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

Section	Objectives
Topic 1: Data Modeling and Transformation	- Performance optimization techniques - Dimensional modeling concepts - Spark SQL transformations
Topic 2: Databricks Lakehouse Platform Architecture	- Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics) - Workspace and cluster architecture
Topic 3: Production Pipelines and Orchestration	- Job scheduling and monitoring - Databricks Workflows - Error handling and recovery strategies
Topic 4: Delta Lake and Data Management	- Time travel and versioning - Schema evolution and enforcement - Delta Lake transactions and ACID properties
Topic 5: Data Ingestion and Processing	- ETL pipeline design patterns - Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q230-Q235):

NEW QUESTION # 230
Which statement describes Delta Lake Auto Compaction?

A. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
B. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
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C. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
D. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
E. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.

Answer: D

Explanation:
This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones. Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB. Auto Compaction only compacts files that have not been compacted previously.

NEW QUESTION # 231
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
B. Instruct the marketing team to download results as a CSV and email them to the sales organization.
C. Add a parallel table write to the current production pipeline, updating a new sales table that varies Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from as required from marketing table.
D. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
E. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.

Answer: D

Explanation:
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.

NEW QUESTION # 232
The data engineering team maintains the following code:
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Assuming that this code produces logically correct results and the data in the source table has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A. An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.
B. A batch job will update the gold_customer_lifetime_sales_summary table, replacing only those rows that have different values than the current version of the table, using customer_id as the primary key.
C. The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.
D. An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.
E. The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.

Answer: C

Explanation:
This code is using the pyspark.sql.functions library to group the silver_customer_sales table by customer_id and then aggregate the data using the minimum sale date, maximum sale total, and sum of distinct order ids. The resulting aggregated data is then written to the gold_customer_lifetime_sales_summary table, overwriting any existing data in that table. This is a batch job that does not use any incremental or streaming logic, and does not perform any merge or update operations. Therefore, the code will overwrite the gold table with the aggregated values from the silver table every time it is executed.

NEW QUESTION # 233
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. Only workspace admins can create and manage shares
B. Users need to be metastore admins or have CREATE SHARE privilege for the metastore
C. Users need the MANAGE SHARES permission on the workspace
D. Any user with USE_CATALOG privilege can create shares

Answer: B

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 # 234
A member of the data engineering team has submitted a short notebook that they wish to schedule as part of a larger data pipeline. Assume that the commands provided below produce the logically correct results when run as presented.

Which command should be removed from the notebook before scheduling it as a job?

A. Cmd 3
B. Cmd 2
C. Cmd 6
D. Cmd 4
E. Cmd 5

Answer: C

Explanation:
When scheduling a Databricks notebook as a job, it's generally recommended to remove or modify commands that involve displaying output, such as using the display() function. Displaying data using display() is an interactive feature designed for exploration and visualization within the notebook interface and may not work well in a production job context.
The finalDF.explain() command, which provides the execution plan of the DataFrame transformations and actions, is often useful for debugging and optimizing queries. While it doesn't display interactive visualizations like display(), it can still be informative for understanding how Spark is executing the operations on your DataFrame.

NEW QUESTION # 235
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