Microsoft DP-750 Exam Dumps

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.

CORRECT ANSWE R: B - No.

According to Microsoft Learn on Unity Catalog catalog types, creating a native catalog creates a standard Unity Catalog-managed catalog that stores metadata and data within Databricks-managed storage. A native catalog does NOT federate or expose external database objects from SQL Server. The requirements specify that 'the data is NOT copied into Databricks-managed storage' and that DB1's schemas/tables appear alongside Unity Catalog objects --- this requires a Foreign Catalog, not a native catalog. A foreign catalog uses Lakehouse Federation to create a read-only, virtual representation of an external database within Unity Catalog without moving or copying the data. Therefore, creating a native catalog does not meet the goal, as it has no connection to the SQL Server database DB1.

CORRECT ANSWE R: B - No.

According to Microsoft Learn and PySpark documentation, comparing a DataFrame column to Python's None using the != operator (df.order_amount != None) does not work correctly in PySpark. In PySpark, null comparisons using standard Python equality/inequality operators (==, !=) with None follow SQL null semantics --- any comparison with NULL returns NULL (not True or False). As a result, df.filter(df.order_amount != None) does not correctly filter out null rows; it may return unexpected results or fail silently. The correct PySpark approach is to use the isNotNull() method: df.filter(df.order_amount.isNotNull()), or df.dropna(subset=['order_amount']). Using Python's None with comparison operators is a common mistake when transitioning from Python to PySpark.

CORRECT ANSWE R: C - The Databricks CLI.

According to Microsoft Learn on Databricks Asset Bundles deployment, the Databricks CLI (version 0.205+) is the official tool for deploying DABs to any environment. The deployment commands 'databricks bundle deploy' and 'databricks bundle run' are part of the CLI and support automated, repeatable, and environment-aware deployments. The CLI reads the databricks.yml configuration and deploys the bundle resources to the specified target environment. Option A (Azure Developer CLI / azd) is for deploying Azure infrastructure and does not natively support Databricks Asset Bundles. Option B (Git folders) is a workspace feature for syncing notebook code from Git but does not handle full DAB deployment. Option D (Azure CLI) manages Azure infrastructure and resources but does not have native support for deploying Databricks Asset Bundles.

CORRECT ANSWE R: B - delta.deletedFileRetentionDuration; D - delta.logRetentionDuration.

According to Microsoft Learn on Delta Lake data retention, two table properties control how long data is retained after deletion. The delta.deletedFileRetentionDuration property controls how long physically deleted data files are retained before the VACUUM command can remove them --- setting this to 30 days ensures deleted data is retained for 30 days to support audits. The delta.logRetentionDuration property controls how long the Delta transaction log is kept --- this enables time-travel queries for the 30-day audit window. Together, both properties must be configured to 30 days to meet the full requirement. Option A (delta.timeUntilArchived) does not exist as a standard Delta property. Option C (delta.autoOptimize.autoCompact) controls file compaction, not retention. Option E (delta.enableDeletionVectors) enables deletion vectors for faster deletes but does not control data retention duration.