Databricks-Certified-Data-Engineer-Professional Buch, Databricks-Certified-Data-Engineer-Professional Prüfungsaufgaben

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

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

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Databricks Databricks-Certified-Data-Engineer-Professional Prüfungsaufgaben, Databricks-Certified-Data-Engineer-Professional Prüfungsinformationen

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Databricks Certified Data Engineer Professional Exam Databricks-Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q246-Q251):

246. Frage
A data company uses Databricks Unity Catalog and has multiple enterprise data sources, including PostgreSQL, Snowflake, and SQL Server. The central data platform team wants to configure Lakehouse Federation so analysts can query external tables directly in Databricks using Databricks SQL, without duplicating data. Which steps are necessary to configure Lakehouse Federation in a secure and governed manner?

A. Configure connections and foreign catalog in Unity Catalog, then grant access to foreign catalogs, schemas, and tables using Unity Catalog permissions.
B. Mirror the external datasets into Delta Lake using Auto Loader, and govern them using Data Lineage and System Tables.
C. Create external locations and storage credentials to connect to each database, then register foreign tables in Unity Catalog.
D. Use Partner Connect to create linked datasets, and apply table ACLs at the source system to govern access through Databricks.

Antwort: A

Begründung:
Lakehouse Federation is configured by defining secure connections to external data sources and registering them as foreign catalogs in Unity Catalog. Access is then governed using Unity Catalog permissions at the catalog, schema, and table levels, enabling analysts to query external tables securely without data duplication.

247. Frage
In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

Antwort: E

Begründung:
https://docs.databricks.com/en/ingestion/auto-loader/schema.html

248. Frage
In a Databricks Asset Bundle project, in the file resources/app.yml, the data engineer would like to deploy a Databricks Apps databricks_app_deployed and Volume volume_deployed and grant the Service Principal behind Databricks Apps permissions to READ and WRITE to the Volume.
How should the data engineer achieve the deployment?

Antwort: D

Begründung:
This configuration correctly references the service principal created for the Databricks App using the deployed app resource identifier, and it grants the required READ and WRITE privileges at the Volume level. The privileges are specified using the correct Volume-specific permissions, ensuring the Databricks App can securely access the Volume after deployment.

249. Frage
What is the first line of a Databricks Python notebook when viewed in a text editor?

A. %python
B. -- Databricks notebook source
C. # MAGIC %python
D. # Databricks notebook source
E. // Databricks notebook source

Antwort: D

Begründung:
https://docs.databricks.com/en/notebooks/notebook-export-import.html#import-a-file-and-convert- it-to-a-notebook

250. Frage
A Delta Lake table was created with the below query:
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Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

A. The table reference in the metastore is updated and no data is changed.
B. The table reference in the metastore is updated and all data files are moved.
C. All related files and metadata are dropped and recreated in a single ACID transaction.
D. A new Delta transaction log Is created for the renamed table.
E. The table name change is recorded in the Delta transaction log.

Antwort: A

Begründung:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly.

251. Frage
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