PL-300 Manage and secure Power BI Practice Question
Exhibit
{
"dataset": "SalesDataset",
"tables": [
{
"name": "Sales",
"columns": [
{"name": "SalesAmount", "dataType": "decimal"},
{"name": "Region", "dataType": "string"}
],
"roles": [
{
"name": "USOnly",
"filter": "[Region] = \"US\""
}
]
}
]
}Refer to the exhibit. You have a Power BI dataset with the JSON policy shown. You add a user to the USOnly role. What happens when that user views a report based on this dataset?
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
✓
The user sees only rows where Region is 'US' in the Sales table.
Row-level security (RLS) restricts data: only rows where Region is 'US' are visible. Option A is wrong because RLS does not hide the entire table. Option C is wrong because RLS does not affect columns. Option D is wrong because RLS does not affect other tables unless defined.
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 user sees all rows in the Sales table.
Why it's wrong here
Row-level security (RLS) is not a no-op; the role assigned to the user applies a filter predicate on the Region column. Because the role restricts rows to Region='US', the user cannot see rows for other regions (e.g., 'Canada' or 'Mexico') in the Sales table. Seeing all rows would require either no RLS role or a role definition without a filter, neither of which applies here. Thus this option is incorrect because it ignores the active role's filter.
- ✓
The user sees only rows where Region is 'US' in the Sales table.
Why this is correct
This is exactly what the role's DAX filter does. When a user maps to a role with a table-level filter like [Region] = 'US', Power BI applies that predicate to every query against the Sales table, returning only matching rows. All other rows are filtered out at the data source level (or in-memory during import), so the user's report and any aggregations are based only on US sales. This is the intended behavior of RLS.
- ✗
The user sees all rows but the SalesAmount column is hidden.
Why it's wrong here
RLS in Power BI is row-based, not column-based; it filters data rows using DAX predicates, whereas column visibility is controlled by object-level security (OLS) or by removing/reordering columns in the data model. Hiding a column would require an OLS rule or a model change, not an RLS role, and it would not alter which rows are returned. Since the role only defines a row filter on Region, the SalesAmount column remains visible for every row that passes the filter. Therefore, this option confuses RLS with column-level security.
- ✗
The user sees all rows in all tables of the dataset.
Why it's wrong here
RLS applies only to tables for which a filter predicate is defined in the active role, and it does not grant visibility into unrelated tables unless those tables are related and the filter propagates. Here the role's filter is explicitly on the Sales table's Region column, so only Sales rows with Region='US' are visible; the user does not see all rows in every table. Even if other tables are related, the filter will propagate through relationships to restrict related rows, not expand visibility. This option incorrectly asserts that RLS is bypassed or that all tables are unfiltered.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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