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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q263-Q268):

NEW QUESTION # 263
A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:

A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?

Answer: D

Explanation:
In Databricks, when creating a table, you can add a comment to columns or the entire table to provide more information about the data it contains. In this case, since it's organization policy to indicate that the new table includes personally identifiable information (PII), option D is correct. The line of code would be added after defining the table structure and before closing with a semicolon. Reference: Data Engineer Associate Exam Guide, CREATE TABLE USING (Databricks SQL)


NEW QUESTION # 264
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?

Answer: B


NEW QUESTION # 265
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 # 266
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

Answer: E

Explanation:
Explanation
In a Delta Live Table pipeline running in Continuous Pipeline Mode, when you click Start to update the pipeline, the following outcome is expected: All datasets defined using STREAMING LIVE TABLE and LIVE TABLE against Delta Lake table sources will be updated at set intervals. The compute resources will be deployed for the update process and will be active during the execution of the pipeline. The compute resources will be terminated when the pipeline is stopped or shut down. This mode allows for continuous and periodic updates to the datasets as new data arrives or changes in the underlying Delta Lake tables occur. The compute resources are provisioned and utilized during the update intervals to process the data and perform the necessary operations.


NEW QUESTION # 267
Which of the following SQL keywords can be used to convert a table from a long format to a wide format?

Answer: D

Explanation:
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.
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]


NEW QUESTION # 268
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