Databricks-DA-Assoc Practice Test 8 — 10 Questions

Question 1

A business user asks a Genie Space, 'Show me the revenue by product.' The model returns an error. What is the most common reason for this failure in a properly configured environment?

Accepted Answer

The model cannot distinguish between multiple columns that could mean 'revenue'.. The most common failure in Genie occurs when the model cannot uniquely identify the requested columns or the business logic for 'revenue' is ambiguous. If multiple columns could represent revenue or if the product identifier is unclear, the model will struggle. Clarifying the schema via table comments and standardizing the definition of key metrics in the data layer is the standard fix for this ambiguity issue.

Answer

The user does not have access to the underlying SQL Warehouse.

Answer

The Genie Space is currently set to 'read-only' mode.

Answer

The user is using the wrong language for the query.

Question 2

When sharing data with external organizations using Delta Sharing, which component manages the secure exchange of data?

Accepted Answer

Unity Catalog metastore.. Delta Sharing is an open protocol that enables the secure exchange of data across different organizations without requiring them to have accounts in the same Databricks workspace. The Unity Catalog metastore acts as the central control point for these shares. By creating a 'Share' object, an administrator defines exactly which tables are shared, and the recipient uses a token to access that data securely.

Answer

Workspace-local Hive metastore.

Answer

Databricks File System (DBFS) Root.

Answer

User-managed S3 bucket policies.

Question 3

An analyst needs to query a table that has many small files resulting from frequent streaming ingestions. Which command is most appropriate to consolidate these files into larger, more efficient files for future analytical queries?

Accepted Answer

OPTIMIZE table_name. The OPTIMIZE command is the standard way to compact small files in Delta tables. It merges multiple small files into larger files, which reduces metadata overhead and improves read performance. By combining this with Z-Ordering, the data layout is optimized for common query patterns, ensuring that the SQL engine reads only the necessary data blocks during execution, which is vital for efficient data warehousing.

Answer

VACUUM table_name

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REFRESH TABLE table_name

Answer

ALTER TABLE table_name REORGANIZE

Question 4

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.

Answer

Create a new notebook file for every code change.

Answer

Download the notebook to their local machine every hour.

Question 5

What is the primary function of a Storage Credential in Unity Catalog?

Accepted Answer

To provide a secure way to access cloud storage locations.. A Storage Credential is an object in Unity Catalog that encapsulates long-term cloud provider credentials (like an AWS IAM role or Azure Service Principal). It allows Databricks to access data in cloud storage without requiring users to configure individual cloud credentials on every cluster. This centralizes security by keeping sensitive cloud keys away from end users and developers.

Answer

To encrypt files stored in the S3 bucket.

Answer

To manage user passwords for the Databricks workspace.

Answer

To define table schema definitions for external tables.

Question 6

A data analyst is querying a large Delta table containing billions of rows of clickstream data. The table is frequently queried using a timestamp column. To optimize range queries on this timestamp, which Delta Lake feature should the table builder implement?

Accepted Answer

Applying Z-Order clustering on the timestamp column to improve multi-dimensional data skipping.. Delta Lake features like Z-Ordering colocate related information in the same set of files based on specified columns. However, for efficient range filtering on high-cardinality timestamps, Liquid Clustering or traditional Z-Ordering combined with proper partitioning or data skipping statistics ensures the engine reads minimal files.

Answer

Enabling column mapping to allow renaming columns without rewriting underlying parquet files.

Answer

Setting the file size compaction threshold to the maximum allowable limit of 1 gigabyte.

Answer

Adding an explicit check constraint on the timestamp column to reject future dates.

Question 7

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 8

An analytics team is building an AI/BI Genie space to allow business users to query sales data using natural language. After setting up the base tables, the initial user questions return inaccurate filter values because the LLM struggles to map colloquial region names to the exact string codes stored in the database. What is the most effective feature within AI/BI Genie to resolve this mapping issue without modifying the underlying physical tables?

Accepted Answer

Configure table and column descriptions, add explicit instructions, and provide verified queries demonstrating the correct region mappings.. Adding instructions, descriptions, and verified queries to the Genie space gives the underlying model precise semantic context about colloquial terms, business definitions, and expected filters. This targeted guidance steers natural language translation toward the correct database columns and values without requiring costly data transformations or restructuring of source tables in the data lakehouse.

Answer

Create a materialized view containing hardcoded string mappings for every possible regional variation queried by business users.

Answer

Enable automatic schema inference and let the Genie space rebuild its internal vector embeddings from scratch overnight.

Answer

Modify the source table column constraints to reject any query that does not use the exact database region code.

Question 9

An analyst notices that the Genie space is consistently ignoring a specific column during query generation. What is the most effective way to force the model to consider this column?

Accepted Answer

Add a column comment in Unity Catalog and mention it in instructions. LLMs can sometimes overlook columns if they aren't described clearly in the metadata or instructions. Providing explicit, high-quality descriptions in the table metadata (within Unity Catalog) or adding a specific note in the Genie space instructions ensures the model recognizes the column's semantic value and usage context, forcing it to include the information in its reasoning process during user interactions.

Answer

Rename the column to start with 'Required_'

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Hard-code the column in the SQL Warehouse settings

Answer

Delete and recreate the Genie space

Question 10

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

Answer

Automatic generation of unit tests for Python code