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| Section | Weight | Objectives |
|---|---|---|
| Spark SQL and DataFrames | 15-20% | - Aggregate and group data - Handle null values and data quality - Write and execute Spark SQL queries - Join and union DataFrames |
| Python for Data Engineering | 10-15% | - Work with Spark APIs in Python - Implement user-defined functions (UDFs) - Use PySpark for data processing |
| Apache Spark Data Processing Fundamentals | 20-25% | - Use Spark SQL for data processing - Work with structured data types (arrays, maps, structs) - Create and use Spark DataFrames - Apply transformations and actions on DataFrames |
| Delta Lake Fundamentals | 20-25% | - Explain Delta Lake features and benefits - Create and manage Delta tables - Write to and read from Delta tables - Understand ACID transactions and time travel |
| Lakehouse Platform Concepts | 10-15% | - Understand the Lakehouse architecture and its benefits - Describe key Databricks Lakehouse platform components - Explain data governance and security concepts |
| Data Pipeline Architecture | 15-20% | - Implement incremental data processing - Monitor and optimize pipeline performance - Understand ELT vs ETL patterns - Design data pipelines for batch and streaming |
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NEW QUESTION # 309
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: D
NEW QUESTION # 310
A data engineer has left the organization. The data team needs to transfer ownership of the data engineer's Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team.
Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?
Answer: C
Explanation:
The workspace administrator is the only individual who can transfer ownership of the Delta tables in Data Explorer, assuming the original data engineer no longer has access. The workspace administrator has the highest level of permissions in the workspace and can manage all resources, users, and groups. The other options are either not possible or not sufficient to perform the ownership transfer. The Databricks account representative is not involved in the workspace management. The transfer is possible and not dependent on the original data engineer. The new lead data engineer may not have the necessary permissions to access or modify the Delta tables, unless granted by the workspace administrator or the original data engineer before leaving. References: Workspace access control, Manage Unity Catalog object ownership.
NEW QUESTION # 311
A data engineer needs to create a table in Databricks using data from a CSV file at location
/path/to/csv.
They run the following command:
Which of the following lines of code fills in the above blank to successfully complete the task?
Answer: B
NEW QUESTION # 312
A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:
DROP TABLE IF EXISTS my_table;
After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.
Which of the following describes why all of these files were deleted?
Answer: D
Explanation:
Explanation
managed tables files and metadata are managed by metastore and will be deleted when the table is dropped .
while external tables the metadata is stored in a external location. hence when a external table is dropped you clear off only the metadata and the files (data) remain.
NEW QUESTION # 313
A company uses Delta Sharing to collaborate with partners across different cloud providers and geographic regions. What will result in additional costs due to cross-region or egress fees?
Answer: C
Explanation:
Databricks documents that Delta Sharing does not require data replication, but cloud providers can still charge data egress fees when data is shared across clouds or across geographic regions. Databricks specifically states that sharing within the same region incurs no egress cost, while cross-cloud or cross-region transfers can create additional charges from the underlying cloud provider. That means option A is correct. Options B and C describe same-cloud or same-region use cases that do not trigger the cross-region/cross-cloud egress pattern Databricks calls out. Option D is unrelated to the main documented billing driver for Delta Sharing costs. The important principle is that Delta Sharing itself is designed to avoid replication overhead, but the physical movement of data between cloud boundaries or regions can still result in vendor networking charges. Therefore, when sharing data with external partners in other regions or clouds, engineers should plan for possible egress costs and monitor them accordingly.
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NEW QUESTION # 314
......
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