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Databricks Certified Data Engineer Professional Sample Questions:

1. A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A) Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
B) Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
C) Databricks has autotuned to a smaller target file size based on the overall size of data in the table
D) Databricks has autotuned to a smaller target file size based on the amount of data in each partition

2. The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A)

B) preds.write.format("delta").save("/preds/churn_preds")
C) preds.write.mode("append").saveAsTable("churn_preds")
D)

E)

3. 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) Implement ingestion using Auto Loader with Structured Streaming, and manage invalid data handling and table updates using checkpointing and merge logic.
B) Use LDP to build declarative pipelines with Streaming Tables and Materialized Views, leveraging built-in support for data expectations and incremental processing.
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) Ingest data directly into Delta tables via Spark jobs, apply data quality filters using UDFs, and use LDP for creating Materialized Views.

4. A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A) Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
B) Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
C) Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
D) Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
E) Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.

5. A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

A) Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
B) Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
C) Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
D) Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
E) Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.

Solutions: