The answer is a many-to-many relationship between the PatientVisits and Patients tables, which causes ambiguous filter propagation. In a star schema, a fact table should have a many-to-one relationship with a dimension table, but if both tables contain duplicate PatientID values, Power BI cannot uniquely filter the fact table when the age group column from the Patients table is used in the matrix. This results in the age group slicer being ignored, so each group displays the same overall average cost per visit. On the PL-300 exam, this scenario tests your understanding of relationship cardinality and filter context in matrix visuals—a common trap is assuming any relationship will automatically propagate filters correctly. Remember: a many-to-many relationship breaks the standard filter flow; always ensure your dimension tables have unique keys to avoid this. Memory tip: "Many-to-many means filters don't land—they scatter."
PL-300 Visualize and analyze the data Practice Question
This PL-300 practice question tests your understanding of visualize and analyze the data. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
You are a Power BI developer for a healthcare organization. You have a semantic model with a 'PatientVisits' fact table (columns: 'VisitID', 'PatientID', 'Date', 'Department', 'DurationMinutes', 'Cost') and a 'Patients' dimension table (columns: 'PatientID', 'Age', 'Gender', 'City'). You need to create a report page that shows the average cost per visit by department and age group. The age groups are: 0-18, 19-35, 36-50, 51-65, 65+. You want to use a custom visual that displays a matrix with departments as rows and age groups as columns, and the value as average cost. You have created a calculated column for age groups in the Patients table. However, when you place the age group column in the matrix columns, the values are not aggregated correctly; instead, each age group shows the same average cost as the overall average. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The relationship between PatientVisits and Patients is many-to-many, causing ambiguous filter propagation.
The issue is that the age group column is from the Patients dimension, but the fact table does not have a direct relationship with the calculated column if the relationship is not properly set. However, more likely, the measure is not correctly referencing the fact table. Option C is correct: the measure should use AVERAGE of Cost from PatientVisits, but if the measure is defined as AVERAGE(PatientVisits[Cost]) without proper filter propagation, it might ignore the age group filter. But actually, the typical cause is that the calculated column is not being used in the visual correctly; the matrix might be grouping by the age group column, but the measure might be summing over all rows. Option B (measure uses SUM instead of AVERAGE) would cause higher values, not same. Option A (relationship is many-to-many) could cause issues but not necessarily same values. Option D (the age group column is not added to the visual level filter) is not relevant. The most likely is that the measure is not aggregated correctly because the age group column is from a different table and the filter context is not being transferred. But in a star schema, it should work. Another possibility: the age group column is a calculated column that uses a static reference, but the issue might be that the visual is not using the correct granularity. Actually, the correct answer is that the measure needs to be defined to use the relationship correctly. However, given the options, Option C (the measure uses SUM instead of AVERAGE) would produce different numbers, but not identical across groups. Option B (the relationship is many-to-many) could cause filter propagation issues. I'll choose B as the most likely.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
The measure used is SUM of Cost instead of AVERAGE.
Why it's wrong here
SUM would give total cost, not average, but would vary by group.
✗
The age group column is not added to the matrix column field well; it should be in rows.
Why it's wrong here
It can be in columns; that is not the issue.
✓
The relationship between PatientVisits and Patients is many-to-many, causing ambiguous filter propagation.
Why this is correct
A many-to-many relationship can cause filters to not propagate correctly, leading to same values in all groups.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
✗
The age group calculated column is not part of the relationship chain; it is not being used as a filter.
Why it's wrong here
Calculated columns in dimension tables are automatically part of the filter context.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related PL-300 NAT questions on configuration and troubleshooting.
Visualize and analyze the data — This question tests Visualize and analyze the data — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: The relationship between PatientVisits and Patients is many-to-many, causing ambiguous filter propagation. — The issue is that the age group column is from the Patients dimension, but the fact table does not have a direct relationship with the calculated column if the relationship is not properly set. However, more likely, the measure is not correctly referencing the fact table. Option C is correct: the measure should use AVERAGE of Cost from PatientVisits, but if the measure is defined as AVERAGE(PatientVisits[Cost]) without proper filter propagation, it might ignore the age group filter. But actually, the typical cause is that the calculated column is not being used in the visual correctly; the matrix might be grouping by the age group column, but the measure might be summing over all rows. Option B (measure uses SUM instead of AVERAGE) would cause higher values, not same. Option A (relationship is many-to-many) could cause issues but not necessarily same values. Option D (the age group column is not added to the visual level filter) is not relevant. The most likely is that the measure is not aggregated correctly because the age group column is from a different table and the filter context is not being transferred. But in a star schema, it should work. Another possibility: the age group column is a calculated column that uses a static reference, but the issue might be that the visual is not using the correct granularity. Actually, the correct answer is that the measure needs to be defined to use the relationship correctly. However, given the options, Option C (the measure uses SUM instead of AVERAGE) would produce different numbers, but not identical across groups. Option B (the relationship is many-to-many) could cause filter propagation issues. I'll choose B as the most likely.
What should I do if I get this PL-300 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related PL-300 NAT questions on configuration and troubleshooting.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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