Databricks Certification Databricks-Certified-Professional-Data-Engineer Passing Score

Delta Lake’s data skipping and file pruning optimizations rely on metadata about columns used in partitioning or Z-ordering. If a filter column (e.g., event_date) is not included in the partition or Z-ordering keys, Spark cannot effectively prune files at query time, resulting in full table scans. The Databricks optimization guide states that “File pruning and data skipping are most effective when queries filter on partition or Z-order columns.” This explains why the filter was applied but had no impact on the amount of data read. Options A and B are incorrect because Delta automatically applies file pruning when possible; D is less likely, as date columns are fully supported for skipping.

The logic uses the MERGE INTO command to merge new records from the view updates into the table customers. The MERGE INTO command takes two arguments: a target table and a source table or view. The command also specifies a condition to match records between the target and the source, and a set of actions to perform when there is a match or not. In this case, the condition is to match records by customer_id, which is the primary key of the customers table. The actions are to update the existing record in the target with the new values from the source, and set the current_flag to false to indicate that the record is no longer current; and to insert a new record in the target with the new values from the source, and set the current_flag to true to indicate that the record is current. This means that old values are maintained but marked as no longer current and new values are inserted, which is the definition of a Type 2 table. Verified References: [Databricks Certified Data Engineer Professional], under “Delta Lake” section; Databricks Documentation, under “Merge Into (Delta Lake on Databricks)” section.

Given the information that the model is registered with MLflow and assuming predict is the method used to apply the model to a set of columns, we use the model.predict() function to apply the model to the DataFrame df using the specified columns . The model.predict() function is designed to take in a DataFrame and a list of column names as arguments, applying the trained model to these features to produce a predictions column. When working with PySpark, this predictions column needs to be selected alongside the customer_id to create a new DataFrame with the schema customer_id LONG, predictions DOUBLE .

Materialized views in Databricks are optimized for precomputing and caching results of complex SQL queries, joins, and aggregations. They store query outputs physically and automatically refresh on a schedule or incremental change basis, drastically improving BI dashboard performance and reducing compute costs.

Conversely, streaming tables are designed for real-time data ingestion and processing , enabling event-driven analytics and low-latency use cases.

Databricks documentation explicitly recommends materialized views for analytical workloads with periodic updates and streaming tables for continuously updating sources. Therefore, the correct choice is C , where complex aggregations from large tables benefit most from materialized precomputation for fast reporting.