Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Test 4 — 15 Questions

Question 1

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

Accepted Answer

Databricks Asset Bundles. Databricks Asset Bundles (DABs) enable developers to package and deploy their projects—including jobs, pipelines, and models—using a declarative, version-controlled approach. This is fundamental for modern MLOps and DataOps workflows, as it replaces manual UI configuration with repeatable, automated deployments. By treating infrastructure as code, teams improve reliability, auditability, and speed when promoting applications from development to production environments.

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Unity Catalog Volumes

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SQL Warehouses

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Delta Sharing

Question 2

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 3

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

Accepted Answer

databricks bundle deploy. The 'databricks bundle deploy' command is the primary interface for deploying resources defined in a Databricks Asset Bundle. It interprets the 'databricks.yml' configuration, validates the resources, and then synchronizes the code and configuration with the targeted Databricks workspace. This CLI-first approach is essential for automating CI/CD pipelines, as it allows developers to trigger deployments programmatically without relying on manual browser-based interactions or fragile API scripts.

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databricks workspace sync

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databricks job create

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databricks deploy project

Question 4

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?

Accepted Answer

Using Databricks Secret Scopes to retrieve credentials.. Using secret scopes within Databricks is the recommended best practice for handling authentication credentials. Secrets allow code to retrieve sensitive information like API keys or database passwords at runtime, keeping these credentials out of the source code. This is a critical security requirement in any production application, as it prevents accidental exposure of sensitive keys in version control systems and allows for centralized management of authentication lifecycle.

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Hardcoding tokens as environment variables in the notebook.

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Storing tokens in a plain text file inside the bundle.

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Creating a public access policy for the external service.

Question 5

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

Accepted Answer

The 'targets' block. The 'targets' block in the 'databricks.yml' file allows developers to define environment-specific configurations. This enables a single bundle to adapt to different workspaces or compute needs. By using targets, you can ensure that development deployments are lightweight and cost-effective, while production deployments are scaled for high availability and performance, all while keeping the underlying application code identical across all deployment stages.

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The 'variables' block at the top level

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The 'resources' block

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The 'include' directive

Question 6

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

Accepted Answer

Enables automated testing through CI/CD pipeline integration. Databricks Asset Bundles standardize the deployment process, making it repeatable, version-controlled, and easier to integrate into CI/CD pipelines. This reduces the risk of configuration drift, where the production environment deviates from the development environment due to manual changes in the UI. By treating infrastructure as code, teams can maintain a clear history of changes and improve the reliability and auditability of their AI application releases.

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Provides a graphical drag-and-drop interface for deployment

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Automatically handles data labeling and cleaning tasks

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Allows users to bypass Unity Catalog governance

Question 7

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

Accepted Answer

Unity Catalog Model Status Transitions. The Unity Catalog Model Registry provides a formal lifecycle management process, including status transitions such as 'Staging', 'Production', and 'Archived'. By using these status labels, organizations can implement a human-in-the-loop approval process where models must pass specific validation criteria—or manual reviews—before they are eligible for the production inference endpoint. This promotes safety, quality, and compliance in machine learning deployments by ensuring only vetted models reach end-users.

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Delta Live Tables Quality Checks

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Databricks Job Task Notifications

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Git branch protection rules

Question 8

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?

Accepted Answer

databricks bundle deploy --target prod. Deploying a modified Mosaic AI Agent that is defined as a bundle resource requires applying the bundle state to the target workspace, which the Databricks CLI does with bundle deploy. Selecting the production target ensures the correct workspace, variables, and permissions are used, and the existing serving endpoint is updated in place instead of being recreated.

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databricks bundle validate --target prod

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databricks serving-endpoints update --name agent-endpoint

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databricks bundle run agent_app --target prod

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

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?

Accepted Answer

Mosaic AI Model Serving, by creating a serving endpoint that serves the registered Unity Catalog model.. Deploying a registered Unity Catalog agent model as a Mosaic AI Model Serving endpoint gives the team a managed REST inference API with automatic scaling and no cluster administration. It preserves Unity Catalog governance and lineage, supports low-latency online serving, and is the intended deployment path for GenAI agents on Databricks. Batch, interactive, and SQL compute cannot provide the same managed serving contract.

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Databricks SQL warehouses, by registering the agent as a SQL function and querying it from a dashboard.

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All-purpose interactive clusters, by starting a cluster and calling the model through a notebook.

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Jobs compute, by scheduling the agent as a nightly batch task and writing predictions to a Delta table.

Question 11

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?

Accepted Answer

Define the node type in a databricks.yml variable and override its value in a target-specific block for each environment.. The bundle should declare a variable for the node type and override it inside each target, because Databricks Asset Bundles resolve variables per target at deploy time. This keeps one authoritative job definition while letting development and production use different compute, and it avoids duplicated YAML that drifts over time.

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Create a separate bundle directory for each environment with entirely duplicated resource files and deploy each one independently.

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Use a notebook widget inside the job to read the node type from a table at runtime and restart the cluster with the new value.

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Hard-code the production node type in the job resource and rely on the development workspace to silently substitute a smaller node type.

Question 12

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?

Accepted Answer

Define target-specific variables in databricks.yml and reference them with ${var.endpoint_name} and ${var.min_replicas} in the resource definition.. Databricks Asset Bundles let you declare variables and override them per target, so a single resource definition can deploy the agent with different endpoint names and replica counts in development and production. Referencing variables with the ${var.} syntax keeps the bundle DRY while honoring environment differences. Hard-coded values, secret scopes, and post-deployment mutation scripts all fail to provide the clean, declarative per-target configuration the scenario requires.

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Use a post-deployment notebook that calls the serving endpoints API to rename the endpoint and adjust replicas after each bundle deployment.

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Store the endpoint name and replica count as secrets in a Databricks secret scope and read them at deployment time with the Databricks CLI.

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Hard-code the production endpoint name and replica count, then maintain a separate copy of the bundle for development.

Question 13

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?

Accepted Answer

Declare bundle variables with defaults in databricks.yml, override them per target, and reference them in the app resource configuration.. Bundle variables with defaults in databricks.yml, overridden per target and referenced in the app resource, let one codebase deploy to dev and prod with different endpoint and index names. The bundle resolves values at deploy time and validates them, so promotion requires no source edits and each target receives the correct configuration without runtime prompts or extra lookups.

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Store the values in a notebook widget and have the app prompt the user for them at startup.

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Hard-code the dev and prod values in the app source and select a branch during deployment.

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Write the values into a Delta table and have the app query that table on every request.

Question 14

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?

Accepted Answer

Create a Mosaic AI Model Serving endpoint that serves the Unity Catalog model version.. Serving a Unity Catalog-registered model through Mosaic AI Model Serving is the standard Databricks pattern for production GenAI endpoints. It provides managed GPU compute, autoscaling, and versioned deployments without the engineer building infrastructure. The other choices either use batch or interactive compute that lacks an inference endpoint, or confuse storage with serving, so none can deliver a low-latency REST API for the chatbot.

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Publish the model artifact to a Delta table and query it with a SQL warehouse.

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Deploy the model to an all-purpose cluster and expose it through a notebook's REST API calls.

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Register the model in the workspace model registry and call it from a Databricks job schedule.

Question 15

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?

Accepted Answer

Store the database credential in a Databricks secret scope, reference it from the endpoint configuration, and enable network connectivity through a private link or VPC configuration for the serving endpoint.. Credentials belong in a Databricks secret scope so they are injected at runtime rather than stored in the artifact, and reaching a private external service from Model Serving requires explicit private connectivity rather than default public routing. Satisfying both constraints means combining secret injection with a private network path.

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Have the retrieval function read the credential from a Delta table at query time and rely on the default public internet route for database access.

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Embed the database credential as an environment variable inside the logged MLflow model so the serving container can read it at startup.

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Package the credential into a Python wheel installed as a model dependency and configure the endpoint with serverless compute only.