Application Development

A data engineering team is using Databricks to prepare data for a RAG application. They want to ensure that document chunks are of consistent size and quality. Which tool should they use within the Databricks notebook environment to achieve this?

An AI engineer is developing a real-time customer service chatbot application using Databricks Model Serving and needs to securely store API keys and database credentials without hardcoding them into the application code or notebook. Which approach should the engineer use?

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?

Refer to the exhibit. A developer is registering a model. Why is the model signature, as shown in the exhibit, considered a best practice for model registration?

A team is deploying a fine-tuned LLM using Mosaic AI Model Serving. To reduce the cost of serving the model while maintaining acceptable performance, which strategy should be prioritized?

A developer is deploying a custom LangChain agent application as a custom Python model to Databricks Model Serving. The application depends on a specific third-party library that is not included in the standard Databricks runtime environment. How should the developer ensure the dependency is installed when the model is loaded into the serving container?

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?

Which approach is most effective for managing the dependencies of a custom ML model when deploying it to Mosaic AI Model Serving?

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?

A developer is configuring a model serving endpoint as shown in the exhibit. They observe that the endpoint fails to respond quickly to the first request after a period of inactivity. What is the cause of this behavior?

A team is developing a generative AI application that uses an LLM to answer questions based on internal documents. They want to ensure the application is robust and provides accurate responses. Which TWO practices should they implement to improve the reliability of the application? (Choose two.)

A company is scaling its RAG applications. Which THREE of the following are benefits of using Databricks Vector Search over managing a standalone vector database outside of the platform?

Which of the following is a primary benefit of using Unity Catalog to manage models for a RAG application?

A GenAI engineer is developing an agent on Databricks that must call a custom Python function to query an inventory database. The agent must decide when to invoke the function and must receive the result back into its reasoning loop. Which TWO actions are required to expose the function as a tool the agent can call? (Choose two.)

Refer to the exhibit. A developer wants to update this serving endpoint configuration to ensure it handles high-concurrency requests with consistent latency. Which change should be applied to the configuration?

Which THREE practices are recommended when using MLflow for managing LLM experiments in Databricks?

Refer to the exhibit. A developer wants to enable monitoring for their deployed LLM endpoint. Given the current configuration, what must the developer change to ensure that request/response logs are captured for analysis?

A developer is building an agentic workflow using Databricks and LangChain. The agent needs to decide whether to answer a user's query directly or call an external tool to retrieve additional information. The developer wants to ensure the agent's decisions are logged for debugging and auditing. Which approach should they take to achieve this?

A GenAI engineer is building a chatbot using Databricks Foundation Model APIs. They need the chatbot to maintain conversation context across multiple user turns and also allow the use of custom tools like a weather API. Which approach should they take?

A GenAI engineer is deploying a chain that calls an external LLM API. The chain must not block the serving thread while waiting on the remote API, and the endpoint must handle many concurrent requests. Which implementation approach should the engineer choose when logging the model?

A GenAI engineer is building a retrieval-augmented generation (RAG) application using Databricks Vector Search. During testing, they observe that for some queries, the retrieval step returns document chunks that are semantically similar but not actually relevant to the user's question, leading to poor answer quality. They want to improve retrieval precision without changing the embedding model. Which of the following approaches is most appropriate?

An engineer is troubleshooting a Vector Search index that fails to update. The index relies on a Delta table that is frequently updated. What is the most likely cause for the index failing to reflect new data?

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?

A team needs to provide fine-grained access control to an LLM application that uses a vector index stored in a Delta table. Which Unity Catalog feature should they use to restrict access to specific rows based on user identity?

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.)

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?

A developer is building a RAG application. Which step is essential to prevent the model from hallucinating or providing outdated information?

When developing a Generative AI application in Databricks, which tool provides a collaborative environment for engineers to write code, visualize data, and document their experiments using Markdown?

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.)

An organization requires that all GenAI models deployed in Databricks be tracked and managed with a unified registry for compliance. Which feature should the developer use?

A team is designing an LLM application that requires strict data privacy. Which TWO approaches ensure that sensitive data is not leaked during the model inference process or training?

When integrating an external LLM via a Databricks Model Serving endpoint, how should the API credentials be managed to ensure they are not exposed in the application code?

A developer is creating a Databricks notebook to orchestrate a GenAI pipeline that includes data ingestion, vector index refresh, and model inference. They want to ensure that the pipeline can be easily tested and deployed across different environments. Which Databricks feature should they use to define the pipeline as code and manage deployments?

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?

When designing an agentic workflow in Databricks, which TWO practices are essential to ensure the application remains observable and maintainable?

An AI engineer is deploying a RAG application using Databricks Model Serving. They need to ensure the endpoint can handle high traffic with low latency and automatically scale based on demand. Which configuration should they use?

Which tool in the Databricks ecosystem is best suited for developers to experiment with prompt engineering and tool-calling logic iteratively?

An AI engineer is using MLflow to track experiments for a generative AI application. They want to log parameters, metrics, and artifacts for each run, and later compare runs to select the best model. Which MLflow component should they use to organize runs into a named group for a specific project?

Which Databricks feature allows developers to track experiment parameters, model artifacts, and evaluation metrics in a structured way?

Which Databricks asset is best suited for scheduling and orchestrating a multi-step GenAI pipeline that includes data ingestion, vector index updating, and model evaluation?

A developer is creating a custom model serving endpoint that requires an external API call for data enrichment. What is the recommended way to handle sensitive API keys within the Databricks environment?

An engineer is using Mosaic AI Agent Evaluation to score a conversational agent that calls tools. The agent sometimes answers correctly but with fabricated citations. Which evaluation approach best surfaces this specific failure mode?

When building an application that retrieves context from Databricks Vector Search, what is the recommended data format for storing the document chunks?

A developer needs to monitor the performance of an LLM application in production. They want to track the latency of their model endpoint. Where can they find this metric in the Databricks workspace?

When building a RAG application, a developer wants to ensure that the retrieved context is strictly limited to documents the user has access to. Where should this security logic be enforced?

An AI engineer is designing a scalable customer support application on Databricks that integrates custom vector search indexes with a fine-tuned LLM. Which TWO architectural components are essential for enabling efficient similarity search and low-latency retrieval within the Databricks ecosystem? (Choose TWO)

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?

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?

A team is evaluating their RAG application using Mosaic AI Model Evaluation. Which TWO metrics are most relevant for assessing the quality of the generated responses?

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?

Refer to the exhibit. An AI engineer is configuring a Databricks Asset Bundle (DAB) to deploy a generative AI application. When executing 'databricks bundle deploy --target prod', which workspace host will the bundle resources be deployed to, and why?

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?

Which component in the Databricks GenAI stack is responsible for orchestrating the flow between data retrieval, prompt construction, and model invocation?

A developer is building a RAG application using Mosaic AI Model Serving. They need to ensure that the model endpoint logs inference requests and responses for audit purposes. Which configuration parameter should they enable?

A developer needs to deploy a custom Python model that requires non-standard library dependencies. Which MLflow feature should the developer use to specify these environment requirements during model logging?

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?

Refer to the exhibit. A developer is deploying a model using the provided JSON configuration. What is the primary benefit of setting 'scale_to_zero_enabled' to true in this production RAG application?

A GenAI engineer is building an agent with LangChain on Databricks. The agent must call a Unity Catalog function `catalog.schema.get_weather` to fetch current weather. They want the LLM to decide when to invoke this function. Which LangChain component should they use to expose the Unity Catalog function to the LLM?

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?

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?

Which TWO of the following are mandatory requirements for developing an AI application using the Databricks Mosaic AI Model Serving environment?

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?

When designing a production-ready Databricks notebook for model inference, which TWO practices improve maintainability and performance?

A developer is using MLflow to track experiments for a RAG application. They want to log the retrieval step's parameters, such as the number of documents retrieved (k) and the embedding model used. Which MLflow API should they use?

A developer deploys a new model version as shown in the exhibit. What is the purpose of this configuration?

When developing a feature engineering pipeline using Feature Store, which practice ensures maximum code reusability across training and inference?

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

Which Databricks feature is specifically designed to facilitate the rapid development and deployment of LLM applications by providing a managed environment for hosting and testing prompts?

A developer is building a RAG application using Mosaic AI Model Serving. They need to ensure that the embedding model endpoint is strictly accessed only by specific service principals within the workspace. Which feature should the developer configure to enforce this security requirement?

A developer is building a retrieval-augmented generation (RAG) application on Databricks. They need to ensure that embeddings are updated automatically when the underlying Delta table changes. Which approach is the most efficient and scalable?

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?

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?

Refer to the exhibit. A developer encounters this error when trying to call a Model Serving endpoint from a job. Which action should the developer take to resolve this authorization failure?

A GenAI engineer has built a retrieval-augmented generation (RAG) application using Databricks Vector Search and a Databricks-hosted LLM served via Mosaic AI Model Serving. Users report that responses are sometimes irrelevant or cite incorrect document passages. The engineer wants to systematically improve answer quality by identifying which retrieved chunks are actually being used by the LLM. Which approach should the engineer take to capture the relationship between retrieved context and the generated response for later evaluation?

A team is deploying a LLM-based application using Databricks Model Serving. They want to implement robust observability and monitoring for their endpoint. Which TWO features should they utilize to track performance and quality metrics? (Select TWO)

Refer to the exhibit. The developer is attempting to log a custom model to the Unity Catalog. Which configuration is missing to ensure the model is registered correctly under the specified Unity Catalog location?

A GenAI engineer is building a retrieval-augmented generation (RAG) application using Databricks Vector Search. They notice that the retriever sometimes returns irrelevant chunks that hurt answer quality. They want to add a reranking step to improve the relevance of retrieved documents before passing them to the LLM. Which component should they add to their RAG pipeline?