Assembling and Deploying Apps

An organization is deploying a GenAI application using Databricks Model Serving. Which TWO steps are required to ensure the deployment environment handles model governance and observability effectively?

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

You are deploying a RAG application. You need to ensure the model uses the most recent vector data without redeploying the model. What should you use?

A team has deployed a model that is experiencing high latency. How should they identify if the bottleneck is the model inference or the preprocessing code?

Refer to the exhibit. What is the most likely cause for this error in a production RAG application?

When deploying a Generative AI application, why is it recommended to use a dedicated Serving Endpoint rather than a shared interactive cluster?

An organization wants to implement 'Guardrails' on their model outputs. Which deployment strategy best facilitates this?

Which security configuration is essential when deploying a model that accesses sensitive data stored in Unity Catalog?

Refer to the exhibit. What is the impact of min_instances: 0 on this deployment?

Which CI/CD approach for model deployment best minimizes downtime during a model update?

When evaluating an LLM for deployment, what is a crucial 'non-functional' requirement that must be addressed?

A machine learning engineer needs to deploy a custom Mosaic AI Model Serving endpoint that requires access to a private internal database. Which mechanism should be used to securely manage the database credentials?

An organization is deploying an LLM application using Databricks Asset Bundles (DABs). Which TWO of the following are primary benefits of using DABs for the deployment process?

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?

Which component of the Databricks platform allows you to bundle together notebooks, model serving configurations, and pipeline definitions for repeatable deployment?

When deploying a model to Databricks Model Serving, which THREE of the following are best practices to ensure production reliability?

Which workflow best describes the recommended CI/CD process for updating a Databricks Asset Bundle?

What is the primary purpose of the 'bundle validate' command in the Databricks Asset Bundles CLI?

Refer to the exhibit. An engineer wants to perform a canary deployment by routing 10% of traffic to a new version (version 6). How should the JSON traffic configuration be modified?

Which of the following is a primary reason to prefer Databricks Asset Bundles (DABs) over manual workspace deployment?

An AI engineer is developing a custom Databricks App using Mosaic AI Agent Framework and needs to deploy the application workspace securely. Which deployment artifact and configuration mechanism should the engineer use to define the app dependencies and entry point?

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?

A data team is developing a Mosaic AI Agent application. Which TWO of the following are mandatory for deploying this application using the Databricks Model Serving infrastructure?

Refer to the exhibit. An engineer is configuring a canary deployment for a churn prediction model. Based on the provided traffic configuration, what is the expected behavior of the endpoint?

When deploying an application using Databricks Asset Bundles (DABs), which file is the primary entry point to define the project structure and configuration?

Which Databricks feature is specifically designed to allow developers to programmatically manage and version their entire data and AI infrastructure as code?

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

Which command is used within the Databricks CLI to deploy a project defined by a Databricks Asset Bundle?

When deploying a Python-based application that interacts with Unity Catalog, which step is essential to ensure the code can authenticate securely to external services without hardcoding tokens?

Which component of a Databricks Asset Bundle (DAB) allows you to define different configurations (e.g., instance sizes, variables) for development versus production environments?

Which TWO of the following are benefits of using Databricks Asset Bundles for deploying AI applications?

When deploying a model endpoint, which Databricks feature provides the capability to review and approve the model before it is promoted to production?

A GenAI engineer has a Mosaic AI Agent application packaged as a Databricks Asset Bundle with a serving endpoint defined in the bundle's resources. A teammate recently updated the agent's prompt template in the source files, and the engineer now needs to push that change to the existing production endpoint without recreating it. Which Databricks CLI command should the engineer run from the bundle root?

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?

A data science team has registered a new version of a retrieval-augmented generation (RAG) agent in Unity Catalog as a model named main.genai.support_agent. They need to make this version available as a low-latency REST endpoint with automatic scaling and no cluster management. Which Databricks capability should they use?

A Generative AI engineer is packaging a retrieval-augmented generation application for deployment with Databricks Asset Bundles. The bundle must provision a Databricks job that periodically refreshes a Delta table used as the vector index, and the job requires a specific cluster node type that differs between the development and production workspaces. Which approach correctly handles the node type difference while keeping a single bundle definition?

An engineer is packaging a GenAI agent application with Databricks Asset Bundles so that the same bundle deploys to a development and a production workspace. The agent's serving endpoint name must differ per target, and the production endpoint needs more concurrent capacity. Which mechanism in the bundle configuration should the engineer use?

A generative AI engineer is packaging a retrieval-augmented generation (RAG) application so it can be deployed as a Databricks App. The app reads the vector index name and the serving endpoint name from environment configuration so the same code can run in dev and prod. Which approach correctly supplies these values at deploy time using Databricks Asset Bundles?

A GenAI engineer has registered a fine-tuned Llama 3 model in Unity Catalog as `prod.ml.models.support_llm` and now needs to expose it as a REST endpoint for a customer-facing chatbot. The team wants Databricks to manage the serving infrastructure, GPU autoscaling, and version upgrades with minimal operational overhead. Which action should the engineer take?

An engineer is deploying a RAG application whose retrieval step calls an external vector database over the public internet. The serving endpoint must reach that database, but corporate policy forbids exposing credentials in the model artifact and forbids public egress from the serving environment. Which configuration satisfies both constraints?

A team deploys a RAG agent to a Mosaic AI Model Serving endpoint. During load testing, requests intermittently return HTTP 429 responses even though the endpoint shows healthy replicas. The agent calls an external vector search index and a foundation model endpoint. Which action most directly addresses the 429 responses?

A team deploys a Mosaic AI Agent application through Databricks Apps and must let the app call a Model Serving endpoint that enforces Unity Catalog permissions on the underlying model. Which TWO configurations are required for the app to authenticate and be authorized to query that endpoint? (Choose two.)

A data scientist has developed a GenAI application locally and now needs to share it with stakeholders as an interactive web app inside the Databricks workspace. The app must use the workspace's existing authentication and compute. Which Databricks capability should be used?

A team is deploying a Retrieval-Augmented Generation application as a Mosaic AI Agent using `databricks.agents.deploy()`. The agent must query a Vector Search index and a Delta table in Unity Catalog. During testing, the endpoint returns permission errors when accessing those resources, even though the deploying user has access. Which configuration should the engineer apply to resolve this?

A team is preparing a Databricks Asset Bundle that deploys a GenAI application consisting of a job that builds a vector index and a Model Serving endpoint that hosts the agent. Before merging, they want the pipeline to validate and deploy the bundle to a staging workspace automatically. Which TWO bundle capabilities should the pipeline rely on? (Choose two.)

A team maintains a Mosaic AI Agent application whose endpoint must call an external LLM provider through a secret stored in a Databricks secret scope. During a deployment pipeline run, the endpoint build step fails while resolving the credential, even though the secret scope exists and the notebook test works. Which configuration should the engineer verify first?

A platform team is preparing to deploy a GenAI chat application built on Mosaic AI Model Serving. They want the deployment to enforce least-privilege access for the application and to keep the serving environment reproducible. Which two practices should they implement? (Choose two.)

An engineer has finished building a RAG chatbot and wants to expose it as a Databricks App so business users can reach it through a browser. The app needs a Python web server and a command that starts it. Which artifact in the app's project layout defines the runtime command and dependencies used when the app is deployed?

An engineer is preparing to deploy a Mosaic AI Agent to a Model Serving endpoint. The agent depends on a custom Python library that is not available on PyPI. Which approach ensures the library is available at serving time?

An engineer maintains a GenAI application that uses a Databricks Asset Bundle to deploy a Mosaic AI Agent serving endpoint. A new model version has been logged and validated, and the team wants to roll it out to production with the ability to revert quickly if quality regressions appear. Which deployment approach best satisfies this?

An engineer is packaging a custom PyFunc model that wraps an open-source LLM and must deploy it to a Databricks Model Serving endpoint. The endpoint must load the model from Unity Catalog and expose it through a REST API. Which two actions are required to make the deployment succeed? (Choose two.)

A Mosaic AI Agent application deployed as a Databricks App intermittently returns stale answers after the team updates the underlying vector index. The app caches a client to the serving endpoint and an index handle at module import time. Which change best resolves the staleness while keeping latency low?

A team is deploying a GenAI application to a Databricks Model Serving endpoint and wants to ensure the deployment can be rolled back quickly and that traffic is shifted safely during updates. Which TWO practices should the team follow? (Choose two.)

A team wants to review what a Databricks Asset Bundle deployment would change in the production workspace before it actually creates or modifies any resources. Which command should the engineer run first?

A team wants its Databricks App to read a secret that stores an external API key used by an agent tool. The secret is managed in a Databricks secret scope. Which approach correctly exposes the secret to the running app without putting the value in source control?

A data science team wants to expose a fine-tuned LLM as a REST API for an internal web application. They have registered the model in Unity Catalog and want Databricks to manage the serving infrastructure, autoscaling, and request routing. Which Databricks capability should they use?

A GenAI team's Mosaic AI Agent application is deployed via a Databricks Asset Bundle and served through a Model Serving endpoint. They need the endpoint to call a Unity Catalog function as a tool during inference, and the function reads from a table the endpoint's service principal cannot currently access. What should the engineer do?

An engineer deployed a RAG agent to a Model Serving endpoint and enabled inference tables for payload logging. After a week, reviewers notice that logged requests contain customer email addresses and order identifiers. Compliance requires that raw prompts and responses not be stored in plain text. Which change should the engineer make?

A team is using `databricks.agents.deploy()` to publish a Mosaic AI Agent to a serving endpoint. They must expose an environment-specific Vector Search index name and the endpoint name to the deployment without hardcoding values in the notebook, and the same notebook must run in dev and prod. Which approach should the engineer use?

A financial services company has registered a Mosaic AI Agent application in Unity Catalog and wants to serve it through a Databricks Model Serving endpoint that queries a Delta table containing sensitive customer records. The security team requires that the endpoint access the table using a dedicated service principal with least privilege, and that the agent's LLM calls go through a governed gateway that logs usage. Which TWO configurations should the team apply to meet these requirements? (Choose two.)

A team has developed a GenAI chatbot using the Databricks Mosaic AI Agent Framework and logged it with MLflow. They now want to expose it as a REST API that their customer support portal can call, with autoscaling and built-in monitoring. Which Databricks capability should they use to host the agent?

A team deploys a Mosaic AI Agent application to a Databricks Model Serving endpoint. During load testing they observe that the first request after an idle period takes several seconds, while subsequent requests are fast. They want to eliminate this cold-start penalty for a latency-sensitive customer-facing application while keeping costs reasonable during off-peak hours. Which configuration should they apply?

An engineer is preparing to deploy a Mosaic AI Agent application with Databricks Asset Bundles. The bundle defines the agent, the serving endpoint, and a job that refreshes the vector index. The engineer wants the deployment to target a staging workspace and a production workspace with different endpoint names and different Unity Catalog catalog names, without editing files between deployments. Which approach should the engineer use?