Databricks-ML-Assoc Model Deployment Practice Test 4 — 15 Questions

Question 1

A machine learning engineer is preparing to deploy a registered model to a Databricks Model Serving endpoint. Before creating the endpoint, the engineer wants to confirm the deployment prerequisites are satisfied. Which two conditions are required for a successful endpoint creation? (Choose two.)

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

Accepted Answer

The model version has a logged MLflow model artifact with a valid model signature. A successful endpoint deployment depends on a loadable MLflow model artifact with a defined signature and a reconstructable dependency environment, because the service must build a container and validate request schemas. Registry stage, attached clusters, and name matching are not technical prerequisites and do not affect whether the endpoint can be created and brought to a ready state.

Answer

The model version is transitioned to the Production stage in the Model Registry

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The endpoint name matches the registered model name exactly

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A dedicated all-purpose cluster is running and attached to the endpoint

Question 2

A data scientist registers a scikit-learn model in the Unity Catalog model registry with the name `prod.ml_team.fraud_detector`. They now want to serve it with Databricks Model Serving. Which value should be supplied as the model identifier when creating the serving endpoint?

Accepted Answer

prod.ml_team.fraud_detector. Because the model was registered in Unity Catalog, the endpoint must be created from the three-level namespace catalog.schema.model_name, optionally pinned to a version. Stage-based URIs and raw storage or run paths do not identify a Unity Catalog registered model, so they cannot be used as the served entity. The fully qualified name is the correct identifier.

Answer

models:/prod.ml_team.fraud_detector/Production

Answer

dbfs:/databricks/mlflow/prod/ml_team/fraud_detector

Answer

runs:/<run_id>/model

Question 3

A machine learning engineer needs to capture every request and response payload sent to a Databricks Model Serving endpoint so that the team can later join predictions with ground-truth labels for monitoring. Which Databricks feature should they enable on the endpoint?

Accepted Answer

Inference tables. Inference tables are the Databricks Model Serving feature that persists request and response payloads to a Delta table. Enabling them allows teams to join predictions with ground-truth labels over time, monitor drift, and debug production behavior. Signature enforcement, MLflow artifact logging, and audit logs serve different purposes and do not capture payloads.

Answer

Model signature enforcement

Answer

Endpoint access logs in the workspace audit log

Answer

MLflow model logging to the run's artifact store

Question 4

A machine learning engineer has registered a scikit-learn model in Unity Catalog as `prod.ml.risk_model` with version 3. They want to serve it through a Databricks Model Serving endpoint that always uses this exact model version, even after new versions are registered. Which endpoint configuration should they use?

Accepted Answer

Set the served entity to `prod.ml.risk_model` with version 3 and do not configure aliases or the `latest` version.. Pinning a Model Serving endpoint to a specific registered model version is done by supplying the model name and version number in the served entity configuration. This produces an immutable reference that does not change when new versions are registered. Relying on aliases or `latest` introduces mutability, which conflicts with the requirement to always serve version 3.

Answer

Set the served entity to `prod.ml.risk_model` and set the version to `latest` so that the endpoint always uses the most recent registered version.

Answer

Set the served entity to `prod.ml.risk_model` with version 3 and enable scale-to-zero.

Answer

Set the served entity to `prod.ml.risk_model` and rely on the `@champion` alias to always resolve to version 3.

Question 5

A machine learning engineer is deploying an MLflow model to a Databricks Model Serving endpoint. The model was trained on a Spark DataFrame and logged with MLflow using the default signature detection. During testing, the endpoint returns predictions, but the engineer notices the input schema shown in the Serving UI does not match the actual DataFrame column types used during training. Which MLflow logging step should the engineer verify first to resolve this schema mismatch?

Accepted Answer

The model was logged with `mlflow.pyfunc.log_model` without explicitly passing the `signature` argument, so MLflow inferred it from a sample that may not represent the full schema.. The logged MLflow model signature is the authoritative contract for the Serving endpoint's expected input columns and types. When a signature is missing or inferred from an unrepresentative sample, the endpoint may expose an incorrect schema. Explicitly passing a `ModelSignature` during `log_model` ensures column names and data types align with training data, resolving the mismatch before redeployment.

Answer

The model's conda environment file lists incompatible library versions, so the Serving endpoint silently coerces input types to match the environment.

Answer

The Serving endpoint was configured with `min_instances` greater than zero, which forces the endpoint to rewrite the schema for autoscaling compatibility.

Answer

The model was registered in the Model Registry under a different name than the one used in the Serving endpoint configuration, causing the endpoint to load a stale schema.

Question 6

A machine learning engineer is deploying a model to a Databricks Model Serving endpoint and needs to send feature values that are computed by a separate upstream pipeline. The engineer wants the endpoint to accept a JSON payload describing a single record with named fields. Which approach correctly describes how the client should format the request?

Accepted Answer

Send a JSON body with a dataframe_records or dataframe_split field containing the named columns matching the model signature.. For models logged with a pandas DataFrame signature, Databricks Model Serving accepts JSON using dataframe_records or dataframe_split, where each record is an object of column names to values. This preserves the named-column contract and allows signature validation. Positional array formats and non-JSON encodings do not match the DataFrame interface and would either fail validation or misalign features.

Answer

Send a JSON body containing a base64-encoded serialized pandas DataFrame along with a pickle content type.

Answer

Send the record as form-encoded key-value pairs in the request body with a content type of application/x-www-form-urlencoded.

Answer

Send a JSON body with an inputs field containing a nested array of numeric values in the exact column order of training.

Question 7

A team owns a Databricks Model Serving endpoint that receives sporadic bursts of traffic. They want to reduce cold-start latency during bursts while keeping cost predictable, and they also need to capture the request payloads and predictions for later monitoring. Which two configuration choices should they make? (Choose two.)

Accepted Answer

Set min_instances to a value greater than zero so a warm replica is always available.. Cold-start latency during sporadic bursts is best mitigated by keeping at least one replica warm with min_instances greater than zero, avoiding scale-to-zero teardown. Capturing request payloads and predictions for monitoring is done with inference tables, which write each request and response to a Delta table. Together these meet both the latency and the observability goals without conflicting.

Answer

Set scale_to_zero_enabled to true on the served entity to lower idle cost.

Answer

Increase the endpoint's workload size from Small to Large.

Answer

Attach an MLflow experiment to the endpoint to record prediction requests.

Question 8

A machine learning engineer has an existing Databricks Model Serving endpoint named churn-endpoint serving version 3 of a model. The team has validated version 5 and wants to direct live traffic to it while keeping the deployment reversible if quality degrades. What is the most appropriate action?

Accepted Answer

Update the served entity in the endpoint configuration to reference model version 5, allowing rollback by re-pointing to version 3. The endpoint serves whichever model version URI its configuration references, so updating the served entity to version 5 redirects live traffic through a managed rollout and keeps rollback simple by re-pointing to version 3. Deleting and recreating, batch overwrites, and registry stage changes do not achieve reversible live traffic redirection.

Answer

Delete the endpoint and create a new one with a different name that serves model version 5

Answer

Transition model version 5 to Production in the Model Registry and assume the endpoint automatically follows the stage change

Answer

Keep the endpoint on version 3 and write a scheduled job that calls version 5 and overwrites predictions in the serving table

Question 9

A data scientist has registered a scikit-learn model in Unity Catalog as `ml_prod.churn.model_v3` and wants the Databricks Model Serving endpoint to automatically pick up newly registered model versions as they are promoted to the `champion` alias. Which configuration should the data scientist use when creating the serving endpoint?

Accepted Answer

Serve the model using the alias-qualified path `ml_prod.churn.model_v3@champion`.. Alias-qualified paths in Unity Catalog let a serving endpoint follow a movable pointer. When a new model version is registered and the `champion` alias is reassigned to it, the endpoint resolves the alias and begins serving the new artifact. This enables zero-touch promotion workflows, keeps governance in Unity Catalog, and avoids endpoint recreation or explicit version pinning.

Answer

Export the model with `mlflow.sklearn.save_model` to a DBFS path and point the endpoint at that directory.

Answer

Serve the model by its explicit version number, `ml_prod.churn.model_v3/3`, and re-create the endpoint after each promotion.

Answer

Serve the model by its registered name only, `ml_prod.churn.model_v3`, and rely on Databricks to always use the latest version.

Question 10

A team has just registered a new version of a fraud detection model in the workspace Model Registry. Before routing production traffic to it, they want to send a small percentage of live requests to the new version and compare its predictions against the current production model. Which Databricks Model Serving feature should they use?

Accepted Answer

Traffic splitting between served entities. Traffic splitting on a Databricks Model Serving endpoint allows multiple served entities, each pointing to a different model version, to share incoming requests by percentage. This makes it possible to route a small fraction of live traffic to a new version and compare its behavior against the existing production model before promoting it fully. Other listed features address logging, capacity, or naming, not request routing.

Answer

Inference tables

Answer

Provisioned throughput

Answer

Model aliases

Question 11

A fraud detection team wants their Databricks Model Serving endpoint to log every request and response payload to a Unity Catalog Delta table so analysts can later join predictions with ground-truth labels. Which endpoint capability should they enable?

Accepted Answer

Inference tables, configured with the endpoint's request and response logging.. Inference tables are the supported way to persist per-request payloads and responses from a Model Serving endpoint into a governed Delta table. Because the table lives in Unity Catalog, downstream jobs can join captured predictions with delayed fraud labels to measure real-world performance. This closes the loop between online scoring and offline evaluation without custom logging code in the model.

Answer

Delta Live Tables pipelines attached to the endpoint's event stream.

Answer

Model monitoring with a Lakehouse Monitor created on the endpoint's metrics.

Answer

Verbose cluster logs enabled on the underlying serving compute.

Question 12

A team has registered a model in Unity Catalog and wants to serve it with Databricks Model Serving. Their security policy requires that the endpoint access the model without embedding long-lived credentials, and they want the endpoint to use a dedicated service principal for accessing downstream feature tables. Which configuration should they apply to meet these requirements?

Accepted Answer

Grant the endpoint's service principal access to the model and feature tables via Unity Catalog privileges, and configure the endpoint to use that service principal.. Unity Catalog allows Databricks Model Serving endpoints to run under a service principal identity, with access governed by Unity Catalog privileges on models and tables. Granting the service principal `EXECUTE` on the registered model and `SELECT` on feature tables enables secure, credential-free access. This design avoids embedding long-lived tokens and provides auditable, least-privilege access for production inference workloads.

Answer

Mount the feature tables as a DBFS path and configure the model to read them using a cluster-scoped credential passthrough token.

Answer

Enable token-based authentication on the endpoint and pass the token in each inference request header from the calling application.

Answer

Configure the Serving endpoint with a personal access token stored in the endpoint's environment variables.

Question 13

A team has several model versions registered in Unity Catalog. They want to serve a specific version through a Databricks Model Serving endpoint and later promote a newer version without changing the endpoint URL used by applications. Which approach should they use?

Accepted Answer

Create the endpoint pointing to a Unity Catalog alias such as 'champion', and update the alias to reference the new model version when promoting.. Databricks Model Serving endpoints can reference a Unity Catalog model alias instead of a fixed version. Applications call a stable endpoint URL, and the team promotes new versions by moving the alias, such as from 'champion' to a newer version. This preserves the URL, avoids endpoint recreation, and keeps promotion governed through alias updates rather than client-side changes.

Answer

Create the endpoint pointing to a fixed model version, then delete and recreate the endpoint with the new version when promoting.

Answer

Create the endpoint pointing to the model name without a version, and rely on the platform to always serve the most recently created version.

Answer

Create one endpoint per model version and place a load balancer in front that routes to the newest endpoint.

Question 14

A team has a Databricks Model Serving endpoint configured with scale-to-zero enabled and min_instances set to 0. During a load test, they observe that the first request after an idle period takes roughly 40 seconds while subsequent requests complete in under 200 milliseconds. They need to eliminate this cold-start latency for a customer-facing application without over-provisioning. Which configuration change best addresses the requirement?

Accepted Answer

Set min_instances to 1 so at least one instance stays warm.. Cold-start latency occurs because a scaled-to-zero endpoint must provision a container and load the model before serving the first request. Keeping one replica always available by setting min_instances to 1 removes that provisioning and loading delay while still allowing the endpoint to scale up under load. Increasing max_instances, changing workload size, or enabling routing features does not prevent the endpoint from scaling to zero during idle periods.

Answer

Increase max_instances so more replicas can absorb the burst.

Answer

Increase the workload size from Small to Large.

Answer

Enable route optimization in the endpoint configuration.

Question 15

A data scientist has a custom Python model wrapped in an MLflow pyfunc flavor and needs to serve it on Databricks Model Serving. The model's preprocessing requires a library that is not part of the default serving environment. What is the correct way to make that dependency available to the endpoint?

Accepted Answer

Log the model with the required library listed in the model's conda environment or pip requirements so it is captured in the model artifact.. Model Serving reconstructs the runtime environment from the dependency specification stored with the logged model. To bring in a library beyond the default environment, the dependency must be captured in the model's conda environment or pip requirements when the model is logged, which guarantees the endpoint installs it reproducibly. Cluster-level installs and storage paths do not reach the serving container.

Answer

Upload the library wheel to DBFS and reference its path in the endpoint's environment variables.

Answer

Add the library as a cluster library on the all-purpose cluster where the model was trained.

Answer

Install the library interactively on the driver node before creating the endpoint.