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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer manages a Unity Catalog table customer_data in schema finance that includes sensitive fields like ssn and credit_score. Intern Group should only see masked values, while Analyst Group should only access rows for their assigned region. The data engineer needs to restrict access based on user role and region without duplicating data. How should the data engineer enforce this security policy?

A) Use Unity Catalog's row filters based on the region and column masks based on user roles.
B) Create dynamic views for each user role and manage access with ACLs.
C) Use Unity Catalog's row filters based on the user roles and column masks based on the region.
D) Create views using current_user() and is_account_group_member() functions, and apply masking logic inside the SQL SELECT clause for each sensitive column.

2. A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?

A) Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
B) Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.
C) Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.
D) Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.

3. A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
How should the data engineer fulfill these requirements?

A) Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
"cloudFiles.schemaEvolutionMode"= "addNewColumns"
B) Use Auto Loader in batch mode with a daily job to overwrite the Delta table.
C) Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"
D) Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")

4. A data engineer inherits a Delta table with historical partitions by country that are badly skewed.
Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve. Which two actions should the data engineer take? (Choose two.)

A) Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.
B) Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.
C) Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
D) Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.
E) Periodically run OPTIMIZE table_name.

5. 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) preds.write.mode("append").saveAsTable("churn_preds")
B)

C) preds.write.format("delta").save("/preds/churn_preds")
D)

E)

Solutions: