PL-300 Prepare the data Practice Question
You are a Power BI data analyst for a subscription software company. You import a table named Subscriptions from an OData feed. The table contains a column named BillingPeriod that stores values such as 'Monthly', 'Annual', and 'Quarterly'. A report author needs a numeric column that converts each value to the number of months in the billing period (1, 12, and 3 respectively) so that revenue can be normalized. You must add this column in Power Query without changing the source system. What should you do?
⚠ Common exam trap
The trap here is reaching for Column From Examples because it feels faster, when a small, fixed set of labels is more reliably handled by an explicit conditional column.
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
✓
Add a conditional column that maps each text value to its corresponding number of months, then change the new column's data type to Whole Number.
A conditional column in Power Query explicitly maps each billing period label to its numeric month count, which is deterministic and easy to audit. Setting the new column to Whole Number makes it usable for arithmetic in the model. The other options either rely on fragile inference, fail outright on non-numeric text, or place the logic in the wrong layer.
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 Column From Examples feature, entering '1' next to 'Monthly' and letting Power BI infer the remaining mappings.
Why it's wrong here
Column From Examples generates M code based on patterns it detects, which works well for consistent formats but is unreliable for arbitrary text labels. With only one example, the inferred logic may not correctly map 'Annual' to 12 and 'Quarterly' to 3. This approach risks producing incorrect values silently, which is worse than an explicit mapping.
- ✓
Add a conditional column that maps each text value to its corresponding number of months, then change the new column's data type to Whole Number.
Why this is correct
A conditional column built on the BillingPeriod values directly maps each known string to the required integer. After setting the new column's type to Whole Number, the model receives a numeric field suitable for calculations such as dividing annual revenue by twelve. This satisfies the requirement entirely inside Power Query without altering the OData source.
- ✗
Create a DAX calculated column in the semantic model that uses SWITCH to translate each billing period into months.
Why it's wrong here
A DAX calculated column would work functionally, but the scenario explicitly requires the transformation to be added in Power Query. Calculated columns also consume memory in the model and cannot be referenced during other Power Query steps. The requirement is scoped to the data preparation layer, so DAX is the wrong tool here.
- ✗
Change the data type of the BillingPeriod column to Whole Number and rely on Power Query's automatic value conversion.
Why it's wrong here
Power Query cannot convert the text 'Monthly' into a number because it contains no numeric content. Changing the type would produce errors in every row rather than the intended month counts. Type conversion only works when the source text is already numeric or parseable as a number, which is not the case here.
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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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