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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Associate 免費考試真題 (Q125-Q130):

問題 #125
Which of the following describes the relationship between Bronze tables and raw data?

答案：A

解題說明：
Bronze tables are the first layer of a medallion architecture, which is a data design pattern used to organize data in a lakehouse. Bronze tables contain raw data ingested from various sources, such as RDBMS data, JSON files, IoT data, etc. The table structures in this layer correspond to the source system table structures "as-is", along with any additional metadata columns that capture the load date/time, process ID, etc. The only transformation applied to the raw data in this layer is to apply a schema, which defines the column names and data types of the table. The schema can be inferred from the data source or specified explicitly. Applying a schema to the raw data enables the use of SQL and other structured query languages to access and analyze the data. Therefore, option E is the correct answer. Reference: What is a Medallion Architecture?, Raw Data Ingestion into Delta Lake Bronze tables using Azure Synapse Mapping Data Flow, Apache Spark + Delta Lake concepts, Delta Lake Architecture & Azure Databricks Workspace.


問題 #126
Which of the following is hosted completely in the control plane of the classic Databricks architecture?

答案：B

解題說明：
The control plane in the classic Databricks architecture is responsible for managing the Databricks workspace, user and group management, and cluster management, among other things. The Databricks web application is a part of the control plane that enables users to interact with the workspace, create and manage clusters, and work with notebooks, jobs, and data.
Worker nodes and driver nodes are part of the data plane, which is responsible for executing data processing tasks. JDBC data sources and the Databricks Filesystem are services that are used by both the control plane and the data plane.


問題 #127
A data engineer inherits a Delta table with historical partitions by country that are badly skewed. Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve.
Which two actions should the data engineer take? (Choose 2)

答案：A,C

解題說明：
Liquid Clustering replaces traditional partitioning and ZORDER optimization by automatically organizing data according to clustering keys. It supports evolving clustering strategies without requiring a full table rewrite. To maintain cluster balance and improve performance, the OPTIMIZE command should be run periodically. OPTIMIZE groups data files by clustering keys and helps reduce small file overhead.
Reference Source: Databricks Delta Lake Guide - "Use Liquid Clustering for Tables" and "OPTIMIZE Command for File Compaction and Data Layout."


問題 #128
A data engineer is preparing a Declarative Automation Bundle, formerly known as a Databricks Asset Bundle, to deploy a Lakeflow pipeline.
To ensure that the pipeline is deployed to the correct environment, where should workspace-specific configurations, such as the workspace host URL and root storage path, be defined within the bundle project?

答案：C

解題說明：
Workspace-specific deployment settings should be defined under each environment in the targets section of databricks.yml. A bundle target represents a deployment environment such as development, staging, or production. Each target can contain its own workspace mapping, including the workspace host and an optional root_path. This structure allows one bundle codebase to be deployed predictably to different environments without embedding infrastructure details in notebooks or pipeline source code. A README file is documentation and is not interpreted as bundle configuration. Hardcoding workspace details in Python couples transformation logic to a particular environment. Global local environment variables are machine-specific and do not provide a complete, version-controlled bundle definition. Therefore, option C correctly places the workspace-specific settings in the declarative configuration used during validation and deployment.


問題 #129
In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?

答案：A

解題說明：
The MERGE INTO command is used to perform upserts, which are a combination of insertions and updates, based on a source table into a target Delta table1. The MERGE INTO command can handle scenarios where the target table cannot contain duplicate records, such as when there is a primary key or a unique constraint on the target table. The MERGE INTO command can match the source and target rows based on a merge condition and perform different actions depending on whether the rows are matched or not. For example, the MERGE INTO command can update the existing target rows with the new source values, insert the new source rows that do not exist in the target table, or delete the target rows that do not exist in the source table1.
The INSERT INTO command is used to append new rows to an existing table or create a new table from a query result2. The INSERT INTO command does not perform any updates or deletions on the existing target table rows. The INSERT INTO command can handle scenarios where the location of the data needs to be changed, such as when the data needs to be moved from one table to another, or when the data needs to be partitioned by a certain column2. The INSERT INTO command can also handle scenarios where the target table is an external table, such as when the data is stored in an external storage system like Amazon S3 or Azure Blob Storage3. The INSERT INTO command can also handle scenarios where the source table can be deleted, such as when the source table is a temporary table or a view4. The INSERT INTO command can also handle scenarios where the source is not a Delta table, such as when the source is a Parquet, CSV, JSON, or Avro file5.
:
1: MERGE INTO | Databricks on AWS
2: [INSERT INTO | Databricks on AWS]
3: [External tables | Databricks on AWS]
4: [Temporary views | Databricks on AWS]
5: [Data sources | Databricks on AWS]


問題 #130
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