Databricks-GenAI-Assoc Practice Test 6 — 10 Questions

Question 1

A generative AI team is building a customer-support assistant on Databricks. The assistant must answer questions using the company's private knowledge base, and the team wants to minimize latency while ensuring that the LLM only uses retrieved documents. They plan to use Databricks Vector Search with a Delta table as the source. Which design choice best balances low latency and grounded responses?

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

Accepted Answer

Use a Vector Search index with a relevance score threshold and retrieve the top 5 chunks, then include those chunks in the prompt with instructions to answer only from the provided context.. Grounding requires retrieving only relevant context and instructing the model to use it. A Vector Search index with a relevance threshold and a small top-k (e.g., 5) reduces prompt size and latency while filtering weak matches. Including those chunks with an explicit instruction to answer from context limits hallucination. This combination balances performance and accuracy better than unfiltered or keyword-only retrieval.

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Use a keyword-based search over the Delta table and pass the top 10 matching rows to the LLM, bypassing Vector Search entirely.

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Store the entire knowledge base in a single prompt and use a long-context LLM to answer questions without retrieval.

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Configure a Vector Search index with a small embedding dimension and retrieve the top 20 chunks, then pass all chunks directly to the LLM without filtering.

Question 2

An engineer is designing a RAG application that uses Databricks Vector Search to retrieve documents and a foundation model endpoint to generate answers. The team wants to log all user queries, retrieved documents, and generated responses for auditing and continuous improvement. They also need to monitor for drift in retrieval quality over time. Which Databricks capability should they integrate into the application design?

Accepted Answer

MLflow Tracing with inference tables on the model serving endpoint.. MLflow Tracing provides detailed spans across the RAG pipeline, capturing queries, retrieved documents, and LLM responses. Inference tables on the serving endpoint automatically log request and response payloads for auditing. This combination enables both observability and drift monitoring. Other options focus on data pipelines, dashboards, or governance, which do not capture the runtime application behavior needed for continuous improvement.

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Delta Live Tables with expectations to validate the retrieved documents.

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Databricks SQL dashboards querying the source Delta table for document updates.

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Unity Catalog audit logs and lineage for the Vector Search index.

Question 3

A GenAI engineer has built a Retrieval Augmented Generation (RAG) application using Databricks Vector Search. The index is created on a Delta table that contains sensitive customer support transcripts. Company policy requires that end users can only retrieve chunks from documents they are authorized to view. The engineer wants to enforce this at query time without duplicating the index. Which approach should the engineer use?

Accepted Answer

Use Databricks Vector Search with the 'filter' parameter in the query, based on user attributes passed from the application.. Databricks Vector Search enables document-level security by allowing metadata filtering at query time. The engineer can include authorization attributes (such as group memberships) as metadata fields when creating the index, then pass a filter expression in the query that matches the user's attributes. This enforces access control without duplicating the index, aligning with the requirement to keep a single index while restricting retrieval to authorized documents.

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Create a separate Vector Search index for each user group and route queries based on the user's group membership.

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Apply Unity Catalog column masks on the embedding column of the source Delta table to redact sensitive text before vectorization.

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Enable Attribute-Based Access Control (ABAC) on the source Delta table using row filter and column mask functions.

Question 4

A GenAI engineer has written a notebook that calls the Databricks Foundation Model APIs to summarize documents. Before the notebook can run in production, the security team wants to confirm exactly which workspace users and service principals are permitted to invoke the pay-per-token foundation model endpoints. Where should the engineer point them to review and manage those permissions?

Accepted Answer

The Unity Catalog privileges on the system.ai schema and its model functions. Foundation Model APIs are surfaced as Unity Catalog functions in the system.ai schema, so access is controlled with Unity Catalog privileges, chiefly EXECUTE on the relevant model function, plus the usual USE CATALOG and USE SCHEMA on system.ai. Directing the security team to those grants gives an auditable, centralized view of exactly which users and service principals can invoke each pay-per-token model.

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The Unity Catalog metastore's default storage location settings

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The workspace admin settings page for cluster policies

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The Azure Databricks access connector assigned to the workspace

Question 5

A developer is building a RAG application and notices that the retrieval step often returns irrelevant context. Which step in the pipeline should be improved to address this?

Accepted Answer

Refine the chunking strategy and embedding model quality.. Improving retrieval accuracy often involves enhancing the quality of the embeddings or the text chunking strategy. By adjusting how data is partitioned into chunks (e.g., overlapping chunks, semantic chunking) or refining the embedding model, the vector search index can better capture the semantic meaning of the documents. This is a common and critical improvement path in RAG development, as the quality of the retrieval is fundamentally limited by the index's representational capability.

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Increase the number of API calls to the LLM.

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Enable auto-scaling on the serving endpoint.

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Switch to a larger, more expensive LLM.

Question 6

Refer to the exhibit. An engineer is automating the deployment of a model to an endpoint using the Databricks CLI. Based on the error log provided, what is the most appropriate action to resolve this deployment failure?

Accepted Answer

Grant the service principal 'USE' and 'READ' permissions on the catalog, schema, and model.. Deployment failures due to 'INSUFFICIENT_PERMISSIONS' often stem from the service principal used by the CI/CD pipeline lacking the necessary grants on the registered model in Unity Catalog. The engineer must ensure the principal has 'USE CATALOG', 'USE SCHEMA', and 'READ' permissions on the model version. Addressing this at the Unity Catalog level is the standard procedure for cross-service authorization in Databricks.

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Upgrade the model serving endpoint to a higher capacity cluster size.

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Delete the model serving endpoint and recreate it using a personal access token.

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Restart the Databricks workspace to force an update of the internal permission cache.

Question 7

A developer is configuring a RAG application and needs to ensure that the LLM response is based on specific, trusted document snippets. Which technique, when implemented correctly, helps mitigate hallucination by grounding the response in provided context?

Accepted Answer

Retrieval Augmented Generation (RAG). Retrieval Augmented Generation (RAG) is the primary technique for grounding LLM responses. By retrieving relevant, trusted documents from a vector store based on a user's query and injecting those documents into the LLM's prompt, the developer forces the model to synthesize an answer based on specific retrieved context rather than relying solely on its internal training data. This significantly reduces hallucinations and increases the accuracy and relevance of the generated responses.

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Increasing the model's temperature parameter

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Fine-tuning the base model on all internal documents

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Using a larger foundation model without context injection

Question 8

Which TWO actions are necessary to ensure that a model serving endpoint in Databricks remains available and performant during peak traffic hours?

Accepted Answer

Enable auto-scaling for the serving endpoint.. Maintaining performance requires both vertical and horizontal scaling strategies. First, configuring auto-scaling on the endpoint allows the infrastructure to adjust to fluctuating demand automatically. Second, ensuring the endpoint uses appropriate instance types—such as GPU-accelerated instances for LLMs—ensures the compute capacity is sufficient for inference tasks. These two actions are foundational for achieving high availability and low latency, preventing request timeouts and ensuring a smooth experience during heavy usage periods.

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Disable the serving endpoint's logging to save on compute cycles.

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Configure the endpoint to run on a single, fixed-size node to minimize latency.

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Manually restart the endpoint every hour to clear the cache.

Question 9

A data engineer is assembling a fine-tuning dataset from a Delta table of conversation transcripts. Each transcript contains a list of message objects with a role and a content field, and the training job requires one row per conversation with the messages serialized into the expected format. Which transformation should the engineer apply?

Accepted Answer

Use the collect_list aggregate to group the message objects back into an array per conversation and serialize that array into the required format.. The training job requires one row per conversation with the message sequence preserved, so the individual message rows must be grouped back together. collect_list aggregates messages into an ordered array per conversation, and serializing that array yields the nested format the job expects. Exploding, pivoting, or flattening would alter the structure and lose the required per-conversation layout.

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Use the explode function to expand each message object into a separate row before writing the dataset.

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Use the pivot function to turn message roles into columns with content values in the cells.

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Use the flatten function to collapse the nested message array into a single string per row.

Question 10

Refer to the exhibit. An engineer is testing a model endpoint. The outputs are too brief and often stop mid-sentence. What is the most likely cause?

Accepted Answer

The max_new_tokens limit is too low for the expected response length.. The 'max_new_tokens' parameter is set to 512, which imposes a hard limit on the length of the generated output. If the model needs more tokens to complete its thought, it will be abruptly cut off. Increasing this limit is the logical troubleshooting step when encountering truncation issues in generative model responses, ensuring the model has sufficient space to finalize its output properly.

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The temperature is too low for the model to generate full sentences.

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The top_p parameter is too high, leading to early termination.

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The model version is incompatible with the specified parameters.