Databricks Updated Databricks-Certified-Data-Engineer-Associate Exam Questions and Answers by haniya

DESCRIBE HISTORY my_table returns the Delta table’s operation history. Its result includes information such as the table version, operation timestamp, user identity when available, operation name, parameters, notebook or job details, and associated metrics. The engineer can filter the returned timestamps to analyze operations performed during the last seven days. SHOW TBLPROPERTIES displays configured table properties rather than transactional history. DESCRIBE DETAIL returns the current table metadata and storage characteristics, not a chronological audit of changes. Directly querying the _delta_log path is unnecessary, depends on file-level implementation details, and the displayed path is not the supported SQL statement for this audit requirement. Therefore, option C provides the supported and structured record of past Delta table operations.

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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.

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Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as commit, pull, push, branch management, and visual comparison of diffs when committing1. However, merge is not supported in the Git dialog2. You need to use the Repos UI or your Git provider to merge branches3. Merge is a way to combine the commit history from one branch into another branch1. During a merge, a merge conflict is encountered when Git cannot automatically combine code from one branch into another. Merge conflicts require manual resolution before a merge can be completed1. References: 4: Run Git operations on Databricks Repos4, 1: CI/CD techniques with Git and Databricks Repos1, 3: Collaborate in Repos3, 2: Databricks Repos - What it is and how we can use it2.

Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as commit, pull, push, merge, and branch management. However, to clone a remote Git repository to a Databricks repo, you must use the Databricks UI or API. You cannot clone a Git repo using the CLI through a cluster’s web terminal, as the files won’t display in the Databricks UI1. References: 1: Run Git operations on Databricks Repos | Databricks on AWS2