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

Section	Weight	Objectives
Topic 1: Data Quality and Governance	12%	- Data Quality - Data Lineage - Governance
Topic 2: Data Modeling and Storage	20%	- Storage Optimization - Data Modeling - File Formats
Topic 3: Databricks Lakehouse Platform	24%	- Delta Lake - Data Management - Unity Catalog - Lakehouse Architecture
Topic 4: Monitoring and Troubleshooting	16%	- Monitoring - Troubleshooting - Performance Optimization
Topic 5: Data Processing	28%	- Structured Streaming - ETL Pipelines - Data Transformation - Spark SQL

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q114-Q119):

NEW QUESTION # 114
A transactions table has been liquid clustered on the columns product_id, user_id, and event_date. Which operation lacks support for cluster on write?

A. CTAS and RTAS statements
B. spark.write.format('delta').mode('append')
C. spark.writestream.format('delta').mode('append')
D. INSERT INTO operations

Answer: C

Explanation:
Delta Lake's Liquid Clustering is an advanced feature that improves query performance by dynamically clustering data without requiring costly compaction steps like traditional Z-ordering.
When performing writes to a Liquid Clustered table, some write operations automatically maintain clustering, while others do not.

NEW QUESTION # 115
A data engineering team is implementing an append-only data pipeline using Delta Lake, and wants to ensure that data is never modified or deleted once written. Which Delta Lake feature should the data engineer enable to prevent modifications to existing data?

A. Delta APPEND_ONLY
B. Delta OPTIMIZE
C. Delta VACUUM
D. Delta Time Travel

Answer: A

Explanation:
Enabling the append-only table property enforces that data can only be inserted into the Delta table. Updates and deletes are blocked, ensuring that once data is written it is never modified or removed, which is essential for strict append-only pipeline guarantees.

NEW QUESTION # 116
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.
C. preds.write.format("delta").save("/preds/churn_preds")
D. preds.write.mode("append").saveAsTable("churn_preds")
E.

Answer: D

NEW QUESTION # 117
A data engineer is setting up a pipeline to ingest data from a message bus system that occasionally delivers duplicate messages. The duplicate messages can be a week apart. The target is a Databricks Delta Lake table where each record should appear exactly once. Which Databricks ingestion pattern should be implemented to handle potential duplicates where events can arrive outside of the configured watermark?

A. Use Delta Lake's change data feed to filter duplicate records
B. Configure Structured Streaming with dropDuplicates transformation
C. Use Delta Lake time travel to identify and remove duplicates
D. Implement a write operation using MERGE INTO with a unique key

Answer: D

Explanation:
Using MERGE INTO with a unique key enforces idempotent writes at the Delta Lake table level.
This approach reliably handles duplicates even when events arrive far outside any streaming watermark, ensuring that each logical record is written exactly once regardless of arrival time.

NEW QUESTION # 118
A data architect is implementing Delta Sharing as part of their data governance strategy to enable secure data collaboration with external partners and internal business units. The architect must establish a permission framework that allows designated data stewards to create shares for their respective domains while maintaining security boundaries and audit compliance. Which specific permissions and roles must be assigned to enable users to create, configure, and manage Delta Shares while maintaining proper security governance and access controls?

A. Users need the MANAGE SHARES permission on the workspace
B. Only workspace admins can create and manage shares
C. Users need to be metastore admins or have CREATE SHARE privilege for the metastore
D. Any user with USE_CATALOG privilege can create shares

Answer: C

Explanation:
Creating and managing Delta Shares requires elevated governance privileges at the metastore level. Assigning users as metastore admins or granting them the CREATE SHARE privilege allows designated data stewards to create and configure shares within defined security boundaries, while preserving centralized auditability and access control.

NEW QUESTION # 119
......

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