PL-300 Visualize and analyze the data Practice Question
You have a report that contains a map visual showing sales by city. Several cities are missing from the map because the location data is ambiguous. What should you do to resolve this?
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
✓
Create a calculated column that combines city and country to provide unambiguous location.
The correct option is B: create a calculated column that combines city and country to provide unambiguous location. When a map visual can't place a city because multiple places share the same name, supplying a more specific, hierarchical location string (for example, concatenating City and Country) gives the geocoding engine enough context to resolve each point uniquely. Option A is unnecessary and less precise here because adding raw latitude/longitude is a heavier modeling change than disambiguating the existing location field, and it doesn't address the root cause of ambiguous city names. Option C does not fix the data ambiguity—it merely abandons the map visualization. Option D is irrelevant, since bubble size affects rendering of plotted points, not the geocoding of missing locations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add latitude and longitude fields to the model.
Why it's wrong here
While adding latitude and longitude columns would definitively disambiguate locations, it is not the simplest solution because the data already contains city and country fields. Implementing it requires geocoding every record or joining to an external coordinate table, adding model complexity and maintenance overhead. Power BI's map visuals can resolve a well-formatted combined location string without needing explicit coordinates, so this approach is over-engineered for the stated problem.
- ✓
Create a calculated column that combines city and country to provide unambiguous location.
Why this is correct
Creating a calculated column that concatenates city and country (e.g., 'Austin, United States') gives the map visual a unique, geocodable location string. This directly resolves ambiguity because Power BI's Bing Maps geocoder treats the combined string as a specific place, avoiding mismatches like 'London' in the UK vs. 'London' in Ohio. It is a lightweight DAX solution that uses existing data, requires no external lookups, and is the intended best practice for ambiguous city names.
- ✗
Replace the map visual with a table.
Why it's wrong here
Replacing the map with a table ignores the core requirement to show geographic patterns on a map. A table would display the same ambiguous city names without addressing why they fail to plot correctly, so missing or misplaced points would simply become text in rows. The underlying data quality issue remains, and the report loses its visual geospatial context, making this a non-solution that changes the deliverable rather than fixing the data.
- ✗
Adjust the bubble size to make missing points visible.
Why it's wrong here
Adjusting the bubble size only alters the proportional area of markers based on the measure value (e.g., sales amount), not their geographic accuracy. Missing points are caused by geocoding failures or ambiguous location strings that cannot be resolved to a coordinate, which is unrelated to bubble dimensions. Enlarging bubbles cannot force an unresolvable location to appear on the map; it would only affect points that already exist, leaving the actual problem untouched.
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Written by Johnson Ajibi, MSc IT Security
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