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

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

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Databricks Certified Data Engineer Associate Exam 認定 Databricks-Certified-Data-Engineer-Associate 試験問題 (Q41-Q46):

質問 # 41
Which of the following SQL keywords can be used to convert a table from a long format to a wide format?

A. SUM
B. TRANSFORM
C. PIVOT
D. CONVERT
E. WHERE

正解：C

解説：
The SQL keyword that can be used to convert a table from a long format to a wide format is PIVOT. The PIVOT clause is used to rotate the rows of a table into columns of a new table1. The PIVOT clause can aggregate the values of a column based on the distinct values of another column, and use those values as the column names of the new table1. The PIVOT clause can be useful for transforming data from a long format, where each row represents an observation with multiple attributes, to a wide format, where each row represents an observation with a single attribute and multiple values2. For example, the PIVOT clause can be used to convert a table that contains the sales of different products by different regions into a table that contains the sales of each product by each region as separate columns1.
The other options are not suitable for converting a table from a long format to a wide format. CONVERT is a function that can be used to change the data type of an expression3. WHERE is a clause that can be used to filter the rows of a table based on a condition4. TRANSFORM is a keyword that can be used to apply a user-defined function to a group of rows in a table5. SUM is a function that can be used to calculate the total of a numeric column.
References:
* 1: PIVOT | Databricks on AWS
* 2: Reshaping Data - Long vs Wide Format | Databricks on AWS
* 3: CONVERT | Databricks on AWS
* 4: WHERE | Databricks on AWS
* 5: TRANSFORM | Databricks on AWS
* : [SUM | Databricks on AWS]

質問 # 42
A data engineer is preparing a Declarative Automation Bundle (formerly Databricks Asset Bundle) to deploy a Lakeflow pipeline. To ensure the pipeline is deployed to the correct environment, where should the workspace-specific configurations, such as the host URL and root storage path, be defined within the bundle project?

A. In the targets section of the databricks.yml file
B. Within the Python source code of the Lakeflow pipeline
C. In a global environment variable on the local development machine
D. Inside the README.md file of the bundle root directory

正解：A

解説：
Environment-specific workspace settings, including the workspace host and deployment root_path, should be defined or overridden under each environment in the targets section of databricks.yml.

質問 # 43
What is the primary function of the Silver layer in the Databricks medallion architecture?

A. Validate, clean, and deduplicate data for further processing
B. Aggregate and enrich data for business analytics
C. lngest raw data in its original state
D. Store historical data solely for auditing purposes

正解：A

質問 # 44
Which of the following must be specified when creating a new Delta Live Tables pipeline?

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

正解：E

解説：
Option E is the correct answer because it is the only mandatory requirement when creating a new Delta Live Tables pipeline. A pipeline is a data processing workflow that contains materialized views and streaming tables declared in Python or SQL source files. Delta Live Tables infers the dependencies between these tables and ensures updates occur in the correct order. To create a pipeline, you need to specify at least one notebook library to be executed, which contains the Delta Live Tables syntax. You can also specify multiple libraries of different languages within your pipeline. The other options are optional or not applicable for creating a pipeline. Option A is not required, but you can optionally provide a key-value pair configuration to customize the pipeline settings, such as the storage location, the target schema, the notifications, and the pipeline mode. Option B is not applicable, as the DBU/hour cost is determined by the cluster configuration, not the pipeline creation. Option C is not required, but you can optionally specify a storage location for the output data from the pipeline. If you leave it empty, the system uses a default location. Option D is not required, but you can optionally specify a location of a target database for the written data, either in the Hive metastore or the Unity Catalog.
Tutorial: Run your first Delta Live Tables pipeline, What is Delta Live Tables?, Create a pipeline, Pipeline configuration.

質問 # 45
A data engineer is onboarding a new Bronze ingestion pipeline in Databricks with Unity Catalog. The team wants Databricks to handle storage layout, apply platform optimizations over time, and simplify lifecycle management so that when a table is dropped, its underlying data is also cleaned up according to Databricks- managed retention policies.
Which table type should the data engineer create for these ingestion tables?

A. Managed tables so that Unity Catalog manages both metadata and underlying data lifecycle
B. External tables with a LOCATION pointing to an external volume for full control of file layout
C. Temporary views over files to avoid table-level governance and lifecycle coupling
D. Foreign tables federated from an external catalog to delegate optimization to the source system

正解：A

解説：
Databricks recommends managed tables when you want the platform to control both the table metadata and the underlying storage lifecycle. For Unity Catalog managed tables, Databricks manages storage layout and applies platform-managed optimization features over time. Databricks also documents that when you drop a managed table , the underlying cloud data is deleted automatically after the managed retention period, which simplifies cleanup and reduces storage administration. That behavior matches the requirement exactly.
External tables, by contrast, are used when you want to retain direct control over the storage path and lifecycle. Databricks explicitly states that dropping an external table removes only the metadata and does not delete the underlying files . Foreign tables are for federation scenarios against external systems, not for Bronze ingestion storage managed by Databricks. Temporary views also do not provide the desired governed lifecycle. Because the team wants Databricks-managed storage, managed retention, and simplified lifecycle handling, managed tables are the correct choice for these Bronze ingestion tables.
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質問 # 46
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