Maintain a data analytics solution

Your organization requires that all data assets containing personally identifiable information (PII) carry a 'Highly Confidential' classification that automatically flows to any downstream artifacts and enforces encryption rights when data is exported to Excel or PDF. A data analyst has built a semantic model on top of a lakehouse table containing customer PII. Which action should you take to meet the requirement with the least ongoing manual effort?

You manage a Fabric warehouse that contains an Employees table with a Salary column. The HR analytics team must query all columns, but a group of operational reporting users must be able to query the Employees table for everything except the Salary column. You want to enforce this at the warehouse layer using T-SQL so the restriction applies regardless of which client tool connects. What should you implement?

Your analytics team maintains several Power BI reports and semantic models in a Fabric workspace. Leadership wants full version history, the ability to review changes through pull requests, and text-based diffs of model and report definitions before changes are merged. You need to recommend how developers should structure and store their work to meet these requirements with the least ongoing manual effort. What should you do?

You maintain a large enterprise semantic model in a Fabric Premium workspace. A colleague made structural changes (new calculated columns and measures) directly in a development workspace model using an external tool. You now need to promote ONLY those metadata differences to the production model without overwriting production-only partitions or triggering a full data reload. Which approach best accomplishes this?

Your team has a Fabric workspace named 'Sales-Dev' that contains a lakehouse, a semantic model, and several reports. Leadership wants a formal promotion process across Development, Test, and Production environments using a Fabric deployment pipeline. You create a new deployment pipeline with three stages. What must you do next to begin promoting content through the pipeline?

Your team uses a three-stage Fabric deployment pipeline (Development, Test, Production). A semantic model in Development connects to a warehouse. When content is deployed from Test to Production, you have configured a data source rule on the Production stage that repoints the connection to the production warehouse. However, you also rely on Fabric's automatic re-binding, which connects deployed items to items already present in the target stage. During a deployment to Production, both the data source rule and auto-binding could apply to the same connection. What determines which connection the deployed semantic model uses in Production?

Your team uses a Fabric deployment pipeline with Development, Test, and Production stages. A developer has updated three reports and one semantic model in the Development workspace, but only the semantic model change has been validated by QA. Before promoting to the Test stage, the lead analytics engineer wants to confirm exactly which items differ between Development and Test and then move only the approved semantic model. What should the engineer do?

Your team manages a Fabric deployment pipeline with Development, Test, and Production stages. During the last sprint, an analyst modified only one semantic model in the Development stage; all reports, lakehouses, and other semantic models remain identical to what is already in Test. You want to move just the updated semantic model into Test while minimizing the risk of accidentally overwriting unrelated Test items. Which action should you take in the deployment pipeline before deploying?

Your team maintains a Fabric workspace containing a semantic model and several reports. The team wants developers to work in isolated feature branches with pull-request reviews before merging, while release managers separately promote validated content from a Development workspace through Test to a Production workspace without touching source files. Which combination of Fabric lifecycle tools should you configure to meet both requirements?

You manage a Fabric workspace containing a shared semantic model that is used by 12 published reports across three separate workspaces. Before you deploy a schema change that renames several measures, your governance team requires you to identify every downstream report that could break, without opening each report manually. Which Fabric feature should you use to obtain this information?

Your team uses a Fabric deployment pipeline with Development, Test, and Production stages. A semantic model in the pipeline connects to a lakehouse. When you deploy from Development to Test, the semantic model in Test must automatically point to the Test workspace's lakehouse instead of the Development lakehouse, without manually editing the model after each deployment. What should you configure?

Your organization uses a Microsoft Fabric deployment pipeline with Development, Test, and Production stages. A junior analyst, Priya, has been assigned the Contributor role on the Development workspace and the Member role on the Test workspace. She is not assigned any role in Production and is not listed as a user of the deployment pipeline itself. When Priya opens the deployment pipeline, she reports that she cannot deploy content from Development to Test. What is the most likely reason she cannot perform the deployment?

Your team uses a Fabric deployment pipeline with Development, Test, and Production stages. A semantic model connects to a lakehouse SQL analytics endpoint. When you deploy from Test to Production, the semantic model must automatically point to the Production lakehouse instead of the Test one, without anyone manually editing the model after each deployment. What should you configure?

Your team has three developers who each need to work on separate features of the same Power BI project simultaneously without overwriting each other's changes. The workspace is already connected to a Git repository. During development, each developer needs an isolated environment to test their changes before merging them into the shared workspace. Which approach best supports parallel, isolated development in this scenario?

Your organization has multiple semantic models in Fabric, and business analysts frequently build reports from unofficial models, causing inconsistent metrics. The central BI team has built an authoritative sales semantic model that has passed data quality and governance reviews. Leadership wants this model to appear with the highest trust indicator so users clearly recognize it as the organization's sanctioned source. Only a small governance group should be able to apply this designation. What should you do?

A Fabric Analytics Engineer manages a workspace containing 30 items, including a warehouse named FinanceDW. An external auditor needs to query only FinanceDW using its SQL analytics endpoint for a compliance review. Company policy forbids granting the auditor any workspace role, and the auditor must not see or access any other item in the workspace. What should the engineer do?

You maintain a certified shared semantic model in a Fabric workspace. Several Power BI reports across multiple workspaces connect to this model via a live connection. Before you publish a structural change that renames two measures, you want to identify every downstream report that could break so you can notify the report owners in advance. Which Fabric capability should you use?

A Fabric Analytics Engineer manages a lakehouse that stores raw CSV and Parquet files in the Files section of OneLake. A team of external contractors needs read access only to a specific subfolder containing anonymized sample data, but they must not be able to see or query any other folders, tables, or the SQL analytics endpoint. The engineer wants to enforce this using native Fabric capabilities without duplicating data. Which approach should the engineer use?

Your organization has hundreds of semantic models scattered across many Fabric workspaces. Business analysts complain that they cannot tell which models are authoritative when building new reports. As the Analytics Engineer, you want a company-wide, curated view where governance admins can designate a small set of models as the single source of truth so they appear at the top of discovery experiences with a special badge. Which endorsement action should you use to achieve this?

A Fabric Analytics Engineer manages a shared semantic model that contains a measure called [Net Profit Margin]. Executive users must see this measure, but a group of regional analysts assigned to a dedicated security role must not be able to see it or query it at all. The engineer wants to hide the measure from these analysts while keeping all other measures available to them. Which approach should the engineer use?