume of data transferred into Power Query, directly addressing the need to improve KQL query performance in Power Query. On the PL-300 exam, this scenario tests your understanding of query folding and source-side optimization—a common trap is to assume you must apply transformations in Power Query after loading all data, which defeats performance. Instead, remember that the 'Run KQL command' option acts like a direct pipeline, bypassing Power Query’s default import behavior. For a quick memory tip: think "KQL command = Kusto crunching, not Power Query pulling."
PL-300 Prepare the data Practice Question
This PL-300 practice question tests your understanding of prepare the data. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
```
A
Use the 'Run KQL command' option in Power Query to pass the query directly.
Running the KQL command directly in Power Query avoids pulling all rows into Power Query for additional transformations, improving performance.
B
Add additional transformations in Power Query to reduce rows.
Why wrong: Adding transformations can increase load time as they are applied after data retrieval.
C
Enable query folding to push the query to the Kusto source.
Why wrong: Query folding is not supported for KQL data sources; the query is already pushed down.
D
Disable query folding to improve performance.
Why wrong: Query folding is already not available; disabling it would have no effect.
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.
Option A is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Running the KQL command directly in Power Query avoids pulling all rows into Power Query for additional transformations, improving performance.
Related concept
Read the scenario before looking for a memorised answer.
✗
Add additional transformations in Power Query to reduce rows.
Why it's wrong here
Adding transformations can increase load time as they are applied after data retrieval.
✗
Enable query folding to push the query to the Kusto source.
Why it's wrong here
Query folding is not supported for KQL data sources; the query is already pushed down.
✗
Disable query folding to improve performance.
Why it's wrong here
Query folding is already not available; disabling it would have no effect.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
Under the hood, the 'Run KQL command' option generates a direct Kusto Data Query Language (KQL) request via the Kusto REST API, bypassing Power Query's M-language translation layer. This allows the Kusto engine to leverage its distributed, columnar storage and indexing to filter, aggregate, and project data before returning results. In real-world scenarios, this can reduce data transfer from gigabytes to kilobytes when queries include early filters or aggregations.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Prepare the data — This question tests Prepare the data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the 'Run KQL command' option in Power Query to pass the query directly. — Option A is correct because 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.
What should I do if I get this PL-300 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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