Databricks-ML-Assoc Model Deployment Practice Test 5 — 15 Questions

Question 1

A data scientist registers a model in the Databricks Model Registry and wants to record the model's intended input and output schema so that a serving endpoint can validate incoming requests. Which action accomplishes this when logging the model?

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

Accepted Answer

Log the model with an inferred or explicit signature using MLflow. A model signature is part of the logged MLflow model artifact and is what the serving runtime uses to validate request payloads against expected input and output types. Registry stages, endpoint tags, and external documentation are metadata that the runtime does not interpret as a schema, so only logging the model with a signature achieves request validation.

Answer

Add the schema to the endpoint's tags in the serving configuration

Answer

Store the schema as a separate Delta table and reference it in the model's README

Answer

Set the model version's stage to Production in the Model Registry

Question 2

A data scientist registers an MLflow model whose `conda.yaml` lists several Python packages. When they create a Databricks Model Serving endpoint from this model, the deployment fails during environment build. Which action is most likely to resolve the failure while preserving the model's dependency requirements?

Accepted Answer

Add the missing or incompatible packages to the endpoint's environment by specifying them in the model's `requirements.txt` or adjusting the `conda.yaml` to versions available in the Databricks runtime.. Model Serving reconstructs the model's environment from the dependency files captured at logging time. When a required package is absent or a pinned version cannot be resolved, the build fails. Adjusting the `requirements.txt` or `conda.yaml` to include the necessary packages at versions compatible with the Databricks runtime lets the environment build succeed while keeping the dependencies the model genuinely needs.

Answer

Remove all pinned package versions from the `conda.yaml` so the environment builder can pick any compatible release.

Answer

Increase the endpoint's workload size to Large so the environment builder has more memory to resolve the dependency graph.

Answer

Convert the model to a different flavor, such as replacing scikit-learn with a PyTorch implementation, so the dependencies are no longer needed.

Question 3

A team observes that their Databricks Model Serving endpoint occasionally returns HTTP 429 responses during bursty traffic, even though the endpoint shows low average CPU utilization. They want to reduce these throttling errors without over-provisioning capacity. Which action is most appropriate?

Accepted Answer

Increase the endpoint's `max_instances` and tune the autoscaling concurrency target so replicas scale out faster during bursts.. Throttling at low average CPU points to concurrency saturation during short bursts. Allowing the endpoint to scale to more replicas and configuring autoscaling to react to concurrency sooner lets the endpoint absorb spikes before requests are rejected. This addresses the root cause without permanently over-provisioning capacity during steady-state periods.

Answer

Switch the endpoint to a smaller workload size so individual replicas handle requests faster.

Answer

Enable scale-to-zero and rely on autoscaling to handle bursts.

Answer

Increase the endpoint's `min_instances` so that more replicas are always warm.

Question 4

A team registered a model in Unity Catalog and wants to serve it on a Databricks Model Serving endpoint. During deployment, the build fails because the model's conda environment references a private Python package hosted on an internal PyPI mirror that the serving build cannot reach. Which approach resolves the deployment?

Accepted Answer

Convert the private package into a wheel, log it as a model artifact, and reference it in the model's conda environment via a pip requirement pointing to the artifact path.. The robust fix is to make the model environment self-contained by logging the private wheel as part of the model artifacts and pointing the conda pip section at that relative path. The serving build then installs the dependency from artifacts it already has, with no need to reach an internal index. This preserves reproducibility and avoids exposing private infrastructure to the serving build.

Answer

Add the internal PyPI mirror URL as an environment variable on the serving endpoint configuration.

Answer

Increase the endpoint's workload size so the build has more memory for dependency resolution.

Answer

Remove the private package from the conda environment and rely on the package being preinstalled in the serving base image.

Question 5

A machine learning engineer is preparing to deploy a scikit-learn model as a Databricks Model Serving endpoint. The model expects a pandas DataFrame with specific column names and types. Which two actions should the engineer take to ensure the endpoint correctly validates and processes inference requests? (Choose two.)

Accepted Answer

Provide a representative `input_example` when logging the model so MLflow can infer and store the input schema.. Logging the model with an explicit `ModelSignature` and a representative `input_example` ensures the Serving endpoint knows the exact column names and types to expect. The signature drives request validation, while the input example documents and helps infer the schema. Together they prevent malformed requests from reaching the model and keep training and serving schemas aligned.

Answer

Convert the model to the `mlflow.pyfunc` flavor using a custom wrapper that hardcodes the expected column order.

Answer

Set the endpoint's `workload_size` to Large so the endpoint can coerce incoming data types automatically.

Answer

Enable inference tables on the endpoint so that incoming requests are automatically reformatted to match the model's schema.

Question 6

A team is preparing to deploy an MLflow model to a Databricks Model Serving endpoint and wants to diagnose why requests are failing before contacting support. Which two actions allow them to inspect the endpoint's behavior and errors? (Choose two.)

Accepted Answer

Query the endpoint's event logs and metrics in the Databricks workspace to review build and update events.. Inference tables capture the actual request and response payloads, exposing malformed inputs and model-side errors, while endpoint event logs and metrics show deployment, scaling, and update events. Together they cover both request-level and infrastructure-level diagnosis. Attaching clusters, SSH access, and redeploying do not provide supported visibility into a managed serving endpoint's runtime.

Answer

Re-register the model with a new signature and redeploy the endpoint to force the error to surface in the UI.

Answer

SSH into the serving container and read the application log files directly from the filesystem.

Answer

Attach a notebook to the serving cluster and run the model's predict method interactively to reproduce the error.

Question 7

A team queries a Databricks Model Serving endpoint through the serving client and receives an error indicating the endpoint is not ready. They confirmed the endpoint exists. Which condition most directly explains why requests fail until it clears?

Accepted Answer

The endpoint is in a state such as Updating or Not Ready while its configuration is being applied or a replica is still building.. When an endpoint is being created or its configuration is being applied, it passes through transitional states and cannot serve requests until at least one replica is ready. A not-ready error for an endpoint known to exist points to that provisioning or updating state, which resolves on its own once the deployment completes and the endpoint reports Ready.

Answer

The caller's personal access token has expired.

Answer

The model version referenced by the endpoint has been deleted from the registry.

Answer

The serving client is using the wrong workspace URL for the endpoint.

Question 8

An ML engineer needs to give an external application a stable HTTPS URL to call a registered model served by Databricks Model Serving. The application must authenticate with a token and must not be able to modify the endpoint configuration. Which approach best meets these requirements?

Accepted Answer

Grant the external application a service principal with a token and assign it only the permissions required to query the serving endpoint.. Model Serving endpoints are invoked over a stable HTTPS URL using bearer-token authentication. Issuing the external application a service principal token scoped to query the endpoint satisfies the authentication requirement while preventing configuration changes, since the principal lacks management permissions. Sharing an admin token over-privileges the client, and the remaining choices either bypass the endpoint or remove authentication entirely.

Answer

Expose the endpoint through a public network with no authentication so the external application can call it directly with the URL alone.

Answer

Embed the model's artifacts in the external application and call the model locally instead of using the serving endpoint.

Answer

Share the serving endpoint's URL and provide the external application with a Databricks personal access token belonging to a workspace admin.

Question 9

A data scientist has trained a model and wants other team members to serve it through a Databricks Model Serving endpoint. The model must be discoverable by a three-level namespace and governed by Unity Catalog. What should the data scientist do first?

Accepted Answer

Log the model with MLflow and register it to a Unity Catalog model using `mlflow.register_model` with a three-level name.. Serving a model through Databricks Model Serving requires a registered model version. Registering to Unity Catalog with a three-level name provides governance and discoverability, and the endpoint then references that catalog, schema, and model name with a version. Exporting to DBFS, scheduling batch jobs, or using custom containers do not establish the Unity Catalog model entry required here.

Answer

Export the model to a DBFS path and point the serving endpoint at the DBFS file path.

Answer

Package the model as a Docker image and push it to a container registry for the endpoint to pull.

Answer

Create a Databricks job that runs the model's predict method on a schedule and writes results to a Delta table.

Question 10

A team is preparing to deploy a registered MLflow model to a Databricks Model Serving endpoint. They want to capture every request and response for later monitoring and debugging, and they also want the endpoint to remain available during a rolling model version update. (Choose two.)

Accepted Answer

Enable inference tables on the serving endpoint.. Capturing every request and response is achieved by enabling inference tables, which log payloads to a Delta table. Keeping the endpoint available during a model version change is achieved by updating the served version in place, which triggers a rolling update rather than endpoint deletion. Together these satisfy both the observability and availability requirements without downtime or manual log plumbing.

Answer

Enable verbose logging in the MLflow model's Python code and write logs to DBFS.

Answer

Delete the existing endpoint and create a new one pointing at the new model version.

Answer

Set the endpoint's `min_instances` to 0 to reduce cost during the update.

Question 11

A data scientist has registered a scikit-learn model in Unity Catalog as `ml_team.prod.churn_model` and wants it served by a Databricks Model Serving endpoint that performs online inference for a web app. The endpoint must automatically pick up new model versions as they are promoted, without the data scientist editing the endpoint each time. Which approach should the data scientist use to configure the served entity?

Accepted Answer

Serve the model by name with the version set to the alias `champion`, so the endpoint resolves the alias to whatever version it currently points to.. Serving by model name with an alias makes the endpoint follow promotions automatically: when a new version is assigned the `champion` alias, subsequent requests resolve to that version. Pinning a numeric version, copying artifacts to DBFS, or polling with a job all require manual or scheduled intervention and cannot deliver the immediate, configuration-free update the scenario demands.

Answer

Create a Databricks job that runs every five minutes, downloads the latest model version, and calls the Model Serving REST API to overwrite the endpoint configuration with the new numeric version.

Answer

Serve the model by name with the version pinned to the numeric version that is currently in production, then re-create the endpoint whenever a new version is registered.

Answer

Export the MLflow model to a local directory, upload the artifacts to DBFS, and configure the endpoint to load the model from that DBFS path.

Question 12

A platform team is rolling out a new Databricks Model Serving endpoint for a churn model. They must ensure the endpoint can be queried by an external application and that only authorized callers can invoke it. Which TWO actions should they take? (Choose two.)

Accepted Answer

Grant the calling principal permission on the served model or endpoint and issue a Databricks personal access token or OAuth token for authentication.. Securing an endpoint for external use requires both an authenticated identity and a permission grant. A service principal with an OAuth token is the preferred machine identity, and granting it permission on the endpoint or served model enforces authorization. Using a personal access token for a human or service identity works similarly. Together these actions let the external application reach the endpoint while ensuring unauthorized callers are rejected.

Answer

Expose the endpoint only through a cluster-scoped init script that writes the scoring URL into the application's configuration.

Answer

Disable authentication on the endpoint so the external application can call the REST API without credentials.

Answer

Enable inference tables on the endpoint so that requests are logged and therefore implicitly authenticated.

Question 13

A data scientist deploys a model to a Databricks Model Serving endpoint and enables inference tables. After a week, they want to analyze prediction drift by joining the logged requests with ground-truth labels that arrive later. Which statement describes how they should access the inference table data for this analysis?

Accepted Answer

Query the inference table using the Delta table name shown in the endpoint's Serving UI, then join it with a labels table on a request identifier.. Inference tables persist request and response payloads as Delta tables, with a fully qualified name visible in the endpoint UI. Analysts can query them directly and join with delayed ground-truth labels using the included request ID and timestamps. This supports drift and accuracy analysis without custom logging. Lakehouse Monitoring can build on these tables for automated metrics.

Answer

Read the endpoint's driver logs from the cluster's log delivery location, parse the JSON entries, and join them with the labels table.

Answer

Call the endpoint's `/metrics` REST API to export inference records, then load them into a Spark DataFrame for joining with labels.

Answer

Enable model monitoring on the endpoint and rely on its automatically generated drift metrics, which replace the need to join with ground-truth labels.

Question 14

A team is standing up a real-time Databricks Model Serving endpoint for a fraud model. Requests will carry several numeric features, and the team wants the endpoint to reject malformed payloads with a clear client error rather than silently scoring them, and to avoid cold-start latency during business hours. Which two actions should the team take? (Choose two.)

Accepted Answer

Configure a non-zero minimum number of serving instances so replicas stay warm.. Declaring an MLflow model signature gives the endpoint a schema contract so invalid payloads are refused before scoring, and holding a minimum instance count above zero keeps replicas loaded so requests are not delayed by provisioning. Together these satisfy both the fail-fast validation goal and the warm-capacity latency goal, whereas scale-to-zero, inference tables and registry choice affect cost or governance only.

Answer

Set the endpoint's scale-to-zero behaviour so idle capacity is released between requests.

Answer

Register the model in the workspace Model Registry instead of Unity Catalog.

Answer

Enable inference tables to persist the request and response payloads.

Question 15

A team serves a model with Databricks Model Serving and wants to send production traffic to a newly registered model version while keeping the ability to revert instantly if quality degrades. They prefer not to edit the endpoint configuration to switch versions. Which approach best fits this requirement?

Accepted Answer

Create a model alias such as Champion in Unity Catalog, point the served entity at that alias, and move the alias between versions to promote or roll back.. Serving through a Unity Catalog alias decouples the endpoint configuration from the specific model version. Promotion and rollback become operations on the alias, so traffic shifts to the new version or back to the previous one without editing or redeploying the endpoint, which is exactly the instant-revert capability the team needs.

Answer

Update the endpoint to reference the new model version by number and rely on the previous version remaining in the registry.

Answer

Deploy a separate endpoint per model version and update the client to choose which endpoint to call.

Answer

Use the workspace model registry and transition the model version between Staging and Production stages to switch traffic.