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GAQM Databricks-Certified-Data-Engineer-Associate（Databricks Certified Data Engineer Associate）認定試験は、データエンジニアリングの分野で働く個人のスキルと知識を評価する専門の認定プログラムです。この認定は、データエンジニアがDatabricksテクノロジーを使用してデータパイプラインとデータウェアハウスを設計、構築、および維持する能力を検証します。試験には、データ取込み、データ変換、データモデリング、データウェアハウジング、データ品質など、さまざまなトピックが含まれています。

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Databricks Certified Data Engineer Associate Exam 認定 Databricks-Certified-Data-Engineer-Associate 試験問題 (Q211-Q216):

質問 # 211
A data engineer is standardizing repository layouts for multiple teams adopting Databricks Asset Bundles.
The engineer wants to ensure every project has a single authoritative configuration file at the repository root that defines the bundle name, targets, workspace settings, permissions, and resource mappings (for jobs and pipelines).
Which strategy should the data engineer use to meet this goal?

正解：A

解説：
In Databricks Asset Bundles , the databricks.yml file located at the repository root serves as the single authoritative configuration file for the entire project. This file defines key elements such as the bundle name, deployment targets (for example, dev, test, prod), workspace configurations, permissions, and resource definitions including jobs and pipelines. Databricks documentation specifies that there must be one primary databricks.yml file at the root , ensuring consistency and simplifying deployment processes. However, to support modularity and maintainability, this root configuration file can reference additional YAML files using the include mapping. This allows teams to organize configurations across directories while still maintaining a single source of truth. Option A is incorrect because multiple root-level configuration files are not supported as independent authorities. Option C is invalid since configuration files are not restricted to hidden directories. Option D introduces ambiguity and contradicts the requirement for a single authoritative configuration. Therefore, using one root-level configuration file with optional includes is the correct and recommended approach.


質問 # 212
A data engineer is troubleshooting a Lakeflow Spark Declarative Pipelines (SDP) where several rows are dropped by expectations. The product owner requests details on which expectation failed most frequently during the last run and the corresponding affected table. The engineer wants to use Databricks' built-in observability data to answer this without adding new logging code. How should the data engineer troubleshoot SDP to verify the expectation failure?

正解：D

解説：
Lakeflow Spark Declarative Pipelines automatically record expectation metrics and outcomes in the pipeline event log, which can be queried to identify failure counts and affected tables without adding custom logging.


質問 # 213
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?

正解：B


質問 # 214
A data engineer is working in a Python notebook on Databricks to process data, but notices that the output is not as expected. The data engineer wants to investigate the issue by stepping through the code and checking the values of certain variables during execution.
Which tool should the data engineer use to inspect the code execution and variables in real-time?

正解：D


質問 # 215
A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

正解：E

解説：
Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks


質問 # 216
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