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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Delta Lake Fundamentals | 20-25% | - Understand ACID transactions and time travel - Write to and read from Delta tables - Explain Delta Lake features and benefits - Create and manage Delta tables |
| Topic 2: 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 3: Apache Spark Data Processing Fundamentals | 20-25% | - Apply transformations and actions on DataFrames - Use Spark SQL for data processing - Create and use Spark DataFrames - Work with structured data types (arrays, maps, structs) |
| Topic 4: Spark SQL and DataFrames | 15-20% | - Handle null values and data quality - Write and execute Spark SQL queries - Join and union DataFrames - Aggregate and group data |
| Topic 5: Data Pipeline Architecture | 15-20% | - Design data pipelines for batch and streaming - Implement incremental data processing - Monitor and optimize pipeline performance - Understand ELT vs ETL patterns |
| Topic 6: Python for Data Engineering | 10-15% | - Implement user-defined functions (UDFs) - Work with Spark APIs in Python - Use PySpark for data processing |
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NEW QUESTION # 30
Which of the following describes the relationship between Gold tables and Silver tables?
Answer: E
Explanation:
According to the medallion lakehouse architecture, gold tables are the final layer of data that powers analytics, machine learning, and production applications. They are often highly refined and aggregated, containing data that has been transformed into knowledge, rather than just information. Silver tables, on the other hand, are the intermediate layer of data that represents a validated, enriched version of the raw data from the bronze layer. They provide an enterprise view of all its key business entities, concepts and transactions, but they may not have all the aggregations and calculations that are required for specific use cases. Therefore, gold tables are more likely to contain aggregations than silver tables. Reference:
What is the medallion lakehouse architecture?
What is a Medallion Architecture?
NEW QUESTION # 31
A data engineer has joined an existing project and they see the following query in the project repository:
CREATE STREAMING LIVE TABLE loyal_customers AS
SELECT customer_id -
FROM STREAM(LIVE.customers)
WHERE loyalty_level = 'high';
Which of the following describes why the STREAM function is included in the query?
Answer: B
Explanation:
Explanation
https://docs.databricks.com/en/sql/load-data-streaming-table.html
Load data into a streaming table
To create a streaming table from data in cloud object storage, paste the following into the query editor, and then click Run:
SQL
Copy to clipboardCopy
/* Load data from a volume */
CREATE OR REFRESH STREAMING TABLE <table-name> AS
SELECT * FROM STREAM read_files('/Volumes/<catalog>/<schema>/<volume>/<path>/<folder>')
/* Load data from an external location */
CREATE OR REFRESH STREAMING TABLE <table-name> AS
SELECT * FROM STREAM read_files('s3://<bucket>/<path>/<folder>')
NEW QUESTION # 32
What is the structure of an Asset Bundle?
Answer: D
Explanation:
An Asset Bundle in Databricks is structured as a YAML configuration file that defines the project's artifacts, resources, and configurations. This format allows reproducibility, automation, and portability of projects across workspaces, making it the standard approach for production pipelines.
NEW QUESTION # 33
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: A
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 # 34
A data engineer requires rapid iteration on pipelines while maintaining reliable rollbacks after bad ingests, ensuring audit trails for regulatory compliance, and providing consistent access to a single source of truth for both AI and BI workloads.
Which strategy should the data engineer apply to meet the needs?
Answer: A
Explanation:
Delta Lake provides ACID transactions and time travel for safe pipeline iteration and reliable rollbacks, while Unity Catalog ensures governed, auditable access with lineage and a single source of truth shared consistently across AI and BI workloads.
NEW QUESTION # 35
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