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

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

>> Databricks-Certified-Data-Engineer-Professional시험대비 공부문제 <<

최신 Databricks Certification Databricks-Certified-Data-Engineer-Professional 무료샘플문제 (Q19-Q24):

질문 # 19
A data organization has adopted Delta Sharing to securely distribute curated datasets from a Unity Catalog-enabled workspace. The data engineering team shares large Delta tables internally via Databricks-to-Databricks and externally via Open Sharing for aggregated reports. While testing, they encounter challenges related to access control, data update visibility, and shareable object types. What is a limitation of the Delta Sharing protocol or implementation when used with Databricks-to-Databricks or Open Sharing?

A. Delta Sharing (both Databricks-to-Databricks and Open Sharing) allows recipients to modify the source data if they have select privileges.
B. With Databricks-to-Databricks sharing, Unity Catalog recipients must re-ingest data manually using COPY INTO or REST APIs.
C. With Open Sharing, recipients cannot access Volumes, Models, or notebooks -- only static Delta tables are supported.
D. Delta Sharing does not support Unity Catalog-enabled tables; only legacy Hive Metastore tables are shareable.

정답：C

설명：
According to Databricks' documentation, Open Sharing allows secure sharing of Delta tables to any recipient via a REST-based protocol without requiring a Databricks account. However, the Open Sharing protocol is limited to static Delta tables--it does not support sharing of Unity Catalog objects like Volumes, Machine Learning models, or notebooks. Only Databricks-to- Databricks sharing supports dynamic data sharing with update visibility and streaming reads.
Thus, the inability to share non-table objects in Open Sharing represents a known limitation of the protocol. Option A accurately reflects this constraint as described in Delta Sharing design principles and documentation.

질문 # 20
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline. Which command should the data engineer enter in a web terminal configured with the Databricks CLI?

A. Stop the existing pipeline; use the returned settings in a reset command
B. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
C. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline
D. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command

정답：D

설명：
The Databricks CLI provides a way to automate interactions with Databricks services. When dealing with pipelines, you can use the databricks pipelines get --pipeline-id command to capture the settings of an existing pipeline in JSON format. This JSON can then be modified by removing the pipeline_id to prevent conflicts and renaming the pipeline to create a new pipeline. The modified JSON file can then be used with the databricks pipelines create command to create a new pipeline with those settings.
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질문 # 21
The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?

A. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
B. Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.
C. Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.
D. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
E. Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.

정답：E

설명：
This is the correct answer because it exemplifies best practices for implementing this system. By isolating tables in separate databases based on data quality tiers, such as bronze, silver, and gold, the data engineering team can achieve several benefits. First, they can easily manage permissions for different users and groups through database ACLs, which allow granting or revoking access to databases, tables, or views. Second, they can physically separate the default storage locations for managed tables in each database, which can improve performance and reduce costs. Third, they can provide a clear and consistent naming convention for the tables in each database, which can improve discoverability and usability.

질문 # 22
A security team wants to enforce data protection for a customer table containing customer PII data. To comply with local policies, sales team members should only see customers from their region, while non-admin users should have email addresses masked. Which implementation approach should be used when using Unity Catalog row filters and column masks?

A. Implement row filters with SQL UDFs based on user region only since column masks cannot be combined with row filters on the same table, then apply them be recreating the table with DROP TABLE and CREATE TABLE SET ROW FILTER commands.
B. Use table ACLs to restrict access using tags with GRANT SELECT ON table_name WITH TAG command, and rely on application-level filtering for sensitive data based on user region.
C. Create a view with dynamic WHERE clauses for region filtering and use string replacement functions for email masking using ALTER COLUMN SET MASK command.
D. Create SQL UDFs for row filtering based on user region and column masking based on group membership, then apply them using ALTER TABLE SET ROW FILTER and ALTER COLUMN SET MASK commands.

정답：D

설명：
Unity Catalog enforces fine-grained access control by applying SQL UDF-based row filters and column masks directly at the table level. Row filter UDFs can restrict visible rows based on the user's region, while column mask UDFs can dynamically mask sensitive fields like email addresses for non-admin users. Applying them with ALTER TABLE SET ROW FILTER and ALTER COLUMN SET MASK ensures centralized, consistent enforcement of data protection policies across all access paths.

질문 # 23
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.
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Which command should be removed from the notebook before scheduling it as a job?

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

정답：D

설명：
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 Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from display interactive visualizations like display(), it can still be informative for understanding how Spark is executing the operations on your DataFrame.

질문 # 24
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Databricks-Certified-Data-Engineer-Professional시험대비공부문제시험덤프자료

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

최신 Databricks-Certified-Data-Engineer-Professional시험대비 공부문제 덤프자료

최신 Databricks Certification Databricks-Certified-Data-Engineer-Professional 무료샘플문제 (Q19-Q24):