Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Test 5 — 15 Questions

Question 1

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?

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

Accepted Answer

Increase the endpoint's provisioned concurrency and configure appropriate rate limits so the agent can handle the incoming request rate.. A 429 from a Mosaic AI Model Serving endpoint signals that inbound request rate exceeds the endpoint's provisioned capacity or configured rate limits. Raising provisioned concurrency and setting realistic rate limits aligns capacity with the load-test traffic and directly removes the rate-limiting responses. Client retries, smaller prompts, and index relocation do not increase the endpoint's ability to accept concurrent requests, so they leave the root cause unaddressed.

Answer

Move the vector search index into the same Unity Catalog schema as the agent model to reduce cross-service latency.

Answer

Add retry logic with exponential backoff and jitter to the client calling the endpoint.

Answer

Switch the agent's foundation model calls to a smaller context window to reduce token usage per request.

Question 2

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.)

Accepted Answer

Grant the app's service principal the appropriate privileges on the serving endpoint and the model it serves.. Querying a Unity Catalog-governed serving endpoint requires both a valid identity and the right privileges. The app must present an OAuth token for its service principal so the request is authenticated, and that service principal must be granted access to the endpoint and the served model so authorization succeeds. Together these let the app call the endpoint without embedding human credentials.

Answer

Register the app as a Delta Sharing recipient and share the model with it.

Answer

Enable public network access on the serving endpoint so the app can reach it without a token.

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Embed a personal access token for a workspace admin in the app source so the endpoint always resolves an admin identity.

Question 3

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?

Accepted Answer

Databricks Apps, which host the application within the workspace and integrate with workspace identity and compute.. Databricks Apps is designed for hosting interactive applications directly in the workspace, using the workspace's identity and compute. This lets stakeholders access the GenAI app through a URL with existing authentication, avoiding the need to build custom hosting or manage separate credentials.

Answer

A Databricks job with a notebook task scheduled to run every five minutes.

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An MLflow experiment tracking run that logs the application as an artifact.

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A Databricks notebook with the %run magic command to execute the application logic on demand.

Question 4

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?

Accepted Answer

Attach a serving credential via a Unity Catalog service principal and grant that principal access to the index and table.. Mosaic AI Agent endpoints execute under a dedicated service identity, not the deploying user's identity, so Unity Catalog resources must explicitly grant that identity access. Configuring a serving credential tied to a service principal and granting it the necessary privileges on the Vector Search index and Delta table lets the agent authenticate at runtime. Token-based workarounds either expire or over-privilege, and Unity Catalog enforcement cannot be disabled.

Answer

Disable Unity Catalog enforcement on the endpoint so it can read resources with the deploying user's identity.

Answer

Grant the deploying user's personal access token to the endpoint and store it in the model signature.

Answer

Set the endpoint's environment variable `DATABRICKS_TOKEN` to the workspace owner's token.

Question 5

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.)

Accepted Answer

Run databricks bundle deploy with the staging target to create or update the job, endpoint, and related resources in the staging workspace.. A bundle pipeline validates the configuration against the target workspace and then deploys it to that target. Validation catches malformed or unresolvable configuration before anything changes, and deployment applies the resolved resources to the staging workspace, which together form the automated promotion path the team wants.

Answer

Run databricks bundle schema to regenerate the JSON schema and commit it to the repository.

Answer

Run databricks bundle run to execute the vector index job as part of the merge validation.

Answer

Run databricks bundle generate to create resource definitions from existing workspace objects before each deployment.

Question 6

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?

Accepted Answer

That the serving endpoint's service principal or the deploying user has READ permission on the secret scope and that the code reads the secret at load time using the correct scope and key.. Credential resolution in Model Serving depends on the identity that loads the model having READ access to the secret scope and on the code requesting the correct scope and key. A notebook author with access may succeed while the endpoint identity fails, and a typo in scope or key produces the same symptom. Checking permissions and the retrieval call resolves the failure without restructuring the deployment.

Answer

That the secret scope is replicated into the Unity Catalog metastore attached to the serving endpoint.

Answer

That the endpoint has been configured with a personal access token embedded directly in the model signature.

Answer

That the secret scope is mounted as a Databricks secret scope in the serving endpoint's environment variables.

Question 7

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.)

Accepted Answer

Pin the model's dependency versions in the logged MLflow environment so the serving environment installs a known, reproducible set of packages.. Least privilege is achieved by granting the application principal only endpoint query rights plus the minimum Unity Catalog reads for the model and its data. Reproducibility is achieved by pinning dependency versions in the logged MLflow environment so the serving container installs a known package set. Workspace admin, dropping Unity Catalog, and always-latest installs each trade away security or reproducibility, so they do not meet the two stated goals.

Answer

Allow the endpoint to install the latest version of every dependency at startup so the environment always has current patches.

Answer

Give the application principal workspace admin so it can create endpoints and modify cluster policies as needed during deployment.

Answer

Disable Unity Catalog for the model and store it in the workspace model registry to simplify permission management.

Question 8

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?

Accepted Answer

The app.yaml file, which declares the command to run and the environment or dependencies for the app.. A Databricks App is launched according to its app.yaml configuration, which specifies the command that starts the web server along with source and dependency information. The bundle file orchestrates deployment, requirements files list packages, and model signatures describe schemas, but only the app configuration tells the platform how to run the application.

Answer

The MLflow model signature stored with the logged model, which encodes the serving command.

Answer

The requirements.txt file alone, which both lists dependencies and specifies the server start command.

Answer

The databricks.yml bundle file, which contains the Python entrypoint and pip requirements for the app.

Question 9

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?

Accepted Answer

Include the library as a wheel file in the MLflow model's requirements or artifacts so it is installed in the serving environment.. Model Serving builds its container from the MLflow model's declared environment, so custom libraries must be packaged as artifacts and referenced in the model's requirements. This ensures the dependency is installed in the serving environment and the agent can import it reliably.

Answer

Copy the library source into the notebook that defines the agent and rely on the notebook's sys.path.

Answer

Install the library manually on the driver node of the cluster used to log the model.

Answer

Add the library to the workspace's global init script so all clusters install it at startup.

Question 10

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?

Accepted Answer

Create a new serving endpoint version by updating the endpoint's served model entity to the new model version, then verify quality and roll back by pointing the entity back to the previous version.. Updating the served model entity on an existing endpoint moves production traffic to the validated model version while keeping the endpoint URL and configuration stable, so clients are unaffected. If quality regresses, reverting the served entity to the previous version restores prior behavior quickly. Recreating endpoints, notebook-only testing, and cross-workspace routing each introduce downtime, delay, or unnecessary complexity instead of a clean, reversible cutover.

Answer

Keep the existing endpoint unchanged and run the new model version only in notebooks until the team is confident, then deploy it later.

Answer

Deploy the new model version to a second workspace and route production traffic there through a global load balancer.

Answer

Delete the production endpoint and create a brand-new endpoint with the new model version, then update all clients to the new URL.

Question 11

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.)

Accepted Answer

Register the logged model in Unity Catalog and grant the serving identity USE CATALOG, USE SCHEMA, and EXECUTE or SELECT privileges on the model version.. A successful custom model deployment requires a correctly logged artifact with all dependencies declared, and a Unity Catalog registration with privileges granted to the serving identity. Environment build failures and permission errors are the two dominant causes of endpoint creation failure. Flavor conversion, attached clusters, and scale-to-zero settings are unrelated to whether the deployment itself can complete.

Answer

Set the endpoint's scale-to-zero configuration to disabled so the model stays in memory permanently.

Answer

Attach the model to a Databricks cluster that stays running so the endpoint can proxy requests to it.

Answer

Convert the PyFunc wrapper to a scikit-learn estimator so the serving container recognizes the flavor.

Question 12

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?

Accepted Answer

Initialize the endpoint client once but resolve the current index version per request, and invalidate the cached handle when the version changes.. Staleness comes from caching an index handle at import time, so the fix is to keep the cheap-to-reuse client but resolve the index version per request and invalidate the handle when it changes. This preserves the latency benefit of a persistent client while ensuring answers reflect the latest index, avoiding both constant rebuilds and redeploy-on-every-update.

Answer

Increase the app's compute size so the cached handle is refreshed more frequently by the runtime.

Answer

Disable caching entirely and rebuild the endpoint client and index handle on every request.

Answer

Redeploy the app after every index update so the module reinitializes with a fresh handle.

Question 13

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.)

Accepted Answer

Configure the endpoint with multiple served entities and use traffic splitting to gradually shift traffic to the new model version.. Defining endpoints as code with Databricks Asset Bundles enables repeatable deployments and fast rollback by redeploying a prior version. Configuring multiple served entities with traffic splitting allows gradual rollout to the new version, so issues can be detected early and traffic reverted quickly, together providing safe updates and rollback.

Answer

Disable inference tables on the endpoint to reduce logging overhead during the update.

Answer

Deploy the new version by deleting the existing endpoint and creating a fresh one with the same name.

Answer

Set the endpoint's scale-to-zero behavior to always on to avoid cold starts during the update.

Question 14

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?

Accepted Answer

`databricks bundle plan -t prod`. The `databricks bundle plan` command produces a deployment plan that lists the resources the deployment would create, update, or delete in the target workspace, without applying any change. Running it with `-t prod` previews the production deployment. This lets the team review the impact before executing `databricks bundle deploy`, which is the recommended safe release workflow.

Answer

`databricks bundle deploy -t prod --dry-run`

Answer

`databricks bundle validate -t prod`

Answer

`databricks bundle summary -t prod`

Question 15

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?

Accepted Answer

Reference the secret scope and key in the app configuration's environment variables using the secrets-backed value syntax, then read it from the environment in code.. The app configuration can declare environment variables whose values are resolved from a Databricks secret scope using the secrets-backed reference syntax. The platform injects the resolved value at runtime, so the key is available to the process without ever being written into source, committed to a repository, or surfaced through deployment output.

Answer

Pass the key as a bundle variable in databricks.yml so it is substituted during deployment.

Answer

Commit the API key into a .env file in the app repository and load it with a dotenv library at startup.

Answer

Store the key in a Delta table and have the app query the table with its service principal at startup.