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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Lakehouse Platform Concepts | 10-15% | - Explain data governance and security concepts - Understand the Lakehouse architecture and its benefits - Describe key Databricks Lakehouse platform components |
| Topic 2: Data Pipeline Architecture | 15-20% | - Understand ELT vs ETL patterns - Monitor and optimize pipeline performance - Design data pipelines for batch and streaming - Implement incremental data processing |
| Topic 3: Delta Lake Fundamentals | 20-25% | - Explain Delta Lake features and benefits - Understand ACID transactions and time travel - Write to and read from Delta tables - Create and manage Delta tables |
| Topic 4: Spark SQL and DataFrames | 15-20% | - Handle null values and data quality - Aggregate and group data - Join and union DataFrames - Write and execute Spark SQL queries |
| Topic 5: Apache Spark Data Processing Fundamentals | 20-25% | - Create and use Spark DataFrames - Work with structured data types (arrays, maps, structs) - Apply transformations and actions on DataFrames - Use Spark SQL for data processing |
| Topic 6: Python for Data Engineering | 10-15% | - Work with Spark APIs in Python - Use PySpark for data processing - Implement user-defined functions (UDFs) |
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NEW QUESTION # 245
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: B
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. References: Data Engineer Associate Exam Guide, CREATE TABLE USING (Databricks SQL)
NEW QUESTION # 246
A data engineer needs to enforce row-level security on main.secure.events(region STRING, event_id STRING). Members of the account group all_regions must see all rows. All other users must see only rows where region = 'EU'.
Which SQL sequence satisfies the requirement?
Answer: A
Explanation:
A row-filter function must return a Boolean value indicating whether each row is visible to the current user. The function in option A returns true for members of the account-level all_regions group, allowing them to see every row. For other users, it returns the result of region = 'EU', limiting visibility to European records. is_account_group_member checks direct or indirect membership in an account-level group. The ALTER TABLE ... SET ROW FILTER ... ON (region) statement then associates the function with the table and passes the region column to it for every query. A column mask transforms displayed column values rather than removing rows. The object is a table, not a view, and USING COLUMNS is not the correct row-filter attachment syntax. Therefore, option A is correct.
NEW QUESTION # 247
Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?
Answer: C
Explanation:
Explanation
https://www.databricks.com/glossary/what-is-parquet#:~:text=Columnar%20storage%20like%20Apache%20Par Columnar storage like Apache Parquet is designed to bring efficiency compared to row-based files like CSV.
When querying, columnar storage you can skip over the non-relevant data very quickly. As a result, aggregation queries are less time-consuming compared to row-oriented databases.
NEW QUESTION # 248
A data engineer is attempting to drop a Spark SQL table my_table. The data engineer wants to delete all table metadata and data.
They run the following command:
DROP TABLE IF EXISTS my_table
While the object no longer appears when they run SHOW TABLES, the data files still exist.
Which of the following describes why the data files still exist and the metadata files were deleted?
Answer: B
Explanation:
Explanation
The reason why the data files still exist while the metadata files were deleted is because the table was external.
When a table is external in Spark SQL (or in other database systems), it means that the table metadata (such as schema information and table structure) is managed externally, and Spark SQL assumes that the data is managed and maintained outside of the system. Therefore, when you execute a DROP TABLE statement for an external table, it removes only the table metadata from the catalog, leaving the data files intact. On the other hand, for managed tables (option E), Spark SQL manages both the metadata and the data files. When you drop a managed table, it deletes both the metadata and the associated data files, resulting in a complete removal of the table.
NEW QUESTION # 249
A data engineer needs to reduce the number of small files generated during streaming ingestion into Delta tables. Small files are causing performance issues during downstream queries. Which Delta Lake feature automatically compacts small files during writes?
Answer: B
NEW QUESTION # 250
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