Databricks-DE-Assoc Databricks Intelligence Platform Practice Test 2 — 15 Questions

Question 1

Refer to the exhibit. A Databricks administrator is using the Unity Catalog JSON policy to manage access. If the 'data_scientist_1' user attempts to execute an 'UPDATE' command on the 'orders' table, what will be the result?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The command will fail due to insufficient privileges on the table.. The policy explicitly grants only 'SELECT' and 'DESCRIBE' permissions. In Unity Catalog, if an action is not explicitly permitted, it is denied by default. Therefore, the update attempt will be rejected. This is critical for data engineers to understand because Unity Catalog enforces a strict least-privilege security model, ensuring that only authorized users can modify data, which protects the integrity of tables in a shared environment.

Answer

The command will succeed because the user is part of the 'dev_analytics' schema.

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The command will succeed because 'SELECT' implies 'UPDATE' in Unity Catalog.

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The command will succeed if the cluster is running in Single User mode.

Question 2

Which THREE of the following are core components of the Databricks Intelligence Platform?

Accepted Answer

Delta Lake. The Databricks Intelligence Platform is built on a foundation that includes high-performance compute, unified governance, and open storage. These components work together to provide a seamless experience for data engineering, science, and analytics. Recognizing these elements helps engineers understand the platform's capabilities to scale horizontally, secure data assets centrally, and maintain interoperability through open standards, which are essential for modern data architecture requirements.

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Proprietary File System Format

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Hardware-level Storage Controllers

Question 3

A data engineer needs to automate the ingestion and transformation of data within Databricks with minimal manual intervention. Which feature is most appropriate for orchestrating these data workflows?

Accepted Answer

Databricks Workflows. Databricks Workflows is a fully managed orchestration service that allows users to create, schedule, and monitor data pipelines. It is essential because it integrates seamlessly with the platform, allowing engineers to chain together notebooks, SQL queries, and Delta Live Tables pipelines into complex, multi-step workflows. This automation reduces manual toil and improves the reliability of data production environments in the Lakehouse.

Answer

Delta Live Tables (DLT)

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Unity Catalog UI

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Databricks SQL Warehouse

Question 4

Which Databricks compute resource is specifically optimized for running BI dashboards and SQL queries?

Accepted Answer

SQL Warehouse. Databricks SQL Warehouses are compute environments designed to deliver high-performance SQL execution, making them ideal for BI tools and dashboarding. Understanding the difference between SQL Warehouses and traditional data engineering clusters is important, as it helps engineers choose the right compute resource to match the workload, ensuring better query performance and cost efficiency for end-users who need to interact with data via SQL.

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All-Purpose Cluster

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Job Cluster

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Delta Live Tables Pipeline

Question 5

Refer to the exhibit. A data engineer encounters this error while trying to list tables in a schema. Based on the error, what must the engineer do to resolve it?

Accepted Answer

Grant 'USE CATALOG' privilege on the catalog to the user.. The error indicates that the user lacks the 'USE CATALOG' privilege, which is the foundational permission required to even browse objects within a specific catalog in Unity Catalog. This is a critical concept because Unity Catalog enforces a hierarchical security model where access must be granted from the root down to the table level, ensuring that users cannot bypass organizational security boundaries by guessing table names or paths.

Answer

Grant 'SELECT' privilege on the table to the user.

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Increase the cluster's worker node count.

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Upgrade the workspace to the Premium tier.

Question 6

A data engineer is designing a pipeline on Databricks that requires ACID transactions and schema enforcement for streaming data. Which storage abstraction should they use to ensure data reliability and support time travel?

Accepted Answer

Delta Lake tables. Delta Lake provides the foundation for the Databricks Lakehouse, offering ACID transactions, schema enforcement, and versioning capabilities. By utilizing Delta tables, engineers can perform time travel via transaction logs to audit data or recover from accidental deletions. This storage layer is essential for maintaining high-quality data throughout the pipeline lifecycle, preventing partial writes and ensuring consistent reads for downstream analytics users in the Lakehouse environment.

Answer

Parquet files in S3 without a transaction log

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Hive-style partitioned CSV files

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A standard memory-mapped temporary table

Question 7

Which TWO of the following capabilities are native to the Databricks Unity Catalog?

Accepted Answer

Row and column-level security. Unity Catalog centralizes governance by providing a unified interface for managing permissions across workspaces. It enables fine-grained access control down to the row and column level and provides a centralized audit log. These capabilities are vital for organizations needing to manage data security at scale, ensuring that data engineers and scientists have consistent access control policies across the entire Lakehouse regardless of the specific compute resource or workspace utilized.

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Auto-scaling of compute clusters

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Automated ML model hyperparameter tuning

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Direct hardware driver installation

Question 8

Which THREE of the following are primary components of the Databricks Lakehouse architecture?

Accepted Answer

Delta Lake. The Databricks Lakehouse architecture combines the best of data warehouses and data lakes by using Delta Lake for reliable storage, Spark for performant compute, and Unity Catalog for centralized governance. These components work together to provide a unified platform that supports diverse workloads, including batch processing, streaming, data science, and business intelligence, ensuring that data is governed, performant, and reliable for all organizational stakeholders.

Answer

Direct Hardware Access

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Proprietary File System Format

Question 9

When a data engineer needs to automate a recurring ETL job, which Databricks tool is most appropriate for orchestrating tasks and handling dependencies?

Accepted Answer

Databricks Workflows. Databricks Workflows allows engineers to create, run, and monitor multi-task jobs. It supports dependencies, retries, and notifications, making it the standard choice for orchestrating complex data pipelines. By using Workflows, engineers can ensure that data transformation tasks occur in the correct sequence, with robust error handling and monitoring, which is critical for maintaining reliable production data pipelines in the Databricks Intelligence Platform.

Answer

Databricks Notebooks

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Delta Live Tables

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MLflow Models

Question 10

Which capability is provided by Databricks' integration with MLflow?

Accepted Answer

Experiment tracking and model lifecycle management. MLflow is an open-source platform that integrates seamlessly with Databricks to manage the machine learning lifecycle. It provides tools for experiment tracking, model registry, and deployment. This integration is vital for data scientists, as it allows them to log experiments, compare results, and manage model versions, ensuring that machine learning projects are reproducible and ready for production deployment within the collaborative workspace.

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Automatic creation of SQL database schemas

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Low-level kernel manipulation

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Automatic hardware upgrades for clusters

Question 11

Which command is used to query the history of a Delta table to perform time travel?

Accepted Answer

DESCRIBE HISTORY table_name. The 'DESCRIBE HISTORY' command provides a detailed log of all transactions performed on a Delta table, including operations like inserts, updates, and deletes. This is essential for auditing and reverting to previous states if data corruption or accidental deletions occur. By understanding the history, engineers can verify the lineage and integrity of their data, which is a core benefit of the Delta Lake storage layer.

Answer

SELECT * FROM TABLE_HISTORY

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SHOW LOGS FROM table_name

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GET_DELTA_VERSIONS(table_name)

Question 12

Which TWO of the following are primary benefits of using Delta Live Tables (DLT) for data pipeline development?

Accepted Answer

Declarative pipeline development. Delta Live Tables simplifies pipeline development by providing declarative syntax and automated infrastructure management. It handles complex tasks like dependency resolution and data quality enforcement automatically. By reducing the manual effort required to manage pipeline state and quality, DLT enables engineers to focus on business logic, ensuring that data pipelines are more robust, maintainable, and reliable across the enterprise, which is crucial for large-scale data engineering teams.

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Manual management of cluster nodes

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Exclusive support for Python-only pipelines

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Requires a dedicated standalone database

Question 13

Refer to the exhibit. A security administrator applies this Unity Catalog policy. What is the impact on users in the 'analyst-group'?

Accepted Answer

They can view the contents of the revenue table.. This Unity Catalog JSON policy grants the 'analyst-group' the SELECT privilege on the 'main.sales.revenue' table. Consequently, any user belonging to this group will be able to read data from this specific table. This is a standard governance practice in Databricks, providing a fine-grained, centralized method to control access to sensitive data assets across the entire organization, ensuring security and compliance without managing individual permissions for each user.

Answer

They can drop the revenue table.

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They gain administrative rights over the entire catalog.

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They can write new data into the revenue table.

Question 14

Which Databricks feature should be used to securely share data with external organizations without duplicating the data?

Accepted Answer

Delta Sharing. Delta Sharing is a secure, open-protocol solution that allows organizations to share data with third parties without moving or copying the underlying files. By using Delta Sharing, the provider maintains a single source of truth while the consumer gains direct access to the data. This minimizes storage costs, ensures data consistency, and simplifies compliance management for multi-party data sharing scenarios.

Answer

Unity Catalog

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Databricks Workflows

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MLflow Model Registry

Question 15

A data engineer is designing a pipeline on Databricks to process streaming data. Which architectural component acts as the unified storage layer, allowing both batch and streaming workloads to access the same underlying data files in a data lake?

Accepted Answer

Delta Lake. The Databricks Intelligence Platform leverages the Lakehouse architecture, which combines the best elements of data lakes and data warehouses. Delta Lake is the critical component that sits on top of object storage, providing ACID transactions, schema enforcement, and time travel. By using Delta Lake, the platform ensures that data consistency is maintained across concurrent streaming and batch processes, which is fundamental for reliable data engineering at scale.

Answer

Unity Catalog

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Databricks SQL

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Photon Engine