Implementing Analytics Solutions Using Microsoft Fabric

The df.show() method also does not meet the goal. It is used to show the contents of the DataFrame, not to compute statistical functions.
References:
The usage of the show() function is documented in the PySpark API documentation.

A relationship between the product dimension and the order fact table is needed so product category can filter order rows. The product dimension stores attributes such as category and product name, while the fact table stores individual order events and measures. When the relationship connects the two tables, a slicer using product category can filter the related facts and produce the intended order totals.

Creating a separate report for every product category duplicates report design and does not provide flexible filtering. A calculated column containing a screenshot has no analytical relationship to the data. A workspace role named Product Category would describe permissions or membership, not model filter behavior. Slicers operate through fields, relationships, and filter context in the model. The dimension-to-fact relationship is therefore the structural requirement that lets the category selection affect the order data correctly.

That structure also avoids hard-coding category logic in individual visuals, so the same product slicer can be reused across pages and reports consistently.

To meet the requirements, developers should have Admin access to the deployment pipeline (B), Contributor access to the Development and Test workspaces (E), and Viewer access to the Production workspace (D). This setup ensures they can perform necessary actions in development and test environments without having the ability to affect production.
References:
The Power BI documentation on workspace access levels and deployment pipelines provides guidelines on assigning appropriate permissions.

The Python code and output shown in the exhibit display a histogram, which is a representation of the distribution of data. This kind of analysis is descriptive analytics, which is used to describe or summarize the features of a dataset.
Descriptive analytics answers the question of "what has happened" by providing insight into past data through tools such as mean, median, mode, standard deviation, and graphical representations like histograms.
References:
Descriptive analytics and the use of histograms as a way to visualize data distribution are basic concepts in data analysis, often covered in introductory analytics and Python programming resources.

Row-level security is the feature that restricts data rows for different users or roles in a semantic model. For regional managers, the model can apply a region filter so each manager sees only the rows associated with the manager's assigned region. The same report and model can then serve many managers while enforcing different data visibility for each audience.

A report page tooltip provides additional information during report interaction, but it does not prevent a user from seeing rows. A visual color theme controls appearance and has no security effect. A data category can help classify a field for behavior such as geographic mapping, but it does not implement access restrictions. The requirement is about protecting data by region at query time, so row-level security is the model feature that matches the need and keeps the restriction close to the governed analytical layer.

Placing this rule in the model is also stronger than relying on report design alone, because the restriction follows the data whenever the governed model is queried.

Source control for analytics definitions requires the editable model and report files to be stored in a version-controlled repository. That gives the team a history of changes, supports comparison between versions, and allows rollback when a change introduces a problem. Separate development and production environments complement the repository because work can be built and reviewed before it affects business users.

Direct edits in production bypass review and make it harder to understand what changed when a report or model behaves differently. Rendered screenshots do not preserve relationships, measures, report definitions, or data model metadata in a useful source-controlled form. Disabling workspace governance might reduce friction temporarily, but it removes the controls that make a multi-person analytics lifecycle reliable. The stronger practice is to keep definitions under version control and use environment separation to promote validated work into production deliberately.

It also supports collaboration: reviewers can examine the definition changes themselves, not just the final rendered output, before approving a release.