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| Section | Objectives |
|---|---|
| Data Governance and Quality | - Unity Catalog basics - Data access control and governance - Data quality concepts and management |
| Productionizing Data Pipelines | - Pipeline deployment and operationalization - Scheduling and monitoring jobs - Databricks Workflows / Jobs orchestration |
| Databricks Lakehouse Platform Fundamentals | - Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts |
| Data Processing and Transformations | - User-defined functions (UDFs) - PySpark DataFrame transformations - Delta Lake fundamentals (tables, transactions, optimization) - Apache Spark SQL operations (joins, aggregations, filtering) |
| Data Ingestion and ELT Development | - Data ingestion using Spark SQL and PySpark - Handling structured and semi-structured data - ETL patterns and transformations |
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NEW QUESTION # 71
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?
Answer: E
Explanation:
Option D is the correct answer because it enables the Serverless feature for the SQL endpoint, which allows the endpoint to automatically scale up and down based on the query load. This way, the endpoint can handle more concurrent queries and reduce the time it takes to return results. The Serverless feature also reduces the cold start time of the endpoint, which is the time it takes to start the cluster when a query is submitted to a non-running endpoint. The Serverless feature is available for both AWS and Azure Databricks platforms.
References: Databricks SQL Serverless, Serverless SQL endpoints, New Performance Improvements in Databricks SQL
NEW QUESTION # 72
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: D
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 # 73
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?
Answer: C
Explanation:
Data Explorer is a graphical interface that allows you to browse, create, and manage data objects such as databases, tables, and views in your workspace. You can also review and modify the permissions on these data objects using Data Explorer. To access Data Explorer, you can click on the Data icon in the sidebar, or use the %sql magic command in a notebook. You can then select a database and a table, and click on the Permissions tab to view and edit the access control lists (ACLs) for the table. You can also use SQL commands such as SHOW GRANT and GRANT to query and modify the permissions on a Delta table. References:
* Data Explorer
* Access control for Delta tables
* SHOW GRANT
* [GRANT]
NEW QUESTION # 74
Which Databricks SQL predicate correctly performs a null-safe equality comparison so that rows are matched when both sides are NULL or when both are equal non-NULL values?
Answer: C
NEW QUESTION # 75
A data engineer is onboarding a new Bronze ingestion pipeline in Databricks with Unity Catalog. The team wants Databricks to handle storage layout, apply platform optimizations over time, and simplify lifecycle management so that when a table is dropped, its underlying data is also cleaned up according to Databricks- managed retention policies.
Which table type should the data engineer create for these ingestion tables?
Answer: C
Explanation:
Databricks recommends managed tables when you want the platform to control both the table metadata and the underlying storage lifecycle. For Unity Catalog managed tables, Databricks manages storage layout and applies platform-managed optimization features over time. Databricks also documents that when you drop a managed table , the underlying cloud data is deleted automatically after the managed retention period, which simplifies cleanup and reduces storage administration. That behavior matches the requirement exactly.
External tables, by contrast, are used when you want to retain direct control over the storage path and lifecycle. Databricks explicitly states that dropping an external table removes only the metadata and does not delete the underlying files . Foreign tables are for federation scenarios against external systems, not for Bronze ingestion storage managed by Databricks. Temporary views also do not provide the desired governed lifecycle. Because the team wants Databricks-managed storage, managed retention, and simplified lifecycle handling, managed tables are the correct choice for these Bronze ingestion tables.
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NEW QUESTION # 76
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