The correct answer is that the JSON policy applies row-level security (RLS), not object-level security (OLS), so the table remains visible. This is because the JSON syntax shown uses a `tables` array with `filter` expressions, which is the standard structure for RLS in Power BI—RLS restricts which rows a user can see within a table but does not hide the table object itself from the report view. Object-level security, by contrast, requires a different configuration using `excludedTables` or `setObjectLevelSecurity` via XMLA endpoints or Tabular Editor, and cannot be implemented through a JSON RLS policy. On the PL-300 exam, this distinction tests your understanding of how security layers operate: RLS filters data at the row level, while OLS hides entire tables or columns. A common trap is assuming any JSON policy hides objects, but remember that RLS controls *data*, OLS controls *metadata*. Memory tip: “RLS rows, OLS objects.”
PL-300 Model the data Practice Question
This PL-300 practice question tests your understanding of model the data. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
```json
{
"dataAccessScope": "Workspace",
"permissions": [
{
"role": "Viewer",
"dataAccess": "ReadOnly"
}
],
"excludedTables": ["EmployeeSalaries"]
}
```
You are configuring a Power BI data model for a workspace containing a semantic model with sensitive salary data. The JSON policy above is applied. A viewer reports that they can see the EmployeeSalaries table in the report. What is the most likely reason?
Refer to the exhibit. You are configuring a Power BI data model for a workspace containing a semantic model with sensitive salary data. The JSON policy above is applied. A viewer reports that they can see the EmployeeSalaries table in the report. What is the most likely reason?
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.
Refer to the exhibit.
```json
{
"dataAccessScope": "Workspace",
"permissions": [
{
"role": "Viewer",
"dataAccess": "ReadOnly"
}
],
"excludedTables": ["EmployeeSalaries"]
}
```
You are configuring a Power BI data model for a workspace containing a semantic model with sensitive salary data. The JSON policy above is applied. A viewer reports that they can see the EmployeeSalaries table in the report. What is the most likely reason?
A
The EmployeeSalaries table is not in the semantic model.
Why wrong: It is mentioned in the policy.
B
The excludedTables property hides the table only in Power Query, not in reports.
Why wrong: OLS would hide the table in reports.
C
The JSON policy is for row-level security (RLS), not object-level security (OLS).
RLS filters rows, not tables; OLS requires a different configuration.
D
The policy has not been applied to the semantic model.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The JSON policy is for row-level security (RLS), not object-level security (OLS).
Option C is correct because the JSON policy shown uses the `tables` array with `filter` expressions, which is the syntax for row-level security (RLS) policies in Power BI. RLS restricts data rows within a table but does not hide the table itself from the report view. Object-level security (OLS) requires a different policy structure using `excludedTables` or `setObjectLevelSecurity` in XMLA endpoints or Tabular Editor, not a JSON RLS policy. Therefore, the viewer can still see the EmployeeSalaries table because OLS has not been configured.
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.
✗
The EmployeeSalaries table is not in the semantic model.
Why it's wrong here
It is mentioned in the policy.
✗
The excludedTables property hides the table only in Power Query, not in reports.
Why it's wrong here
OLS would hide the table in reports.
✓
The JSON policy is for row-level security (RLS), not object-level security (OLS).
Why this is correct
RLS filters rows, not tables; OLS requires a different configuration.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The policy has not been applied to the semantic model.
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse row-level security (RLS) with object-level security (OLS), assuming any JSON policy that restricts data will also hide the table structure, but RLS only filters rows and does not prevent the table from appearing in the report.
Detailed technical explanation
How to think about this question
Under the hood, Power BI enforces RLS at the query level by injecting DAX filter expressions into every query against the semantic model, limiting which rows a user can see. OLS, on the other hand, is enforced at the metadata level, effectively removing the table or column from the report field list and preventing any reference to it. A common real-world scenario is using RLS for department-level salary data and OLS to completely hide the salary table from unauthorized roles, requiring both policies to be configured separately.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
Model the data — This question tests Model the data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The JSON policy is for row-level security (RLS), not object-level security (OLS). — Option C is correct because the JSON policy shown uses the `tables` array with `filter` expressions, which is the syntax for row-level security (RLS) policies in Power BI. RLS restricts data rows within a table but does not hide the table itself from the report view. Object-level security (OLS) requires a different policy structure using `excludedTables` or `setObjectLevelSecurity` in XMLA endpoints or Tabular Editor, not a JSON RLS policy. Therefore, the viewer can still see the EmployeeSalaries table because OLS has not been configured.
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.
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?
Read the scenario before looking for a memorised answer.
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