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

SectionObjectives
Productionizing Data Pipelines- Scheduling and monitoring jobs
- Databricks Workflows / Jobs orchestration
- Pipeline deployment and operationalization
Data Ingestion and ELT Development- Data ingestion using Spark SQL and PySpark
- ETL patterns and transformations
- Handling structured and semi-structured data
Databricks Lakehouse Platform Fundamentals- Clusters, notebooks, and basic Databricks environment usage
- Workspace, architecture, and core platform concepts
Data Processing and Transformations- Apache Spark SQL operations (joins, aggregations, filtering)
- Delta Lake fundamentals (tables, transactions, optimization)
- User-defined functions (UDFs)
- PySpark DataFrame transformations
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 (Q20-Q25):

NEW QUESTION # 20
An organization plans to share a large dataset stored in a Databricks workspace on AWS with a partner organization whose Databricks workspace is hosted on Azure. The data engineer wants to minimize data transfer costs while ensuring secure and efficient data sharing. Which strategy will reduce data egress costs associated with cross-cloud data sharing?

Answer: D

Explanation:
Delta Sharing natively supports cross-cloud data access without requiring data duplication or special networking setups. It allows secure sharing of datasets across different cloud providers while minimizing egress costs, since recipients query the data directly from its source location rather than transferring large volumes unnecessarily.


NEW QUESTION # 21
A data engineer is reviewing the documentation on audit logs in Databricks for compliance purposes and needs to understand the format in which audit logs output events.
How are events formatted in Databricks audit logs?

Answer: B


NEW QUESTION # 22
Identify the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATE for a constraint violation.
A data engineer has created an ETL pipeline using Delta Live table to manage their company travel reimbursement detail, they want to ensure that the if the location details has not been provided by the employee, the pipeline needs to be terminated.
How can the scenario be implemented?

Answer: B

Explanation:
In the given scenario, the data engineer wants to ensure that if the location details have not been provided by the employee, the ETL pipeline should be terminated. This means that the data engineer needs a strict enforcement of the constraint, and the pipeline should fail if there is a violation. The correct option for this is:
CONSTRAINT valid_location EXPECT (location != NULL) ON VIOLATION FAIL UPDATE: This will ensure that if the location is NULL, the pipeline will fail the update, effectively terminating the process as required.
The other options are incorrect because:
CONSTRAINT valid_location EXPECT (location = NULL): This is incorrect because it expects the value to be NULL, which is the opposite of what is intended here.
CONSTRAINT valid_location EXPECT (location != NULL) ON DROP ROW): This would drop rows that violate the constraint, but it would not terminate the pipeline, which does not meet the requirement.
CONSTRAINT valid_location EXPECT (location != NULL) ON VIOLATION FAIL: This would terminate the pipeline, but the correct syntax is ON VIOLATION FAIL UPDATE, which clearly indicates how to handle updates.


NEW QUESTION # 23
A data engineer runs a statement every day to copy the previous day's sales into the table transactions. Each day's sales are in their own file in the location "/transactions/raw".
Today, the data engineer runs the following command to complete this task:

After running the command today, the data engineer notices that the number of records in table transactions has not changed.
Which of the following describes why the statement might not have copied any new records into the table?

Answer: D


NEW QUESTION # 24
A pipeline uses COPY INTO to ingest CSV files from cloud object storage into a Unity Catalog Delta table. Some files are occasionally re-uploaded with corrections using the same filename.
The engineer needs the corrected data to be ingested as soon as it becomes available.
What should the engineer do?

Answer: A

Explanation:
COPY INTO is idempotent by default and tracks files that have already been loaded into the target table. A corrected file uploaded under the same name and path can therefore be treated as previously processed and skipped. The reliable pattern is to publish the correction under a new unique filename so that ingestion detects it as a new file. The pipeline can then use a business key with MERGE, UPDATE, or equivalent transformation logic to apply the corrected values to the target Delta table. Overwriting or recreating the complete table risks unnecessary data loss, increases processing cost, and disrupts downstream consumers. Reloading every historical source file is also inefficient and can introduce duplicates. Therefore, option B supports incremental ingestion while applying corrections safely and efficiently.


NEW QUESTION # 25
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