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

Section	Weight	Objectives
Topic 1: Data Processing & Transformations	31%	- Data transformation techniques 1. Aggregations, joins, and window functions 2. Complex data processing logic 3. DataFrame operations and transformations - Query optimization and performance 1. Optimization strategies 2. Understanding query plans
Topic 2: Data Governance & Quality	11%	- Unity Catalog implementation 1. Permissions and access control 2. Data governance model - Data quality and reliability 1. Data validation and quality checks 2. Schema enforcement and evolution
Topic 3: Development and Ingestion	30%	- Data ingestion patterns and methods 1. Connecting to external data sources 2. Delta Lake basics and usage 3. Batch and streaming ingestion - Notebook development fundamentals 1. Data exploration and validation 2. Using PySpark and Spark SQL
Topic 4: Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Workspace navigation and management 2. Data layout and optimization: partitioning, file sizing, caching 3. Compute options: clusters, SQL warehouses, serverless
Topic 5: Productionizing Data Pipelines	18%	- Deployment and CI/CD 1. Databricks Asset Bundles 2. Version control integration - Monitoring and troubleshooting 1. Pipeline reliability and recovery 2. Logging and error handling - Workflow orchestration 1. Lakeflow Jobs creation and management 2. Scheduling, triggers, and dependencies

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Databricks Certified Data Engineer Associate Exam 認定 Databricks-Certified-Data-Engineer-Associate 試験問題 (Q134-Q139):

質問 # 134
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. org.apache.spark.sql.jdbc
B. DELTA
C. org.apache.spark.sql.sqlite
D. sqlite
E. autoloader

正解：D

解説：
In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is "sqlite" because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, "sqlite" would be appropriate here. Reference:
Create a table using JDBC
JDBC connection string
SQLite JDBC driver

質問 # 135
A data engineer is decommissioning a sandbox schema in Unity Catalog. Some tables are ephemeral staging outputs that can be safely removed entirely, but a few tables point at shared cloud storage used by downstream jobs outside Databricks. The engineer must avoid deleting any shared files when cleaning up catalog objects.
How does Unity Catalog behave when dropping Managed vs External tables?

A. Drop all tables; Databricks will only remove metadata for both managed and external tables
B. Drop managed staging tables to remove data and metadata, and drop external tables to remove only metadata
C. Drop external tables first to delete their files, then drop managed tables to keep their files for recovery
D. Drop managed tables that are ephemeral and drop external tables; files for both remain for 7 days

正解：B

解説：
In Unity Catalog, managed tables and external tables have different lifecycle behavior. When you drop a managed table , Databricks removes the table metadata and also schedules deletion of the underlying managed data files according to the managed-table retention behavior. By contrast, when you drop an external table , Unity Catalog removes only the table metadata and does not delete the underlying files in cloud storage. This distinction exists specifically so that external data can remain available to systems outside Databricks. In the scenario described, ephemeral staging outputs that should disappear completely are best represented by managed tables, while tables backed by shared cloud storage should be external tables so that dropping them does not remove files used by downstream jobs. Therefore, option C is the correct answer.
Options A, B, and D contradict documented Unity Catalog behavior because external table files are not automatically deleted, while managed table data is lifecycle-managed by Databricks.
=========

質問 # 136
Which of the following data workloads will utilize a Gold table as its source?

A. A job that aggregates uncleaned data to create standard summary statistics
B. A job that queries aggregated data designed to feed into a dashboard
C. A job that ingests raw data from a streaming source into the Lakehouse
D. A job that cleans data by removing malformatted records
E. A job that enriches data by parsing its timestamps into a human-readable format

正解：B

解説：
A Gold table is a table that contains highly refined and aggregated data that powers analytics, machine learning, and production applications. It represents data that has been transformed into knowledge, rather than just information. A Gold table is typically the final output of a medallion lakehouse architecture, where data flows from Bronze to Silver to Gold tables, with each layer improving the structure and quality of data. A job that queries aggregated data designed to feed into a dashboard is an example of a data workload that will utilize a Gold table as its source, as it requires data that is ready for consumption and analysis. The other options are either data workloads that will use a Bronze or Silver table as their source, or data workloads that will produce a Gold table as their output. References: Databricks Documentation - What is the medallion lakehouse architecture?, Databricks Documentation - What is a Medallion Architecture?, K21Academy - Delta Lake Architecture & Azure Databricks Workspace.

質問 # 137
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?

A. The ability to manipulate the same data using a variety of languages
B. The ability to collaborate in real time on a single notebook
C. The ability to support batch and streaming workloads
D. The ability to set up alerts for query failures
E. The ability to distribute complex data operations

正解：C

解説：
Explanation
Delta Lake is a key component of the Databricks Lakehouse Platform that provides several benefits, and one of the most significant benefits is its ability to support both batch and streaming workloads seamlessly. Delta Lake allows you to process and analyze data in real-time (streaming) as well as in batch, making it a versatile choice for various data processing needs. While the other options may be benefits or capabilities of Databricks or the Lakehouse Platform in general, they are not specifically associated with Delta Lake.

質問 # 138
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?

A. Dashboards
B. Databricks Filesystem
C. Jobs
D. Data Explorer
E. Repos

正解：D

解説：
Data Explorer is a graphical interface that allows you to browse, create, and manage data objects such as databases, tables, and views in your workspace. You can also review and modify the permissions on these data objects using Data Explorer. To access Data Explorer, you can click on the Data icon in the sidebar, or use the
%sql magic command in a notebook. You can then select a database and a table, and click on the Permissions tab to view and edit the access control lists (ACLs) for the table. You can also use SQL commands such as SHOW GRANT and GRANT to query and modify the permissions on a Delta table. References:
* Data Explorer
* Access control for Delta tables
* SHOW GRANT
* [GRANT]

質問 # 139
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