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

SectionObjectives
Data Processing and Transformations- User-defined functions (UDFs)
- Apache Spark SQL operations (joins, aggregations, filtering)
- PySpark DataFrame transformations
- Delta Lake fundamentals (tables, transactions, optimization)
Databricks Lakehouse Platform Fundamentals- Clusters, notebooks, and basic Databricks environment usage
- Workspace, architecture, and core platform concepts
Data Ingestion and ELT Development- ETL patterns and transformations
- Data ingestion using Spark SQL and PySpark
- Handling structured and semi-structured data
Productionizing Data Pipelines- Pipeline deployment and operationalization
- Databricks Workflows / Jobs orchestration
- Scheduling and monitoring jobs
Data Governance and Quality- Data access control and governance
- Unity Catalog basics
- Data quality concepts and management

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q108-Q113):

NEW QUESTION # 108
A data engineer needs to support a Unity Catalog Delta table catalog.schema.sales that is currently registered as an external table. New requirements state:
- Automatic file cleanup after a drop is handled by the platform.
- Minimal downtime during migration while keeping the same table name,
permissions, and history.
- Enforced name-based access through Unity Catalog (no path-based
access).
Which action should the data engineer take to meet the requirements?

Answer: B

Explanation:
ALTER TABLE catalog.schema.sales SET MANAGED converts the external Delta table in place while retaining its name, permissions, configuration, views, and history, with minimal downtime.
As a managed table, Unity Catalog controls its storage lifecycle, including cleanup after deletion, and workloads should access it by its three-part table name.


NEW QUESTION # 109
A data engineer has three tables in a Delta Live Tables (DLT) pipeline. They have configured the pipeline to drop invalid records at each table. They notice that some data is being dropped due to quality concerns at some point in the DLT pipeline. They would like to determine at which table in their pipeline the data is being dropped.
Which of the following approaches can the data engineer take to identify the table that is dropping the records?

Answer: A

Explanation:
One of the features of DLT is that it provides data quality metrics for each dataset in the pipeline, such as the number of records that pass or fail expectations, the number of records that are dropped, and the number of records that are written to the target. These metrics can be accessed from the DLT pipeline page, where the data engineer can click on each table and view the data quality statistics for the latest update or any previous update. This way, they can identify which table is dropping the records and why. Reference:
Monitor Delta Live Tables pipelines
Manage data quality with Delta Live Tables


NEW QUESTION # 110
A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.
Which of the following Git operations does the data engineer need to run to accomplish this task?

Answer: A

Explanation:
To sync a Databricks Repo with the changes from a central Git repository, the data engineer needs to run the Git pull operation. This operation fetches the latest updates from the remote repository and merges them with the local repository. The data engineer can use the Pull button in the Databricks Repos UI, or use the git pull command in a terminal session. The other options are not relevant for this task, as they either push changes to the remote repository (Push), combine two branches (Merge), save changes to the local repository (Commit), or create a new local repository from a remote one (Clone). Reference:
Run Git operations on Databricks Repos
Git pull


NEW QUESTION # 111
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?

Answer: D

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


NEW QUESTION # 112
A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?

Answer: A

Explanation:
The VACUUM command is used to remove files that are no longer referenced by a Delta table and are older than the retention threshold1. The default retention period is 7 days2, but it can be changed by setting the delta.logRetentionDuration and delta.deletedFileRetentionDuration configurations3. If the VACUUM command was run on the table with a retention period shorter than 3 days, then the data files that were needed to restore the table to a 3-day-old version would have been deleted. The other commands do not delete data files from the table. The TIME TRAVEL command is used to query a historical version of the table4. The DELETE HISTORY command is not a valid command in Delta Lake. The OPTIMIZE command is used to improve the performance of the table by compacting small files into larger ones5. The HISTORY command is used to retrieve information about the operations performed on the table. References: 1: VACUUM | Databricks on AWS 2: Work with Delta Lake table history | Databricks on AWS 3: [Delta Lake configuration | Databricks on AWS] 4: Work with Delta Lake table history - Azure Databricks 5: [OPTIMIZE | Databricks on AWS] : [HISTORY | Databricks on AWS]


NEW QUESTION # 113
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