Databricks-ML-Pro ML Ops Practice Test 8 — 15 Questions

Question 1

You are responsible for a fraud detection model deployed to a Databricks Model Serving endpoint. The model was trained on transaction data from the past 6 months. After two months in production, you notice a gradual decline in precision and recall. You suspect data drift. Which approach should you use to monitor feature drift for this endpoint?

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

Accepted Answer

Enable inference logging on the endpoint and use Databricks Lakehouse Monitoring to compare production feature distributions against the training baseline.. Inference logging captures the actual feature values sent to the model endpoint, and Lakehouse Monitoring provides built-in drift metrics by comparing these to a baseline profile. This combination enables automated, scalable drift detection without manual intervention. Other options either misrepresent platform features or fail to statistically compare distributions.

Answer

Set up a Databricks SQL dashboard that queries the endpoint's logs and calculates the average prediction value over time.

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Configure the endpoint to automatically retrain the model when drift is detected using the built-in auto-retrain feature.

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Use MLflow Tracking to log the model's predictions and manually compare them to ground truth labels in a notebook.

Question 2

You are retiring a real-time model endpoint on Databricks Model Serving. The endpoint has been serving production traffic for six months and you want to archive its request logs for compliance before deleting the endpoint. Which action should you take first?

Accepted Answer

Query the existing inference table that was configured for the endpoint and export its contents before deleting the endpoint.. Model Serving writes inference payloads to a Delta inference table when the feature is enabled, and that table survives endpoint deletion because it is a separate Unity Catalog object. Exporting the table before removing the endpoint preserves the historical request and response records required for compliance, whereas configuration snapshots or model artifacts do not contain traffic data.

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Download the endpoint's model artifact from the MLflow Model Registry and store it as the audit record.

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Use the Databricks REST API to list the endpoint's configuration and save the JSON response as the archive.

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Enable inference table logging on the endpoint and wait for the next inference requests to be persisted.

Question 3

A data scientist has registered a model in Unity Catalog under the name `prod.ml.forecast_model`. They now need to define a service-level objective (SLO) that automatically monitors the model's prediction quality in production, not just endpoint uptime. Which Databricks feature should they configure to detect model performance degradation?

Accepted Answer

Lakehouse Monitoring on the inference table. Lakehouse Monitoring on the inference table is the only option that provides statistical monitoring of model inputs and outputs. It computes drift and profile metrics and, when ground truth labels are available, model quality metrics such as accuracy or RMSE. This makes it the correct mechanism for an SLO tied to prediction performance rather than simple endpoint availability.

Answer

MLflow Model Registry webhooks

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Cluster autoscaling policies on the serving cluster

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Model Serving endpoint health checks

Question 4

Your team is using MLflow Model Registry to manage a model that predicts customer churn. A new version has been registered and passed validation. You need to transition this version to the 'Production' stage and ensure that all downstream scoring jobs automatically use it. What should you do?

Accepted Answer

Transition the model version to 'Production' and update the downstream jobs to reference the model by stage 'Production'.. Transitioning the model version to the 'Production' stage and having downstream jobs reference the model by stage 'Production' is the correct approach. This allows you to promote new versions to production without modifying job code, ensuring that all consumers automatically use the latest approved model. It also maintains a clear audit trail and governance.

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Keep the model version in 'Staging' and update the downstream jobs to reference the model by stage 'Staging'.

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Archive the current production model and register the new version with a new model name.

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Transition the model version to 'Production' and update the downstream jobs to reference the model by version number.

Question 5

A team wants to deploy a scikit-learn model to a real-time REST endpoint on Databricks. They have logged the model with MLflow and registered it in Unity Catalog. Which method should they use to create the serving endpoint?

Accepted Answer

Use the Databricks Model Serving UI or REST API to create an endpoint and select the registered model version.. Databricks Model Serving is the managed solution for deploying registered models as REST endpoints. It handles infrastructure, scaling, and dependency management automatically. Using the UI or REST API to create an endpoint from a Unity Catalog model version is the correct and supported approach, unlike manual containerization or local serving.

Answer

Package the model into a Docker image and deploy it to a Kubernetes cluster managed by Databricks.

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Write a Databricks job that loads the model and exposes it via a Flask app on a driver node.

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Use MLflow's `mlflow models serve` command on a Databricks cluster to start a local REST server.

Question 6

A data scientist has trained a scikit-learn model and logged it with MLflow. They now want to register this model in the MLflow Model Registry and transition it to 'Production' to be served via Databricks Model Serving. Which of the following is a prerequisite for registering the model?

Accepted Answer

The model must be logged using the MLflow Tracking API and have a valid run ID.. Registering a model in MLflow Model Registry requires that the model artifact is linked to an MLflow run. The run ID establishes the model's origin and enables versioning. Other options are either optional or incorrect; signatures, experiment associations, and admin approvals are not required for registration.

Answer

The model must be logged to an MLflow experiment that is associated with a registered model.

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The model must be logged with a signature.

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The model must be approved by a workspace admin.

Question 7

A fraud-detection model is registered in Unity Catalog and deployed to a Model Serving endpoint. Compliance requires that every prediction be traceable to the exact model version and the request that produced it. Which combination of Databricks features should you configure to meet this requirement?

Accepted Answer

Enable inference table logging on the endpoint and register the model version in Unity Catalog with a descriptive comment.. Traceability of individual predictions requires server-side capture of the request, response, and serving model version, which is what inference tables provide, combined with Unity Catalog's governed model version identity. Training-time logging, endpoint configuration snapshots, and client-side logs each capture only part of the picture and cannot reconstruct the full per-prediction audit trail.

Answer

Attach a model signature to the registered model and enable request logging in the serving client application.

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Configure the endpoint with a scale-to-zero policy and log the endpoint configuration to a Delta table daily.

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Enable MLflow autologging in the training notebook and set the model version stage to Production.

Question 8

You are using Databricks Feature Store to manage features for a model. A feature table is updated daily with new data. Your model training job reads from the feature table and logs the model with MLflow. To ensure that the model in production uses the correct feature values at inference time, what must you do when logging the model?

Accepted Answer

Log the model with the feature store lookup tables and specify the feature table names.. To ensure the model uses the correct feature values at inference time, you must log the model with the feature store lookup tables and specify the feature table names. This enables automatic feature retrieval, maintaining consistency between training and serving and ensuring that daily updates are reflected in predictions.

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Log the model with a static copy of the feature values used during training.

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Log the model with the feature table's primary key and timestamp columns only.

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Log the model without any feature store metadata and manually join the feature table in the scoring job.

Question 9

You are implementing a CI/CD pipeline for a Databricks ML project using Databricks Repos and Databricks Asset Bundles. You need to ensure that the pipeline promotes code and model artifacts across dev, staging, and prod workspaces consistently. Which TWO practices should you implement to achieve this? (Choose two.)

Accepted Answer

Register model versions to a shared Unity Catalog metastore that is accessible from all workspaces, using environment-specific model names or aliases.. Using Databricks Asset Bundles with target blocks and variables, and registering models to a shared Unity Catalog metastore with environment-specific names or aliases, together provide a consistent, governed promotion path. These practices externalize configuration and centralize artifact management, which are core to reliable CI/CD across multiple workspaces.

Answer

Store model artifacts in a separate cloud storage bucket outside Databricks and copy them manually between environments.

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Use the same Unity Catalog catalog name in all workspaces and rely on workspace-local paths for artifacts.

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Hardcode the production workspace URL and cluster ID in the notebook code to simplify deployment.

Question 10

A data scientist registers a new model version to Unity Catalog and wants to promote it to Production after validation. Which action accomplishes this in the current Databricks recommendation?

Accepted Answer

Assign the alias 'champion' to the validated model version using the MLflow client or Catalog Explorer.. In Unity Catalog, model promotion is done with aliases rather than the deprecated stages. Assigning an alias such as 'champion' to a validated version marks it as the one that serving endpoints and jobs should consume, and endpoints referencing that alias automatically serve the newly promoted version without changing their configuration.

Answer

Set the model version's stage to Production using the MLflow Model Registry stage transition API.

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Add a tag named 'stage' with the value 'Production' to the model version and restart the serving endpoint.

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Create a new registered model with the same name plus a '-prod' suffix and copy the artifacts into it.

Question 11

A data scientist at your company has trained a scikit-learn model and logged it with MLflow using the default 'sklearn' flavor. The model must now be deployed to a Databricks Model Serving endpoint for real-time inference, and the endpoint must automatically reload the new version whenever the model's stage changes to 'Production' in the MLflow Model Registry. Which deployment approach should the team use?

Accepted Answer

Register the model version in the MLflow Model Registry, then create the serving endpoint by specifying the model name and the 'Production' stage in the endpoint configuration.. The requirement is automatic reload when a model version enters the 'Production' stage. Databricks Model Serving supports this directly by referencing the registered model name and stage in the endpoint configuration. Other approaches either bypass the registry, require manual redeployment, or rely on mechanisms the platform does not provide, making them unsuitable for a managed real-time endpoint with stage-driven updates.

Answer

Log the model to a new MLflow experiment and configure the endpoint to poll the experiment's artifact location for changes.

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Package the model into a Docker image with a Flask app and deploy it to a Databricks cluster running as a job.

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Export the model artifact to DBFS as a pickle file and create a Python script that loads the pickle at endpoint startup.

Question 12

You are monitoring a model deployed to a Databricks Model Serving endpoint. You need to track the distribution of input features and model predictions over time to detect data drift. Which Databricks feature should you use?

Accepted Answer

Inference tables. Inference tables automatically log the input features and predictions for each request to a Model Serving endpoint, storing them in a Delta table. This allows you to monitor feature distributions and detect data drift over time using SQL or dashboards, making it the correct choice for this scenario.

Answer

Delta Live Tables

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MLflow tracking server logs

Answer

Databricks SQL dashboards

Question 13

You are using MLflow to track experiments for a computer vision model. After several runs, you notice that the training loss is not being logged correctly because the metric name contains a space. What is the recommended way to log metrics with names that include spaces?

Accepted Answer

Replace spaces with underscores or hyphens before logging.. MLflow enforces strict naming rules for metrics and parameters, disallowing spaces. The correct approach is to replace spaces with underscores or hyphens before logging. This maintains compatibility with the MLflow tracking server, UI, and API, and avoids logging errors. Other options either do not solve the problem or introduce unnecessary complexity.

Answer

Use the `mlflow.log_metric` function with the space included; MLflow will automatically sanitize it.

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Log the metric as a tag instead of a metric.

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Use a custom MLflow plugin to allow spaces in metric names.

Question 14

A fraud-detection model is served via a Databricks Model Serving endpoint. Compliance requires that every prediction request be traceable to the exact model artifact that produced it and that the model's inputs be auditable for drift analysis. Which two actions should you take to satisfy these requirements? (Choose two.)

Accepted Answer

Enable inference tables on the endpoint so requests and responses are automatically logged to a Delta table.. Inference tables persist endpoint request and response payloads to a governed Delta table, and enabling model version capture records which served model version produced each prediction. Together they provide the per-request audit trail and the artifact-level traceability the compliance scenario demands, without custom containers or manual tagging that cannot capture payloads.

Answer

Set the endpoint's scale-to-zero behavior to disabled so logs are never lost during cold starts.

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Configure the endpoint to use a custom container image that writes each request to an external S3 bucket.

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Tag the registered model version in Unity Catalog with the deployment timestamp and serving endpoint name.

Question 15

A nightly Databricks job trains a model, registers a new version in Unity Catalog, and then updates a Model Serving endpoint that serves the 'champion' alias. The endpoint must switch to the new version only after the job's validation step passes. Which approach correctly enforces this?

Accepted Answer

Assign the 'champion' alias to the new version only inside the validation-passed branch of the job, and leave the endpoint configured to serve the alias.. Decoupling promotion from deployment is the key: the endpoint always serves the 'champion' alias, and the job reassigns that alias only after validation succeeds. This makes the alias the single source of truth for what is in production and keeps rollback to a one-step alias change, while the validation branch ensures unvalidated versions never reach the endpoint.

Answer

Configure the endpoint with a scale-out policy that adds replicas whenever a new model version is registered.

Answer

Delete the previous model version after training so the endpoint is forced to pick up the newest registered version.

Answer

Have the job update the endpoint configuration to reference the new model version number directly after training completes.