Implement and manage an analytics solution

A data engineering team wants to migrate their existing Directed Acyclic Graphs (DAGs) written in Python into Microsoft Fabric to orchestrate a complex multi-stage ETL process that includes calls to external REST APIs and dependencies across multiple Fabric items. They require native support for their current DAG definitions with minimal rewriting. Which Fabric capability should they configure to meet this requirement?

A governance team at Contoso must produce a monthly report showing which users viewed or downloaded a specific certified Power BI report hosted in a Fabric workspace, including exact timestamps and IP addresses. The Fabric Data Engineer needs to identify the correct source of this activity data and the role required to retrieve it. Which approach should be used?

A data engineering team needs to ingest CSV files from an Azure Data Lake Gen2 source, apply complex multi-step transformations using PySpark logic that reuses existing custom Python libraries, and write the results to a Lakehouse Delta table. The process must run every night at 2:00 AM and the team wants to minimize the number of components in the solution. Which Fabric item should they use to author and orchestrate this workload?

A Fabric warehouse contains a table named dbo.Employees that includes a Salary column. The HR analytics team must be able to query all other columns in the table, but they must be denied access to the Salary column specifically. You want to enforce this at the object level using T-SQL without creating a separate view or masking the data. What should you do?

A data engineering team uses a Dataflow Gen2 in Fabric to ingest a large sales transaction table into a lakehouse. The source table grows by roughly 500,000 rows daily, but historical rows are never modified. Each scheduled refresh currently reprocesses the entire table, causing long run times and high capacity consumption. The team wants to reduce refresh duration while keeping the destination data accurate. Which approach should they configure?

A data engineering team ingests raw CSV files into a lakehouse, applies complex PySpark transformations that reuse a shared library, and then loads curated data into a warehouse. Business analysts also need a low-code way to reshape a small reference dataset from a SharePoint list before the Spark job runs. The team wants a single orchestration that: runs the reference reshaping first, then the heavy transformation, then the warehouse load, with each step only starting after the previous one succeeds. Which combination best fits these requirements?

Your team uses a Fabric deployment pipeline with three stages: Development, Test, and Production. A Development-stage workspace contains a lakehouse, a warehouse, and a semantic model that connects to the warehouse. After you deploy all items from Development to Test for the first time, you notice the semantic model in Test still points to the warehouse in the Development workspace instead of the one in Test. You want future and current deployments to automatically repoint the semantic model to the warehouse within the same target stage without manually editing connections after every deployment. What should you do?

A data engineering team uses a Fabric deployment pipeline with Development, Test, and Production stages. Before promoting changes from Test to Production, the release manager wants to see exactly which items differ between the two stages so only intended changes are deployed. Which action lets the release manager view the differences between Test and Production before deploying?

A data engineering team manages a Fabric workspace containing lakehouses, notebooks, and semantic models. They want to enforce peer review of every code change through pull requests before changes are merged, while also promoting validated content across Development, Test, and Production workspaces without manually rebuilding items. Which combination of lifecycle management features should they configure to satisfy BOTH requirements?

Your team uses a Fabric deployment pipeline with Development, Test, and Production stages. A data engineer updated three notebooks and one semantic model in the Development stage, but a colleague warns that a fourth notebook still in Development is experimental and must NOT reach Test yet. Before deploying, you want to verify exactly which items differ between Development and Test and push only the approved changes. Which approach meets these requirements with the least risk?

A data engineering team maintains a Fabric warehouse whose schema (tables, views, stored procedures) changes frequently. They want developers to author schema changes locally in Visual Studio Code using SQL, review those changes through pull requests before merging, and validate the resulting schema against a target before publishing. Which lifecycle management approach best satisfies these requirements?

Your team uses a Fabric deployment pipeline with three stages: Development, Test, and Production. A data pipeline in the workspace uses a copy activity that connects to a SQL source. In Development the connection points to a dev SQL server, but after deploying to Production the item must automatically point to the production SQL server without any manual edits after each deployment. What should you configure to ensure the correct server is always used in the Production stage?

A data engineering team uses Git integration to manage source control for their Fabric workspace, which is connected to a feature branch. Separately, the team lead has configured a deployment pipeline with Development, Test, and Production stages. A developer completes changes in the workspace, commits them to Git, and now needs to promote validated content from Development to Test. Which statement correctly describes how these two lifecycle management features work together in this scenario?

Your team uses a Fabric deployment pipeline with Development, Test, and Production stages. After finishing several updates in Development, a data engineer notices that only two of the five modified items are ready to promote to Test; the other three are still being validated. The engineer wants to move just the two finished items to Test without affecting the unvalidated items already in Test. What should the engineer do?

A data engineering team maintains a Fabric workspace containing a Data Factory pipeline that ingests raw files, a notebook that transforms them, and a semantic model refresh. The transformation notebook must run only after the ingestion pipeline completes successfully, and business users need the entire flow to run automatically every night at 2:00 AM. The team wants a single orchestration artifact that controls the end-to-end run order and can pass the ingestion date as a parameter into the notebook. What should the team implement?

You manage a Fabric deployment pipeline with three stages: Development, Test, and Production. Each stage is assigned to a separate workspace. After a colleague made changes in the Development workspace, you open the deployment pipeline view and notice an orange 'X' comparison indicator between the Development and Test stages, while the Test-to-Production comparison shows a green checkmark. You need to promote only the reviewed changes and verify what will move. What does the orange indicator signify, and what is the correct next step?

A data engineering team wants to formalize their release process for Fabric items. They need three separate environments: one for active development, one where QA testers validate changes before release, and one that serves production reports to business users. They plan to use a Fabric deployment pipeline to move content between these environments. What is the minimum configuration they should set up to meet this requirement?

You manage a Fabric Warehouse containing a customer table with an EmailAddress column. Compliance requires that most analysts see only masked email values, but a small group of fraud investigators must see the full unmasked values. You apply dynamic data masking to the EmailAddress column. After applying the mask, the fraud investigators report they still cannot see the real email addresses. What must you do so that the fraud investigators see unmasked data while other analysts continue to see masked values?

Your Fabric tenant has multiple domains, and a governance team wants to formally recognize high-quality datasets. A data engineer has built a semantic model that has passed all quality reviews and should be marked as 'Certified' so it appears as the authoritative source in the OneLake data hub. The engineer attempts to set the certification but only sees the 'Promoted' option available. What must happen for the model to be marked as Certified?

A data engineering team receives sales files from partners at unpredictable times throughout the day. The files are uploaded to a specific folder in a OneLake lakehouse Files section. The team wants a Fabric Data pipeline to run automatically within moments of a new file arriving, without polling on a fixed schedule. Which approach should they configure to trigger the pipeline?