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

Section	Objectives
Production Pipelines and Orchestration	- Job scheduling and monitoring - Error handling and recovery strategies - Databricks Workflows
Data Modeling and Transformation	- Dimensional modeling concepts - Performance optimization techniques - Spark SQL transformations
Delta Lake and Data Management	- Delta Lake transactions and ACID properties - Schema evolution and enforcement - Time travel and versioning
Data Ingestion and Processing	- ETL pipeline design patterns - Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals
Databricks Lakehouse Platform Architecture	- Workspace and cluster architecture - Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics)

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q225-Q230):

NEW QUESTION # 225
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

A. No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
B. Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
C. No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
D. No; the change data feed only tracks inserts and updates not deleted records.
E. Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.

Answer: A

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Explanation:
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.

NEW QUESTION # 226
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:
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Which statement describes the execution and results of running the above query multiple times?

A. 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.
B. 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.
C. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
D. 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.
E. 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.

Answer: E

Explanation:
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.

NEW QUESTION # 227
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.
D. preds.write.format("delta").save("/preds/churn_preds")
E. preds.write.mode("append").saveAsTable("churn_preds")

Answer: E

NEW QUESTION # 228
A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns. The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB. How should the data engineer meet this requirement?

A. Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.
B. Apply Z-ordering, since it allows flexible reorganization of data layout without rewriting existing files and adapts easily to new filter columns.
C. Enable liquid clustering, as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.
D. Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.

Answer: C

Explanation:
Liquid clustering is designed for managed tables under 1TB with evolving query patterns. It efficiently addresses data skew, continuously optimizes data layout, and allows clustering keys to be changed without requiring full data rewrites, making it well suited for frequently changing filter columns while minimizing maintenance overhead.

NEW QUESTION # 229
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
B. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.
C. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
D. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.

Answer: D

Explanation:
Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. These statistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.

NEW QUESTION # 230
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