Databricks-GenAI-Assoc Application Development Practice Test 6 — 15 Questions

Question 1

A GenAI engineer has registered a RAG chain in Unity Catalog as a model and now needs to deploy it for real-time inference with per-request token usage and latency captured automatically. Which Databricks capability should they enable on the serving endpoint?

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

Accepted Answer

Enable inference tables on the Mosaic AI Model Serving endpoint.. Inference tables are the native Mosaic AI Model Serving feature that persists request and response payloads plus metadata to a Delta table, giving automatic capture of token usage and latency for real-time endpoints. Autoscaling, MLflow experiments, and Unity Catalog lineage address capacity, development tracking, and governance respectively, but none record per-request serving telemetry.

Answer

Attach an MLflow experiment to the endpoint and rely on the run history for telemetry.

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Enable Unity Catalog lineage on the registered model version.

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Configure the endpoint with autoscaling enabled to log per-request metrics.

Question 2

A GenAI engineer is building a retrieval-augmented generation application on Databricks. They want to store document embeddings and perform fast approximate nearest-neighbor search without managing a separate vector database. They have already created a source Delta table with columns: id (string), text (string), and embedding (array<float>). Which Databricks feature should they use to create a Vector Search index that automatically syncs with the Delta table?

Accepted Answer

Databricks Vector Search with a Delta Sync index. Databricks Vector Search is the managed service designed for storing embeddings and performing fast similarity search. A Delta Sync index automatically syncs with a source Delta table, so when new documents or embeddings are added, the index updates without manual intervention. This eliminates the need to manage a separate vector database and integrates natively with Databricks workflows.

Answer

Databricks Feature Store with a training set

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MLflow Model Registry with a custom PyFunc model

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Delta Live Tables with a materialized view

Question 3

A GenAI engineer is developing an agent using Databricks Mosaic AI Agent Framework. The agent must call an external API to fetch real-time stock prices. The engineer wants to ensure the agent can be evaluated and deployed with minimal changes between development and production. Which approach should the engineer take to define the tool-calling logic?

Accepted Answer

Implement the API call as a Python function and register it as a tool in the agent's LangChain or similar framework, then log the agent with MLflow.. The Mosaic AI Agent Framework supports defining tools as Python functions that the agent can invoke. By registering the function as a tool and logging the agent with MLflow, the same code is used for evaluation and deployment, ensuring consistency. Other approaches either do not execute the call, introduce delay, or add architectural complexity that hinders portability.

Answer

Create a separate Databricks job that periodically fetches stock prices and writes them to a Delta table, then have the agent query that table.

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Use a Databricks SQL function to call the external API via a UDF, and invoke it from the agent.

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Hardcode the API call directly in the agent's prompt as a description of the endpoint.

Question 4

An AI engineer is building a RAG application and wants to log the retrieval context and generated response for each request to MLflow for evaluation. They are using the `mlflow.langchain` flavor. Which method should they use to log the model so that MLflow automatically captures the necessary artifacts?

Accepted Answer

mlflow.langchain.log_model. For LangChain models, `mlflow.langchain.log_model` is the specialized logging function that captures the chain's configuration and enables MLflow to track inputs and outputs. This allows the engineer to later evaluate retrieval context and responses using MLflow's evaluation capabilities. Other logging methods do not provide this automatic integration.

Answer

mlflow.sklearn.log_model

Answer

mlflow.tensorflow.log_model

Answer

mlflow.pyfunc.log_model

Question 5

An engineer built an agent using Mosaic AI Agent Framework and wants the agent to call a Unity Catalog function that returns customer order history. The function must be invoked by the LLM at runtime without exposing raw SQL to the model. Which approach should the engineer use?

Accepted Answer

Register the Unity Catalog function as a tool in the agent and let the LLM select it by name and arguments.. Registering the Unity Catalog function as a tool gives the agent a typed, governed interface the LLM can invoke by name with structured arguments. The function's metadata supplies the schema, so no SQL is exposed to or generated by the model. Prompt-embedded SQL, scheduled jobs, and text-to-SQL all fail the requirement of runtime, governed invocation without model-authored queries.

Answer

Expose the function through a SQL warehouse and have the agent send natural-language queries to the warehouse endpoint.

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Embed the SQL body of the function in the system prompt so the LLM can generate the query when needed.

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Create a Databricks job that runs the function on a schedule and writes results to a Delta table the agent reads.

Question 6

A developer is using MLflow to track experiments for a generative AI application. They want to log a prompt template and its associated parameters so that they can reproduce the exact input to the model later. Which MLflow function should they use to log the prompt template as an artifact?

Accepted Answer

mlflow.log_artifact. Prompt templates are best stored as artifacts because they are files that may be reused, versioned, and referenced independently of the run. mlflow.log_artifact provides a robust way to persist such files, ensuring reproducibility and easy access later.

Answer

mlflow.set_tag

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mlflow.log_metric

Answer

mlflow.log_param

Question 7

A GenAI engineer is building a multi-step agent that uses Databricks Foundation Model APIs. The agent must decide when to call a weather tool and when to answer directly. The engineer wants to ensure the agent's decision-making is reliable and that failures in tool calls are handled gracefully. Which design approach should the engineer use?

Accepted Answer

Implement a ReAct-style loop where the LLM outputs a thought, action, and action input, and the agent executes the tool and feeds back the observation.. A ReAct-style loop enables the agent to reason about whether a tool is needed, execute it, and observe the result before deciding the next step. This iterative process supports graceful error handling because exceptions can be captured and returned as observations, allowing the LLM to adjust. Single-pass or fixed-chain approaches lack this adaptability and feedback.

Answer

Chain multiple LLM calls where the first call always invokes the weather tool and the second call formats the answer.

Answer

Fine-tune the LLM on examples of weather queries and answers, then deploy it without tools.

Answer

Use a single prompt that instructs the LLM to output a JSON with either an answer or a tool call, and parse it once.

Question 8

A developer is creating a Databricks notebook to prototype a GenAI application. They need to install the `databricks-langchain` library to use LangChain integrations with Databricks. Which command should they use in the notebook?

Accepted Answer

%pip install databricks-langchain. In Databricks notebooks, the `%pip install` magic command is the recommended way to install Python libraries. It ensures the package is installed in the notebook's isolated environment and is immediately available for import. Using `%pip` with `databricks-langchain` correctly sets up the LangChain integrations needed for the GenAI application.

Answer

%conda install databricks-langchain

Answer

dbutils.library.installPyPI("databricks-langchain")

Answer

!pip install databricks-langchain

Question 9

A GenAI engineer has a RAG application whose retrieval step uses Databricks Vector Search. Users report that answers are sometimes irrelevant because the retriever pulls chunks from documents the user is not authorized to see. The engineer must enforce per-user document ACLs at query time without re-indexing the corpus. Which approach should the engineer take?

Accepted Answer

Add a metadata column that stores the document's ACL principals, then pass a filter string with the caller's identity to the Vector Search query.. Vector Search indexes can carry metadata columns that are queryable through the filters parameter, letting the application pass the caller's identity so only permitted chunks are returned. This satisfies per-user ACL enforcement at query time without rebuilding the index when permissions change, and it avoids leaking unauthorized content into the application tier. The other approaches either do not affect index results or are operationally impractical at scale.

Answer

Post-filter the retrieved chunks in the application layer by querying Unity Catalog for the user's group memberships and dropping chunks that fail the check.

Answer

Create a separate Vector Search index per user and have the application route each request to the user-specific index.

Answer

Enable Delta Sharing on the source Delta table and rely on the workspace's existing table ACLs to filter Vector Search results.

Question 10

A developer needs to store prompt templates, model parameters, and evaluation results for a GenAI application so that each iteration can be compared and reproduced later. Which Databricks capability should they use?

Accepted Answer

MLflow Tracking with experiments and runs. MLflow Tracking provides experiments and runs that capture parameters, metrics, and artifacts, making it the right tool to version prompts, record model settings, and store evaluation results for comparison and reproduction. Data pipelines, volumes, and dashboards serve different purposes and do not offer run-based experiment tracking.

Answer

Delta Live Tables pipelines

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Databricks SQL dashboards

Answer

Unity Catalog volumes

Question 11

A GenAI engineer is developing a conversational agent using Databricks. The agent must maintain context across multiple turns and retrieve relevant information from a knowledge base. They want to ensure the agent can handle follow-up questions that refer to previous exchanges. Which TWO techniques should they implement to manage conversation state and retrieval effectively? (Choose two.)

Accepted Answer

Store the conversation history in a Delta table and include it in the prompt for each turn.. Storing conversation history in a Delta table ensures persistence and allows inclusion of past turns in the prompt for context. Rewriting follow-up questions into standalone queries using that history improves retrieval accuracy by making the query self-contained. Together, these techniques enable the agent to handle multi-turn conversations effectively, where follow-up questions depend on earlier exchanges, and ensure the retrieval step has the necessary context.

Answer

Cache all responses from the LLM to avoid re-processing similar queries.

Answer

Use a separate Vector Search index for each conversation to retrieve relevant past messages.

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Use a fixed window of the last three messages as context, ignoring older messages.

Question 12

A GenAI engineer is building a retrieval-augmented chatbot whose answers must cite the exact source document and page. The team wants the chatbot's responses to include structured citations that downstream UIs can render. Which Databricks feature should the engineer use to return these structured citations from the model endpoint?

Accepted Answer

Mosaic AI Agent Framework's document citation support, where the retriever returns chunks with metadata and the agent formats them into a structured citations field in the response.. Structured citations must be part of the model endpoint's response contract, not a side channel. The Mosaic AI Agent Framework's citation support lets the retriever supply chunk metadata and the agent emit a citations field the UI can render directly. Observability traces, retrieval reranking, and payload logging serve different purposes and cannot substitute for returning structured citations to the caller at inference time.

Answer

Databricks Vector Search's built-in reranking, which automatically appends source metadata to the model's generated answer.

Answer

MLflow Tracing with span attributes that capture the retrieved document IDs and page numbers for each LLM call.

Answer

Mosaic AI Model Serving's payload logging, which writes each request and response to an inference table that the UI can read for citations.

Question 13

A team is deploying a GenAI application using Mosaic AI Model Serving. They want to ensure that the endpoint can handle sudden spikes in traffic without dropping requests. Which feature should they configure?

Accepted Answer

Autoscaling. Autoscaling is designed to automatically adjust the serving endpoint's capacity based on incoming traffic. It ensures that the endpoint can handle sudden spikes by adding more resources and scales down when traffic decreases. This maintains availability and performance without manual intervention, making it the correct choice for handling variable loads.

Answer

Request batching

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Model versioning

Answer

Provisioned throughput

Question 14

A team is building a retrieval-augmented generation application on Databricks and wants to reduce hallucination by improving the quality of retrieved context before it reaches the LLM. Which TWO techniques should they apply? (Choose two.)

Accepted Answer

Apply a reranker model to the top-k retrieved chunks before passing them to the LLM.. Reranking retrieved candidates with a cross-encoder and improving chunking with overlap and metadata both raise the relevance of the context supplied to the LLM, which directly reduces hallucination. Raising temperature, stuffing the full corpus, and dropping semantic search all degrade grounding rather than improve it.

Answer

Increase the LLM temperature to encourage more diverse answers.

Answer

Disable the Vector Search index and rely on keyword matching only.

Answer

Store the entire document corpus in the prompt for every request.

Question 15

A developer is packaging a GenAI chat application as an MLflow model that will be deployed to a Mosaic AI Model Serving endpoint. The application needs to load a retrieval index and a prompt template at startup so the first request is not slowed by initialization. Which MLflow logging pattern should the developer use?

Accepted Answer

Log the application with mlflow.pyfunc.log_model and perform the heavy initialization in the model's load_context method so resources are ready before predict is called.. MLflow's PyFunc flavor calls load_context when the model is loaded, which happens before the endpoint begins serving requests. Initializing the retrieval index and prompt template there ensures they are ready once per replica and reused across calls. Loading inside predict or fetching resources per request would add latency, and a signature alone cannot prepare application state.

Answer

Use mlflow.pyfunc.log_model with a signature only, and rely on the serving environment's default warm-up to populate the index and template automatically.

Answer

Log only the prompt template as an artifact and let the Model Serving endpoint fetch the index from Unity Catalog on each request.

Answer

Log the application with mlflow.pyfunc.log_model and load the index and template inside the predict function on every request.