Databricks-GenAI-Assoc Practice Test 25 — 15 Questions

Question 1

A company wants to share a GenAI application's prompt templates and evaluation datasets with a partner organization. The partner uses its own Databricks account and must not access any other company data. The company also wants to revoke access easily. Which Unity Catalog feature should they use?

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

Accepted Answer

Delta Sharing with a share containing only the prompt templates and evaluation datasets.. Delta Sharing is designed for secure cross-organization data sharing without copying. By creating a share with only the prompt templates and evaluation datasets, the company limits exposure to those assets. The partner accesses the share from their own Databricks account, and the company can revoke access by removing the recipient, satisfying both isolation and revocation needs.

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Export the prompt templates and evaluation datasets to cloud storage and share a bucket URL.

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Create a new metastore for the partner and attach the company's workspace to it.

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Grant the partner's users direct SELECT privileges on the company's Unity Catalog tables.

Question 2

A data engineer wants to share a specific subset of sensitive PII data with an external department using Unity Catalog. The engineering team must ensure the data is anonymized dynamically based on the user's role without creating physical copies. Which feature is most appropriate?

Accepted Answer

Apply a masking function to the column and use row filters in Unity Catalog.. Unity Catalog row-level security and column-level masking allow for dynamic data governance. By applying a masking function using 'current_user_role()' or 'is_account_group_member()', administrators can ensure that users see redacted data without duplicating the underlying storage. This approach centralizes governance policies, simplifies auditing, and ensures that sensitive data exposure is strictly controlled at the query execution time across all compute resources within the Unity Catalog metastore environment.

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Create a temporary view that filters rows using a WHERE clause and grants SELECT access.

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Export the data to a new table with masked columns and grant access to the department.

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Configure access control lists (ACLs) on the underlying S3 bucket or ADLS container.

Question 3

A GenAI engineer prepares a Delta table of product descriptions that will feed a chunking and embedding pipeline. The descriptions are written in mixed languages, and the embedding model supports only English. The engineer needs to ensure that non-English rows are detected and routed for translation before embedding. Which approach is most appropriate?

Accepted Answer

Add a language detection UDF that runs ai_classify or a language-identification library on each description, write the detected language to a column, and split the table into English and non-English subsets.. When the embedding model is English-only, the pipeline must identify which rows need translation. Detecting language per row and storing the result as a column gives a deterministic routing signal, so English rows embed directly while non-English rows are sent to translation. This keeps the preparation step auditable and avoids both data loss and unnecessary translation cost.

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Translate every description into English before chunking, regardless of its original language, to guarantee uniform embeddings.

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Drop all rows where the description contains non-ASCII characters, since those rows are likely non-English.

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Configure the embedding model endpoint to accept a language parameter and pass the detected locale from Unity Catalog tags on the table.

Question 4

A team is deploying a RAG application using Databricks Model Serving with a foundation model endpoint and a Databricks Vector Search index. During load testing, they observe that p95 latency spikes when the retriever returns many chunks, and the LLM occasionally truncates context. They want to reduce latency while preserving answer quality. Which change is most appropriate?

Accepted Answer

Apply a reranker to the retrieved chunks and pass only the top few highest-scoring chunks to the LLM.. Reranking retrieved chunks and passing only the top few to the LLM reduces prompt length, which lowers LLM latency and mitigates context truncation. Because the reranker prioritizes the most query-relevant chunks, answer quality is preserved even with fewer chunks. The other options either increase prompt size, change an unrelated parameter, or alter the index without addressing prompt length.

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Increase the number of retrieved chunks and rely on the LLM to ignore irrelevant content.

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Switch the Vector Search index to a smaller embedding dimension and keep the same number of chunks.

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Lower the LLM temperature to zero to reduce response time.

Question 5

Which Databricks feature is primary for managing the lifecycle, versioning, and deployment readiness of custom Generative AI models?

Accepted Answer

MLflow Model Registry. MLflow Model Registry is the central feature for managing the lifecycle of models, including versioning and stage transitions. Understanding how to promote a model from 'Staging' to 'Production' is fundamental for ensuring that only tested and verified models reach the end-users. This workflow is critical for maintaining quality and stability in generative AI applications, as it provides a structured process for model evolution and deployment management.

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Unity Catalog Volumes

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Databricks SQL Warehouse

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Delta Live Tables

Question 6

A developer is using Databricks AI Functions to extract structured information from a large set of customer reviews stored in a Delta table. They want to apply a prompt to each review and store the results in a new column. Which function should they use?

Accepted Answer

ai_query(). Databricks AI Functions provide SQL-native access to generative AI models. ai_query() is the general-purpose function that accepts a prompt and a model endpoint, making it ideal for custom extraction tasks. It can be used in a SELECT statement to process each row and write the output to a new column. Other functions like ai_summarize and ai_classify are specialized and do not offer the same flexibility for structured extraction.

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ai_summarize()

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ai_extract()

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ai_classify()

Question 7

A team's RAG evaluation shows high context recall but low answer correctness. Retrieved documents contain the needed facts, yet the generated answers frequently contradict them. Which single metric should they examine next to pinpoint whether the generator is ignoring or misusing the retrieved context?

Accepted Answer

Groundedness, to check whether the claims in the generated answer are actually supported by the retrieved context.. With context recall high, retrieval supplied the necessary facts, so the failure lies downstream in generation. Groundedness directly measures whether the answer's claims are supported by the retrieved context, so a low groundedness score localizes the problem to the generator ignoring or misreading context. That distinguishes a generation-faithfulness issue from a retrieval-ranking or labeling issue.

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Retrieval latency, to determine whether slow document fetches cause the generator to fall back on parametric memory.

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Token count of the retrieved chunks, to verify whether the context window is overflowing and truncating documents.

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Context precision, to check whether the most relevant chunks are ranked at the top of the retrieved list.

Question 8

Which Unity Catalog object should be used to manage access to a folder of JSON files that are not part of a formal Delta table?

Accepted Answer

Unity Catalog Volume. Volumes in Unity Catalog are designed specifically for managing non-tabular data, such as JSON files, CSVs, or machine learning models. By creating a volume, you can govern access to these files using the same security model as tables. This allows for consistent data governance across all types of assets in your environment, not just those formatted as Delta tables.

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Managed Table

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External Location

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Global View

Question 9

A team is deploying a GenAI agent built with Mosaic AI Agent Framework. The agent must be exposed as a REST API that automatically scales with traffic and records inference logs to Unity Catalog. Which deployment mechanism meets these requirements with the least operational overhead?

Accepted Answer

Deploy the agent to a Databricks Model Serving endpoint using the agents.deploy() API.. The agents.deploy() API is the native Mosaic AI Agent Framework mechanism for taking an agent from development to a production Model Serving endpoint. It handles endpoint creation, scaling, and integrates with Unity Catalog inference tables for logging, so it directly satisfies all stated requirements with minimal manual work.

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Deploy the agent on a single-node cluster with a public IP and expose it via a Flask server.

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Package the agent as a Python wheel and run it on a Databricks job cluster with a scheduled trigger.

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Register the agent as a Unity Catalog model and call it directly from a notebook using mlflow.pyfunc.load_model().

Question 10

Which of the following describes the 'drift' phenomenon in the context of LLM monitoring?

Accepted Answer

A shift in user query patterns over time.. LLM drift occurs when the input data distribution or the nature of user queries changes over time, causing the model to perform worse than during validation. Because user expectations and language usage evolve, a model that performed well at launch may become less accurate as queries drift into new domains or change in tone. Monitoring this drift is essential for proactive maintenance and model updating.

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The model's weights change during inference.

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The model generating the same answer too often.

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Increased latency in the serving endpoint.

Question 11

A GenAI engineer is instrumenting a production RAG application on Databricks to detect quality degradation before users complain. The team wants signals that reveal problems in the retrieval stage specifically, rather than in the generation stage. Which TWO signals should they track? (Choose two.)

Accepted Answer

Recall of the retrieval step measured against a curated question-to-document mapping. Retrieval-stage quality is measured by whether the right documents are found and how many of the returned ones are relevant. Precision at k and recall against a curated question-to-document mapping capture those two dimensions directly, so regressions localize to indexing, chunking, or embeddings. Token count, time to first token, and toxicity all describe the generator or the serving layer, not retrieval.

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Toxicity score of the final generated answer

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Average completion token count per response

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Time to first token on the serving endpoint

Question 12

A machine learning engineer needs to deploy a real-time Model Serving endpoint. Which Databricks construct is required to manage the model's environment, dependencies, and artifacts while ensuring version control for the deployment?

Accepted Answer

MLflow Models within Unity Catalog. Databricks Model Serving relies on the Unity Catalog Model Registry to manage model versions and artifacts. By using Unity Catalog, organizations ensure governance, lineage, and consistent environment reproduction. This is critical for production deployments as it decouples the model development lifecycle from the serving infrastructure, ensuring that the exact artifact deployed in staging is the one running in production, thereby maintaining model integrity and reproducibility across environments.

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Delta Live Tables pipelines

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Databricks Job clusters

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Workspace-local Model Registry

Question 13

Why should you integrate Mosaic AI Model Evaluation with Unity Catalog?

Accepted Answer

To provide centralized governance and lineage.. Unity Catalog acts as the central governance layer for all Databricks data. By integrating evaluation with Unity Catalog, you ensure that all evaluation datasets, results, and models are discoverable, lineage-tracked, and governed. This is essential for compliance, ensuring that every model deployment is backed by a verifiable audit trail of its evaluation performance, which is a core requirement for enterprise AI deployments that must adhere to strict internal and external standards.

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To increase the model's training speed.

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To automatically delete old evaluation data.

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To bypass the need for prompt engineering.

Question 14

When migrating a Databricks Asset Bundle (DAB) project from 'development' to 'production', which TWO actions should an engineer perform?

Accepted Answer

Update the target in the databricks.yml file to point to the production host. Moving code to production requires updating the target configuration to point to production-grade environments and ensuring that environment variables or secrets are correctly scoped. These actions ensure that the production deployment uses hardened settings—such as different cluster types or access permissions—while maintaining the same logic defined in the bundle. This discipline prevents 'environment drift' and ensures that production workloads remain isolated, secure, and performant according to enterprise standards.

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Manually recreate all jobs using the Databricks UI in production

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Delete all development artifacts from the local machine

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Convert the bundle project into a standard notebook folder

Question 15

What is the purpose of the 'metastores.list' command in the Unity Catalog CLI?

Accepted Answer

To list all metastores available to the user in the account.. The 'metastores.list' command allows administrators to view all Unity Catalog metastores that the current user has permission to see within their Databricks account. This is essential for managing multiple metastores in a complex, multi-region architecture. Understanding which metastores exist and their properties is the first step in ensuring that data governance policies are consistently applied across all regional footprints.

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To list all users registered in the metastore.

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To list all tables within a specific metastore.

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To list all storage credentials linked to the metastore.