Databricks-Certified-Data-Engineer-Associate考題 - Databricks-Certified-Data-Engineer-Associate試題

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

Section	Objectives
Productionizing Data Pipelines	- Pipeline deployment and operationalization - Scheduling and monitoring jobs - Databricks Workflows / Jobs orchestration
Data Governance and Quality	- Data access control and governance - Data quality concepts and management - Unity Catalog basics
Data Processing and Transformations	- User-defined functions (UDFs) - Delta Lake fundamentals (tables, transactions, optimization) - Apache Spark SQL operations (joins, aggregations, filtering) - PySpark DataFrame transformations
Databricks Lakehouse Platform Fundamentals	- Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts
Data Ingestion and ELT Development	- Data ingestion using Spark SQL and PySpark - ETL patterns and transformations - Handling structured and semi-structured data

>> Databricks-Certified-Data-Engineer-Associate考題 <<

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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Associate 免費考試真題 (Q56-Q61):

問題 #56
Which of the following describes the storage organization of a Delta table?

A. Delta tables are stored in a single file that contains only the data stored within the table.
B. Delta tables are stored in a collection of files that contain only the data stored within the table.
C. Delta tables are stored in a collection of files that contain data, history, metadata, and other attributes.
D. Delta tables store their data in a single file and all metadata in a collection of files in a separate location.
E. Delta tables are stored in a single file that contains data, history, metadata, and other attributes.

答案：C

解題說明：
Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is open source software that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling1. Delta Lake stores its data and metadata in a collection of files in a directory on a cloud storage system, such as AWS S3 or Azure Data Lake Storage2. Each Delta table has a transaction log that records the history of operations performed on the table, such as insert, update, delete, merge, etc. The transaction log also stores the schema and partitioning information of the table2. The transaction log enables Delta Lake to provide ACID guarantees, time travel, schema enforcement, and other features1. Reference:
What is Delta Lake? | Databricks on AWS
Quickstart - Delta Lake Documentation

問題 #57
A data engineer is attempting to grant a user access to a view. They successfully run GRANT SELECT ON VIEW sales_catalog.market_data.summary TO analyst_group.
However, the analyst still receives an "Insufficient Permissions" error when trying to query the view.
In which order should the engineer verify the Hierarchy of Securable Objects to ensure the analyst has the necessary usage permissions?

A. METASTORE -> TABLE -> VIEW
B. STORAGE CREDENTIAL -> EXTERNAL LOCATION -> VIEW
C. WORKSPACE -> CLUSTER -> VIEW
D. CATALOG -> SCHEMA -> VIEW

答案：D

解題說明：
To query the view, the analyst needs USE CATALOG on sales_catalog, then USE SCHEMA on market_data, and SELECT on summary. Privileges on the underlying tables are not required for querying a Unity Catalog view.

問題 #58
Which of the following must be specified when creating a new Delta Live Tables pipeline?

A. A location of a target database for the written data
B. The preferred DBU/hour cost
C. At least one notebook library to be executed
D. A key-value pair configuration
E. A path to cloud storage location for the written data

答案：C

解題說明：
Explanation
https://docs.databricks.com/en/delta-live-tables/tutorial-pipelines.html

問題 #59
A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. The data engineer wants to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing.
Which role must be in place to perform these actions across the metastore?

A. Catalog Owner, because catalog owners can transfer any object in any catalog in the metastore.
B. Account Admin, because account admins can only create metastores but cannot change ownership of catalogs.
C. Metastore Admin, because metastore admins can transfer ownership and manage privileges across all metastore objects, including shares and recipients.
D. Workspace Admin, because workspace admins can transfer ownership of any Unity Catalog object.

答案：C

解題說明：
In Unity Catalog, the Metastore Admin role has the highest level of authority within a metastore and is responsible for governing all data assets registered in that metastore. Metastore admins can transfer ownership of catalogs, manage privileges across all securable objects (catalogs, schemas, tables, views), and administer storage credentials, external locations, and Delta Sharing objects such as shares and recipients. This makes the role essential when a platform owner departs and continuity must be maintained at an organizational level. Workspace admins (option B) manage workspace-level settings but do not automatically have authority over Unity Catalog ownership unless explicitly granted. Catalog owners (option D) are limited to a single catalog and cannot perform actions across the entire metastore. Account admins (option A) operate at the account level and are primarily responsible for creating metastores and assigning admins, not for day-to-day governance within an existing metastore. Databricks documentation clearly defines the Metastore Admin as the role required to centrally manage ownership, privileges, and data sharing across the metastore in Databricks.

問題 #60
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The cade block used by the data engineer is below:

If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

A. trigger(processingTime="5 seconds")
B. trigger("5 seconds")
C. trigger()
D. trigger(continuous="5 seconds")
E. trigger(once="5 seconds")

答案：A

解題說明：
Explanation
# ProcessingTime trigger with two-seconds micro-batch interval
df.writeStream \
format("console") \
trigger(processingTime='2 seconds') \
start()
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#triggers

問題 #61
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