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Answer : A

CORRECT ANSWE R: A - Enable Photon acceleration for a job compute cluster.

According to Microsoft Learn and the Azure Databricks documentation, Photon is a high-performance vectorized query engine written in C++ that accelerates Apache Spark workloads, especially ingestion and SQL operations. The Contoso technical requirement states: 'Ensure that production ingestion workloads run on compute clusters that can scale automatically during telemetry spikes' and 'Provide fast and consistent performance for BI workloads.' Photon on a job compute cluster directly addresses both speed and consistency for ingestion pipelines. Option B is incorrect because moving ingestion to shared compute would violate the requirement to isolate production from development. Option C is incorrect because increasing a fixed-node all-purpose cluster does not provide autoscaling. Option D is incorrect because disabling autoscaling would prevent the cluster from handling bursty telemetry workloads, directly contradicting the stated requirements.

Answer : D

CORRECT ANSWE R: D - Apply the main branch changes to Branch1 and resolve the conflicts.

According to Microsoft Learn on Databricks Git integration and standard Git workflows, when a merge fails due to conflicts, the recommended approach is to pull/rebase the main branch changes into the feature branch (Branch1), resolve the conflicts locally within Branch1, and then merge the conflict-free branch into main. This ensures Notebook1 includes all changes from both branches. Option A (clone Branch1 as new repository) creates a disconnected copy and does not resolve the merge conflict. Option B (apply changes directly to main) bypasses the branch protection model and risks overwriting the other user's changes. Option C (clone main as new repository) similarly bypasses proper conflict resolution. The correct Git practice is always to resolve conflicts in the feature branch before merging into main.

Answer : A

CORRECT ANSWE R: A - Yes.

According to Microsoft Learn on PySpark DataFrame filtering, df.filter(df.order_amount.isNotNull()) correctly removes all rows where order_amount is null and returns a DataFrame containing only rows where order_amount has a non-null value. The isNotNull() method is a Column method that evaluates to True for non-null values and False for null values, making it the correct and PySpark-idiomatic way to filter out null rows. Unlike Python's != None comparison (which uses SQL null semantics and returns NULL for null comparisons), isNotNull() explicitly handles the SQL NULL case and returns a proper boolean. This is semantically equivalent to WHERE order_amount IS NOT NULL in SQL. The filter passes all non-null rows and excludes all null rows from the resulting DataFrame.

Answer : B

CORRECT ANSWE R: B - No.

According to Microsoft Learn on Azure Databricks Access Connectors, a Databricks Access Connector is an Azure resource that allows Azure Databricks to authenticate to Azure Data Lake Storage Gen2 and other Azure services using a managed identity. An Access Connector is an authentication mechanism, not a data federation or catalog feature. Creating an Access Connector does not expose DB1's schemas and tables in Unity Catalog, does not enable querying of SQL Server data, and does not create any catalog representation of the external database. The Access Connector is a prerequisite for setting up external locations or storage credentials in Unity Catalog, but it alone does not meet the federation requirements. To expose DB1 in Unity Catalog, a foreign catalog (Lakehouse Federation) is required, not an Access Connector.

Answer : B

CORRECT ANSWE R: B - No.

According to Microsoft Learn on Lakeflow Connect, Lakeflow Connect (formerly Databricks Ingestion) is a managed ingestion service that copies data FROM external databases (like SQL Server) INTO Databricks-managed Delta tables. This means Lakeflow Connect physically copies the data into Databricks storage, directly violating the requirement that 'the data is NOT copied into Databricks-managed storage.' Additionally, Lakeflow Connect creates Delta tables in Unity Catalog rather than exposing the original external database objects alongside other Unity Catalog objects in a federated manner. For the requirement to expose DB1's existing schemas and tables without data movement, Lakehouse Federation (foreign catalog) is the correct solution, not Lakeflow Connect.