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

Section	Weight	Objectives
Topic 1: CI/CD, Testing, and Deployment	~6%	- Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines
Topic 2: Data Sharing and Federation	~8%	- Configure Delta Sharing and Lakehouse Federation
Topic 3: Data Modeling	~10%	- Apply dimensional modeling techniques - Design scalable Delta Lake schemas and clustering
Topic 4: Cost and Performance Optimization	~13%	- Leverage system tables and observability tools - Optimize queries, clusters, and storage
Topic 5: Security and Governance	~10%	- Manage Unity Catalog permissions and ACLs - Implement row-level security, column masking, and compliance
Topic 6: Data Transformation, Cleansing, and Quality	~12%	- Apply advanced Spark transformations - Enforce data quality and quarantine bad data
Topic 7: Developing Code for Data Processing using Python and SQL	~22%	- Implement scalable Python/SQL code and project structures - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Manage dependencies, libraries, and UDFs
Topic 8: Streaming Workloads and Change Data Capture	~11%	- Implement reliable streaming pipelines - Apply AUTO CDC APIs and exactly-once semantics
Topic 9: Monitoring, Logging, and Troubleshooting	~8%	- Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables

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Databricks Certified Data Engineer Professional Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q41-Q46):

41. Frage
The data science team has created and logged a production model using MLflow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, loads the customers table containing the customer_id key column into a DataFrame, and defines the feature columns needed for the model.

Which code block will output a DataFrame with the schema "customer_id LONG, predictions DOUBLE"?

A. model.predict(df, columns)
B. df.map(lambda x:model(x[columns])).select("customer_id, predictions")
C. df.apply(model, columns).select("customer_id, predictions")
D. df.select("customer_id", pandas_udf(model, columns).alias("predictions"))
E. df.select("customer_id", model(*columns).alias("predictions"))

Antwort: E

Begründung:
This code block applies the Spark UDF created from the MLflow model to the DataFrame df by selecting the existing customer_id column and the new column produced by the model, which is aliased to predictions. The model(*columns) part is where the UDF is applied to the columns specified in the columns list, and alias("predictions") is used to name the output column of the model's predictions. This will result in a DataFrame with the desired schema: "customer_id LONG, predictions DOUBLE".

42. Frage
The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evolution enabled on this table.

How would the following update be handled?

A. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
B. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
C. The update throws an error because changes to existing columns in the target schema are not supported.
D. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.

Antwort: D

Begründung:
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.

43. Frage
A data engineer needs to implement column masking for a sensitive column in a Unity Catalog- managed table. The masking logic must dynamically check if users belong to specific groups defined in a separate table (group_access) that maps groups to allowed departments. Which approach should the engineer use to efficiently enforce this requirement?

A. Apply a column mask that references the group_access mapping table in its UDF.
B. Create a view without selecting the sensitive column.
C. Create a UDF that hardcodes allowed groups and apply it as a column mask.
D. Use a row filter to restrict access based on the user's group.

Antwort: A

Begründung:
Databricks Unity Catalog supports dynamic column masking, where masking logic can be implemented using SQL functions or UDFs that reference external mapping tables or metadata for context-aware access control.
By referencing the group_access table inside the masking function, the mask dynamically evaluates whether a requesting user belongs to an authorized group. If permitted, the original column value is returned; otherwise, a masked value (such as NULL or asterisks) is shown.
This method enables fine-grained, data-driven masking policies while maintaining a single authoritative access mapping source.
Hardcoding values (A) reduces flexibility, and row filters (D) apply to entire rows rather than specific columns. Therefore, C correctly aligns with Databricks best practices for dynamic masking.

44. Frage
A senior data engineer is planning large-scale data workflows. The current task is to identify the considerations that form a foundation for creating scalable data models that are essential for effective management of large datasets. The data engineering team has identified the core capabilities as part of a scalable data model to build a modern data platform and provided their reasoning for considering Delta Lake for review. The senior data engineer is responsible for identifying the recommendations that are not valid. Which key features can be ignored while evaluating Delta Lake?

A. Delta Lake's capability to process data in both batch and streaming modes seamlessly, providing flexibility in data ingestion and processing.
B. Delta Lake provides limited support for monitoring and troubleshooting data pipelines, so relevant partner tools have to be identified and set up for enhanced operational efficiency.
C. Delta Lake works with various data formats (e.g., Parquet, JSON, CSV) and integrates well with Spark and Databricks tools.
D. Delta Lake optimizes metadata handling, efficiently managing billions of files and facilitating scalability to petabyte-scale datasets.

Antwort: B

Begründung:
Delta Lake includes built-in capabilities for monitoring, auditing, and troubleshooting through transaction logs, history, and tight integration with the Databricks platform. Therefore, limited support for monitoring and troubleshooting is not a valid concern when evaluating Delta Lake and can be ignored.

45. Frage
A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing. Which role must be in place to perform these actions across the metastore?

A. Workspace Admin, because workspace admins can transfer ownership of any Unity Catalog object.
B. Account Admin, because account admins can only create metastores but cannot change ownership of catalogs.
C. Catalog Owner, because catalog owners can transfer any object in any catalog in the metastore.
D. Metastore Admin, because metastore admins can transfer ownership and manage privileges across all metastore objects, including shares and recipients.

Antwort: D

46. Frage
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