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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineering team needs to create a SQL Alert that monitors data quality across multiple columns in their customer table. They want to trigger an alert when both the percentage of customers with missing email addresses exceeds 15% AND the percentage of customers with invalid phone number formats exceeds 10%. Which SQL query pattern is appropriate for implementing this multi-column alert condition?

A) SELECT email, phone FROM customers WHERE email IS NULL AND phone NOT RLIKE ''[0-9-
+()\\s]+$'
B) SELECT COUNT (*) FROM customers WHERE email IS NULL OR phone_format_invalid = true
C) SELECT CASE WHEN email_null_pct >15 AND phone_invalid_pct> 10 THEN 1 ELSE 0 END FROM (SELECT (COUNT (CASE WHEN email IS NULL THEN 1 END) * 100.0 / COUNT (*)) as phone_invalid_pct FROM customers) metrics
D) SELECT email_null_pct, phone_invalid_pct FROM (SELECT (COUNT(CASE WHEN email IS NULL THEN 1 END) *
100.0/COUNT (*)) as email_null_pct, (COUNT(CASE WHEN phone NOT RLIKE ''[0-9-+()\\s]+$' THEN 1 END)*
100.0/COUNT (*)) as phone_invalid_pct FROM customers)

2. A data team is working to optimize an existing large, fast-growing table 'orders' with high cardinality columns, which experiences significant data skew and requires frequent concurrent writes. The team notice that the columns 'user_id', 'event_timestamp' and 'product_id' are heavily used in analytical queries and filters, although those keys may be subject to change in the future due to different business requirements. Which partitioning strategy should the team choose to optimize the table for immediate data skipping, incremental management over time, and flexibility?

A) Cluster the table with: ALTER TABLE orders CLUSTER BY user_id, product_id, event_timestamp
B) Partition the table with: ALTER TABLE orders PARTITION BY user_id, product_id, event_timestamp
C) Z-order the table with OPTIMIZE orders ZORDER BY (user_id, product_id, event_timestamp)
D) Use z-order after partitiing the table: OPTIMIZE orders ZORDER BY (user_id, product_id) WHERE event_timestamp = current date () - 1 DAY

3. A data engineer is using Lakeflow Declarative Pipeline to propagate row deletions from a source bronze table (user_bronze) to a target silver table (user_silver). The engineer wants deletions in user_bronze to automatically delete corresponding rows in user_silver during pipeline execution.
Which configuration ensures deletions in the bronze table are propagated to the silver table?

A) Use apply_changes without CDF and filter rows where _soft_deleted is true.
B) Configure VACUUM on user_bronze to delete files, then rebuild user_silver from scratch.
C) Enable Change Data Feed (CDF) on user_bronze, read its CDF stream, and use apply_changes() with apply_as_deletes=True for user_silver.
D) Enable CDF on user_silver, read its transaction log, and use MERGE to sync deletions.

4. A data engineer is designing a secure data sharing strategy for their organization. The company needs to share sensitive customer analytics data with two different partners. Partner A uses Databricks with Unity Catalog enabled, while Partner B uses Apache Spark on AWS without Databricks. How should the company implement secure data sharing for these scenarios?

A) For Partner A, implement Databricks-to-Databricks sharing (D2D) with Unit Catalog integration and no-token exchange system. For Partner B, use open sharing protocol (D2O) with either bearer tokens or OIDC federation for authentication, ensuring both approaches maintain robust security and governance.
B) Databricks-to-Databricks sharing (D2D) can only be used within the same cloud provider, so you must use open sharing (D2O) for any cross-cloud scenarios. Unit Catalog governance is not available when sharing with external platforms.
C) Both partners should use the same Delta Sharing approach since security requirements are identical. You should create bearer tokens for both partners and use the open sharing protocol (D2O) for maximum compatibility.
D) Open sharing protocol (D2O) should be used for both partners because it provides better security than D2D sharing. The bearer token approach is always more secure than Unity Catalog's native authentication.

5. A data engineer is evaluating tools to build a production-grade data pipeline. The team must process change data from cloud object storage, filter out or isolate invalid records, and ensure the timely delivery of clean data to downstream consumers. The team is small, under tight deadlines, and wants to minimize operational overhead while keeping pipelines auditable and maintainable.
Which approach should the data engineer implement?

A) Use LDP to build declarative pipelines with Streaming Tables and Materialized Views, leveraging built-in support for data expectations and incremental processing.
B) Ingest data directly into Delta tables via Spark jobs, apply data quality filters using UDFs, and use LDP for creating Materialized Views.
C) Use a hybrid approach: Ingest with Auto Loader into Bronze tables, then process using SQL queries in Databricks Workflows to generate cleaned Silver and Gold tables on a schedule.
D) Implement ingestion using Auto Loader with Structured Streaming, and manage invalid data handling and table updates using checkpointing and merge logic.

Solutions: