Databricks-DA-Assoc Multiple Select — 40 Questions

Question 1

An analyst is preparing a Genie Space and wants to ensure users get the most accurate answers while maintaining security. Which TWO actions should the analyst take? (Choose two)

Accepted Answer

Exclude highly sensitive columns that are not needed for general analysis.. Proper curation of the Genie Space is vital for performance and security. By including only the necessary columns and providing explicit instructions, the analyst reduces noise and ensures the AI model operates within the boundaries of the organization's data governance framework. These steps directly impact the quality of the SQL generated and the safety of the information exposed to end-users via the AI interface.

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Enable 'auto-query' for all users to maximize data exploration.

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Grant all users the 'Owner' role on the Genie Space to allow for maximum customization.

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Remove all constraints and indexes to allow the AI to move faster.

Question 2

A Databricks workspace administrator wants to optimize costs and manage resources effectively. Which TWO of the following capabilities allow for automatic cluster termination and scaling?

Accepted Answer

Auto-termination. Databricks provides built-in mechanisms to manage compute costs through Auto-termination and Auto-scaling. Auto-termination automatically shuts down clusters after a period of inactivity, preventing wasteful billing. Auto-scaling allows the cluster to resize itself based on the current workload requirements, adding or removing workers dynamically. These features are fundamental for maintaining a cost-efficient data platform while ensuring sufficient performance for varying data volumes during analysis.

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Cluster Tagging

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Delta Cache

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Instance Pools

Question 3

Which TWO of the following are benefits of using Unity Catalog with Databricks SQL?

Accepted Answer

Centralized access control for all data assets.. Unity Catalog provides a unified governance layer for data and AI assets across the Databricks workspace. It enables fine-grained access control, centralized auditing, and simplified data discovery. By implementing Unity Catalog, organizations can ensure compliance and security while allowing analysts to collaborate effectively. This is a critical component of modern data platforms, providing the necessary visibility and control required by data teams to manage data assets securely at scale.

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Automatic translation of SQL to Python code.

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Increased query execution speed for simple SELECTs.

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Ability to bypass authentication for local files.

Question 4

A data analyst is using the Databricks Add Data UI to import a CSV file from cloud storage into a Delta table. The analyst wants to ensure the import process handles the data correctly. Which two actions can the analyst perform directly in the Add Data UI? (Choose two.)

Accepted Answer

Specify a custom delimiter for the CSV file.. The Add Data UI allows analysts to preview data, adjust column types, and specify a custom delimiter during import. It does not support scheduling, DDL schema definition, or schema evolution, which are features of other tools like Auto Loader or Jobs.

Answer

Set up a scheduled job to automatically ingest new files from the same location.

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Enable schema evolution for future files.

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Define a custom schema using DDL statements.

Question 5

Which THREE actions occur when using Auto Loader with schema evolution enabled? (Choose three)

Accepted Answer

New columns found in the source files are automatically added to the target table.. Schema evolution is a powerful feature of Auto Loader that allows pipelines to adapt to changes in upstream data without requiring manual code updates. By understanding how the schema is managed, detected, and reconciled, analysts can build flexible ingestion systems that reduce maintenance overhead. This is critical in modern data environments where upstream sources frequently add or rename columns, requiring the platform to dynamically update downstream tables to accommodate the new structure.

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Existing columns with data type conflicts cause the pipeline to stop immediately.

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The schema is inferred from the metadata of the cloud storage provider.

Question 6

An analyst is reviewing a slow-running query and wants to identify potential bottlenecks. Which TWO of the following metrics in the Databricks SQL query profile are most indicative of inefficient data distribution?

Accepted Answer

Shuffle Read/Write bytes are significantly higher than the input data size.. Data skew and excessive shuffling are primary causes of performance degradation. High 'Shuffle Read' and 'Shuffle Write' values indicate large amounts of data moving across the network, while skewed task durations signal that one executor is doing significantly more work than others. Identifying these metrics early allows analysts to apply techniques like salting keys or partitioning to balance the workload across the cluster.

Answer

The 'Scan' node shows high throughput and low execution time.

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The query shows a high number of file metadata operations.

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The query plan indicates a broadcast hash join is being used.

Question 7

Which TWO of the following are true regarding Databricks SQL Warehouses?

Accepted Answer

They are specifically optimized for SQL-based analytical queries.. SQL Warehouses are specialized compute resources optimized for SQL workloads. Understanding the difference between these and general-purpose clusters is crucial for analysts, as warehouses offer features like serverless startup and auto-stop, which significantly optimize cost and performance. These warehouses enable the platform's BI and dashboarding capabilities, making them a cornerstone of the data analyst's toolkit for delivering consistent, reliable reports to business stakeholders.

Answer

They must be manually started every time a user runs a query.

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They support the same multi-language notebook features as all-purpose clusters.

Answer

They are only available for administrative users.

Question 8

A user wants to import a local 50MB CSV file into a Databricks workspace for a quick ad-hoc analysis. Which TWO methods are available within the Databricks UI to accomplish this task directly?

Accepted Answer

Using the 'Create or modify table from file upload' feature in the 'Add' menu.. Databricks provides several entry points for local file uploads to facilitate rapid analysis. The 'Create or modify table from file upload' UI is the primary method for non-programmatic ingestion, while Unity Catalog Volumes offer a modern way to manage non-tabular data. Both options allow analysts to bypass complex cloud storage configurations when dealing with small, local datasets for immediate exploratory data science tasks.

Answer

Using the Databricks File System (DBFS) CLI to push the file to the workspace.

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Using the SQL Editor to copy and paste the raw text into a new table.

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Using the Workspace Sidebar 'Import' button to select the local CSV file.

Question 9

An analyst needs to manage data lifecycle and performance in Databricks SQL. Which TWO of the following tasks are best achieved using the Liquid Clustering feature?

Accepted Answer

Optimize data skipping for frequently filtered columns.. Liquid Clustering is a flexible way to manage data layout in Delta tables, replacing static partitioning and Z-Ordering. It automatically adapts to data distribution changes over time without manual intervention. Choosing the correct clustering columns ensures that queries filter data efficiently while minimizing the storage overhead associated with maintaining high-cardinality partitions, which often lead to small-file problems in large datasets.

Answer

Enforce strict schema validation during ingestion.

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Implement row-level security policies.

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Manage concurrent write conflicts in Delta tables.

Question 10

Which THREE of the following are benefits of using Liquid Clustering instead of traditional partitioning in Databricks SQL?

Accepted Answer

It simplifies data layout management by removing the need for manual partitioning.. Liquid Clustering provides a flexible alternative to manual partitioning by automatically managing data layout based on query patterns. It eliminates the need for manual 'partition evolution' and solves issues related to skewed data distribution or small file problems. By offloading cluster management to the Databricks engine, analysts can spend more time on business logic and less on the intricacies of physical data file management for large-scale datasets.

Answer

It provides faster write throughput by disabling transaction logs during updates.

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It forces data to be sorted by every column in the table automatically.

Question 11

A data platform administrator needs to configure secure data access policies in Unity Catalog. Which TWO actions can the administrator perform to restrict access to sensitive columns within a Delta table? (Choose TWO)

Accepted Answer

Grant SELECT privilege on the specific sensitive columns to authorized users while withholding it on other columns in the table.. Unity Catalog supports fine-grained governance through column-level access controls and dynamic masking functions. Administrators can restrict column visibility directly by revoking SELECT privileges on specific columns or by applying SQL-based masking functions that evaluate user attributes at query time. These native capabilities ensure sensitive information remains protected across all notebooks, dashboards, and SQL queries without duplicating physical data assets.

Answer

Modify the cloud storage bucket permissions in AWS S3 or Azure Blob Storage to block read access to individual table files containing sensitive columns.

Answer

Enable row-level security filters on the table to automatically exclude rows containing sensitive attribute values from query results.

Answer

Create a static clone of the table using the CLONE command and delete the sensitive columns from the cloned copy for general users.

Question 12

Which TWO of the following are true about Databricks SQL Alerts? (Choose two)

Accepted Answer

Alerts can trigger external workflows using webhooks.. Alerts in Databricks SQL are a critical component of a proactive monitoring strategy. By tracking the results of a query, they provide timely notifications that help analysts and business users respond to events immediately. Understanding the configuration of thresholds and notification channels is essential for building a reliable operational dashboard suite that moves beyond passive data reporting to active, event-driven data management.

Answer

Alerts only function if the dashboard is currently open.

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Alerts automatically delete data that triggers the condition.

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Alerts are only compatible with Python-based notebooks.

Question 13

Which THREE of the following are benefits of using Delta Lake over standard Parquet files in Databricks?

Accepted Answer

ACID transaction support. Delta Lake introduces ACID transactions, time travel, and schema enforcement to the data lake, transforming it into a lakehouse. These features are critical for maintaining data integrity in complex production environments. Understanding these benefits allows analysts to make informed decisions about storage formats, ensuring that the data platform remains reliable, scalable, and capable of handling complex analytical requirements without risking data corruption or inconsistency.

Answer

Automatic data compression to non-standard formats

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The ability to run queries without a compute engine

Question 14

A data analyst is working in a Databricks workspace and needs to ensure that their notebook code is version-controlled and collaborative. Which TWO actions should they take?

Accepted Answer

Use the built-in Databricks Repos feature to connect to a Git provider.. Integrating version control and collaboration is essential for professional data engineering workflows. By linking notebooks to Git providers, analysts ensure that changes are tracked, auditable, and easily revertible. This standard practice prevents accidental data loss and fosters teamwork, allowing multiple contributors to manage code repositories effectively within the Databricks environment while maintaining a single source of truth for all analytical project documentation and complex transformation logic.

Answer

Copy and paste code into a local text file.

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Create a new notebook file for every code change.

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Download the notebook to their local machine every hour.

Question 15

Which THREE of the following are essential components of a well-architected AI/BI Genie Space setup?

Accepted Answer

Detailed documentation in the Unity Catalog table/column comments.. A robust Genie Space setup relies on clear semantic metadata, optimized data access, and controlled user permissions. By defining clear table descriptions in Unity Catalog, you provide the LLM with the context needed for accurate SQL generation. Combining this with granular permissions and well-crafted system instructions ensures the solution is both secure and reliable. These three pillars create a scalable environment that allows users to self-serve insights while maintaining strict organizational data governance.

Answer

Providing the end-users with direct read access to all raw source tables.

Answer

Granting all users 'owner' privileges on the Genie Space.

Question 16

Which THREE features are provided by AI/BI Genie spaces to help business users analyze data independently?

Accepted Answer

Conversational natural language querying. Genie spaces empower business users by abstracting complex SQL development. They provide a natural language interface for asking questions, automatic visualization of results for immediate insight, and a feedback loop where users can rate answers to refine the model's future performance. These features collectively reduce the burden on data teams while fostering a self-service culture where users can safely interact with governed data assets.

Answer

One-click deployment of production ETL pipelines

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Automatic generation of unit tests for Python code

Question 17

Which THREE of the following are valid sources for importing data into a Databricks Delta table? (Choose three)

Accepted Answer

S3, ADLS, and GCS cloud object storage.. Databricks provides flexible connectivity to ingest data from diverse sources into the Lakehouse. Whether the data is in cloud storage, a relational database, or a streaming message broker, the platform offers optimized connectors to load this information into Delta format. Mastering these integration points allows analysts to architect robust data pipelines that can pull information from virtually any enterprise system for unified analysis and reporting within a single environment.

Answer

Local files stored on the Databricks Driver node.

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Direct memory buffers from the user's browser.

Question 18

Which THREE of the following are benefits of using the Delta Lake format in Databricks SQL?

Accepted Answer

ACID transactions for data integrity. Delta Lake provides the foundation for reliable, performant SQL querying in Databricks. Key benefits include ACID transactions, which prevent data corruption during concurrent operations; time travel, which allows analysts to query previous versions of data; and data skipping/compaction, which drastically improve query performance. Together, these features enable reliable data warehousing and complex analytics at scale, which are the main reasons why it is the standard format in Databricks SQL environments.

Answer

The ability to run queries without a cluster

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Automatic conversion of all queries to Python

Question 19

An analyst is preparing a dashboard and needs to ensure that the queries powering it are as performant as possible. Which THREE techniques should the analyst use to optimize these SQL queries in Databricks?

Accepted Answer

Regularly run OPTIMIZE on the underlying tables. Optimizing dashboards requires a combination of efficient data layout, metadata management, and query-level tuning. Using the 'OPTIMIZE' command keeps files consolidated, while 'ANALYZE' provides the CBO with the statistics it needs for optimal plan selection. Finally, using 'Z-Ordering' on high-cardinality columns frequently appearing in filters or joins ensures the query engine can skip large volumes of irrelevant data, directly leading to faster dashboard response times.

Answer

Always use SELECT * to fetch all available columns

Answer

Convert all Delta tables to CSV format for speed

Question 20

Which THREE of the following are valid methods to optimize the performance of a slow-running SQL query in Databricks?

Accepted Answer

Using Z-Ordering on high-cardinality columns.. Performance optimization in Databricks involves a combination of data organization, compute resource management, and query plan refinement. By using techniques like Z-Ordering, partitioning, and leveraging the cost-based optimizer, analysts can significantly reduce query latency. These methods are essential for managing large-scale datasets, ensuring that compute resources are utilized effectively, and providing timely insights to business stakeholders who rely on the performance of analytical dashboards and reports.

Answer

Manually rewriting every query to use nested subqueries.

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Increasing the query timeout setting to 24 hours.