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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Lakehouse Platform Concepts | 10-15% | - Understand the Lakehouse architecture and its benefits - Describe key Databricks Lakehouse platform components - Explain data governance and security concepts |
| Topic 2: Delta Lake Fundamentals | 20-25% | - Write to and read from Delta tables - Explain Delta Lake features and benefits - Understand ACID transactions and time travel - Create and manage Delta tables |
| Topic 3: Python for Data Engineering | 10-15% | - Use PySpark for data processing - Implement user-defined functions (UDFs) - Work with Spark APIs in Python |
| Topic 4: Apache Spark Data Processing Fundamentals | 20-25% | - Work with structured data types (arrays, maps, structs) - Create and use Spark DataFrames - Use Spark SQL for data processing - Apply transformations and actions on DataFrames |
| Topic 5: Spark SQL and DataFrames | 15-20% | - Write and execute Spark SQL queries - Join and union DataFrames - Handle null values and data quality - Aggregate and group data |
| Topic 6: Data Pipeline Architecture | 15-20% | - Monitor and optimize pipeline performance - Implement incremental data processing - Understand ELT vs ETL patterns - Design data pipelines for batch and streaming |
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NEW QUESTION # 300
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: A
NEW QUESTION # 301
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
NEW QUESTION # 302
A data engineer is cleaning a Bronze table. The requirement is to eliminate rows where either the customer_email field or the customer_phone field is NULL. The cleaning must be performed in one operation using a single method call.
Which PySpark approach supports filtering multiple columns for NULL values in one call?
Answer: B
NEW QUESTION # 303
A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.
Which of the following describes why Auto Loader inferred all of the columns to be of the string type?
Answer: B
Explanation:
JSON data is a text-based format that represents data as a collection of name-value pairs. By default, when Auto Loader infers the schema of JSON data, it treats all columns as strings. This is because JSON data can have varying data types for the same column across different files or records, and Auto Loader does not attempt to reconcile these differences. For example, a column named "age" may have integer values in some files, but string values in others. To avoid data loss or errors, Auto Loader infers the column as a string type. However, Auto Loader also provides an option to infer more precise column types based on the sample data. This option is called cloudFiles.inferColumnTypes and it can be set to true or false. When set to true, Auto Loader tries to infer the exact data types of the columns, such as integers, floats, booleans, or nested structures. When set to false, Auto Loader infers all columns as strings. The default value of this option is false. Reference: Configure schema inference and evolution in Auto Loader, Schema inference with auto loader (non-DLT and DLT), Using and Abusing Auto Loader's Inferred Schema, Explicit path to data or a defined schema required for Auto loader.
NEW QUESTION # 304
A data engineer is managing a data pipeline in Databricks, where multiple Delta tables are used for various transformations. The team wants to track how data flows through the pipeline, including identifying dependencies between Delta tables, notebooks, jobs, and dashboards. The data engineer is utilizing the Unity Catalog lineage feature to monitor this process.
How does Unity Catalog's data lineage feature support the visualization of relationships between Delta tables, notebooks, jobs, and dashboards?
Answer: B
Explanation:
Databricks documentation for Unity Catalog lineage states that lineage is captured across queries run on Databricks , is supported for all languages , and is tracked down to the column level . It also explicitly says that lineage data includes related notebooks, jobs, and dashboards , and that the lineage can be visualized in Catalog Explorer in near real time as an interactive graph. This directly matches option D. The value of this feature is that it helps engineers understand where a table's data came from, which upstream notebooks or jobs produced it, and which downstream assets, including dashboards, depend on it. Column-level lineage adds another layer of traceability by showing how specific fields are derived through transformations, which is especially useful for impact analysis, governance, and troubleshooting. Options A, B, and C each remove documented capabilities that Unity Catalog lineage actually supports. Therefore, the Databricks-documented answer is the full interactive graph across Delta tables and related assets, with column-level tracking included.
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NEW QUESTION # 305
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