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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Pipeline Architecture | 15-20% | - Implement incremental data processing - Understand ELT vs ETL patterns - Design data pipelines for batch and streaming - Monitor and optimize pipeline performance |
| Topic 2: Lakehouse Platform Concepts | 10-15% | - Understand the Lakehouse architecture and its benefits - Explain data governance and security concepts - Describe key Databricks Lakehouse platform components |
| Topic 3: Apache Spark Data Processing Fundamentals | 20-25% | - Create and use Spark DataFrames - Use Spark SQL for data processing - Work with structured data types (arrays, maps, structs) - Apply transformations and actions on DataFrames |
| Topic 4: Delta Lake Fundamentals | 20-25% | - Write to and read from Delta tables - Create and manage Delta tables - Understand ACID transactions and time travel - Explain Delta Lake features and benefits |
| Topic 5: Python for Data Engineering | 10-15% | - Implement user-defined functions (UDFs) - Work with Spark APIs in Python - Use PySpark for data processing |
| Topic 6: 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 |
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NEW QUESTION # 101
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 # 102
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: A
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 # 103
A company is collaborating with a partner that does not use Databricks but needs access to a large historical dataset stored in Delta format. The data engineer needs to ensure that the partner can access the data securely, without the need for them to set up an account, and with read-only access.
How should the data be shared?
Answer: D
Explanation:
Delta Sharing is an open protocol developed by Databricks that enables secure, real-time sharing of data across organizations without requiring recipients to have a Databricks account. It is specifically designed for scenarios where external partners need read-only access to data stored in Delta format. With Delta Sharing, data providers create a share and grant access to recipients via a secure mechanism (such as a profile file or URL-based access), ensuring that the data cannot be modified by the consumer. This approach eliminates the need for data duplication or manual file transfers, maintaining data freshness and governance. Option B is incorrect because Unity Catalog governs internal access within an organization, not external sharing without accounts. Option C is inefficient and breaks data lineage and freshness. Option D violates security best practices by granting excessive permissions. Delta Sharing ensures fine- grained access control, auditability, and secure external collaboration , making it the correct solution for this requirement.
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NEW QUESTION # 104
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?
Answer: A
Explanation:
Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale1. Delta Lake supports upserts using the merge operation, which enables you to efficiently update existing data or insert new data into your Delta tables2. Delta Lake also provides time travel capabilities, which allow you to query previous versions of your data or roll back to a specific point in time3. Reference: 1: What is Delta Lake? | Databricks on AWS 2: Upsert into a table using merge | Databricks on AWS 3: [Query an older snapshot of a table (time travel) | Databricks on AWS] Learn more
1learn.microsoft.com2medium.com3slideshare.net4docs.databricks.com5github.com6key2consulting.com
NEW QUESTION # 105
Which of the following data workloads will utilize a Gold table as its source?
Answer: E
Explanation:
A Gold table is a table that contains highly refined and aggregated data that powers analytics, machine learning, and production applications. It represents data that has been transformed into knowledge, rather than just information. A Gold table is typically the final output of a medallion lakehouse architecture, where data flows from Bronze to Silver to Gold tables, with each layer improving the structure and quality of data. A job that queries aggregated data designed to feed into a dashboard is an example of a data workload that will utilize a Gold table as its source, as it requires data that is ready for consumption and analysis. The other options are either data workloads that will use a Bronze or Silver table as their source, or data workloads that will produce a Gold table as their output. References: Databricks Documentation - What is the medallion lakehouse architecture?, Databricks Documentation - What is a Medallion Architecture?, K21Academy - Delta Lake Architecture & Azure Databricks Workspace.
NEW QUESTION # 106
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