Microsoft DP-750 Exam Questions

CORRECT ANSWE R: A - Yes.

According to Microsoft Learn on Lakehouse Federation and Unity Catalog foreign catalogs, a foreign catalog is a Unity Catalog object that represents an external database (such as SQL Server) through a registered connection. Creating a foreign catalog in Catalog Explorer using an existing connection to DB1 exposes all schemas and tables from DB1 as queryable objects within Unity Catalog. These objects appear alongside native Unity Catalog objects in Catalog Explorer. Crucially, Lakehouse Federation queries the external database in place --- the data is never copied into Databricks-managed storage. This satisfies all three requirements: schemas and tables can be queried in Databricks, they appear alongside other Unity Catalog objects, and the data is not copied. The foreign catalog is created under the registered connection to DB1.

CORRECT ANSWE R: B - No.

According to Microsoft Learn on PySpark DataFrame operations, df.fillna(0, subset=['order_amount']) replaces null values in the order_amount column with the integer 0. This does NOT exclude rows where order_amount is null --- it replaces the null with 0, meaning those rows are still included in the resulting DataFrame with 0 as the order_amount value. The requirement is to 'create a DataFrame that excludes rows where the order_amount is null' --- which means null rows must be removed (dropped), not filled. The correct operation to exclude null rows is df.dropna(subset=['order_amount']) or df.filter(df.order_amount.isNotNull()). fillna is used for data imputation (replacing nulls with a default value), which is a different operation from filtering out null rows.

CORRECT ANSWE R: D - Continuous trigger.

According to Microsoft Learn on Lakeflow Jobs triggers, the Continuous trigger keeps the job running perpetually and immediately processes new records as they become available --- this directly satisfies 'New records must be processed as soon as they become available.' The Continuous trigger is specifically designed for near-real-time ingestion scenarios where latency must be minimized. Option A (Manual) requires a human to start each run, adding latency. Option B (File Arrival) triggers a job when new files land in a storage location, which is appropriate for file-based ingestion but introduces per-file trigger overhead rather than true continuous processing. Option C (Scheduled) runs at fixed intervals (e.g., hourly), introducing batch latency that is inconsistent with processing records 'as soon as they become available.'

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: D - Create separate tasks for ingestion, cleansing, and curation.

According to Microsoft Learn, Lakeflow Jobs (formerly Databricks Workflows) supports multi-task pipelines where each task encapsulates a single, well-defined step. The official documentation states that best practice is to decompose complex pipelines into discrete tasks --- ingestion, cleansing, and curation --- so that each task contains only the logic appropriate for that stage. This approach aligns with the Contoso planned change to 'implement scalable data pipeline orchestration' and the requirement to 'define a clear execution order and dependencies.' Option A is incorrect because combining all logic in one notebook violates the single-responsibility principle and makes retry/recovery difficult. Option B is incorrect because duplicating identical logic across tasks wastes resources and defeats the purpose of a modular pipeline. Option C is incorrect because a single SQL MERGE task cannot cleanly separate the ingestion, cleansing, and curation concerns required.