ML Ops

A data science team is transitioning from manual model training to automated pipelines in Databricks. They require a mechanism to track model lineage, versions, and stage transitions programmatically. Which Databricks component best satisfies this requirement?

An organization needs to implement a robust CI/CD strategy for their Databricks ML models. Which TWO of the following practices are recommended to ensure reliable model deployment?

Refer to the exhibit. An MLOps engineer is reviewing a JSON object representing a model in the Databricks Model Registry. The engineer wants to promote this model to the 'Production' stage using the MLflow Python API. Which command is correct?

A team is building a feature store in Databricks. They need to ensure that training data and inference data are consistent to avoid training-serving skew. What is the primary benefit of using the Databricks Feature Store in this context?

When deploying a model as a real-time REST API endpoint on Databricks, which service should be used to manage the serving infrastructure, scaling, and availability?

Which THREE of the following are primary responsibilities of an MLOps engineer when maintaining production ML models in Databricks?

What is the primary advantage of using a Model-as-Code approach in Databricks for machine learning deployments?

Which of the following describes the purpose of a 'Gold' table in the Medallion architecture within an MLOps pipeline?

Refer to the exhibit. An engineer is configuring a serving endpoint. Based on the configuration provided, what is the impact of the 'auto_scale' flag?

When auditing an ML pipeline in Databricks for compliance and governance, which THREE of the following should be verified?

A machine learning engineer needs to track model experiments and ensure that all training parameters and metrics are captured in a reproducible way. What is the best practice for using MLflow within Databricks notebooks?

Your organization requires that all models deployed to production undergo a drift detection check. Which approach is most effective for monitoring model performance in Databricks?

You are managing a Databricks environment and need to ensure that ML models are reproducible across different workspaces. Which strategy is most effective for cross-workspace model promotion?

Your company uses MLflow tracking. You want to query all experiments that achieved a specific accuracy threshold across multiple teams. What is the most efficient way to achieve this?

When deploying a model to a Databricks Model Serving endpoint, how can you ensure the model scales automatically based on traffic demand?

A team is transitioning from manual experimentation to a formal MLOps pipeline. Which component should be prioritized to ensure that data used during training is reproducible?

Which TWO statements regarding the use of Unity Catalog in Databricks for MLOps are correct?

You are designing a model retraining strategy. What is the most reliable way to trigger a retraining job based on model performance degradation?

Refer to the exhibit. The deployment pipeline is failing to load the model artifact in the target production environment. What is the most likely cause?

Which component of Databricks is specifically designed to prevent training-serving skew by ensuring that feature engineering code is consistent during both model training and real-time inference?

You are implementing a CI/CD pipeline for a model. You want to automate unit testing of the model's inference performance. Which approach is best suited for Databricks?

Refer to the exhibit. If the 'train_model' task fails, what happens to the 'evaluate_model' task in this Databricks Job?

Your organization wants to monitor production models for drift. Which Databricks service should be used to detect changes in the input data distribution compared to the training data?

Which action should be taken to ensure that sensitive PII data is not leaked when logging models using MLflow?

In the context of Databricks MLOps, what is the primary purpose of a 'Staging' environment in the Model Registry?

When deploying a model to a production environment, why is it recommended to use a 'Model Signature'?

Which TWO of the following are primary benefits of using the MLflow Model Registry in Databricks?

Which of the following describes the 'Gold' layer in the Medallion Architecture, and why is it important for machine learning?

Refer to the exhibit. A data scientist attempted to register a new model version but received the error shown in the exhibit. Which step should be taken to resolve this issue?

Which feature in Databricks allows you to automatically track training code, parameters, metrics, and models during the development phase?

Your organization requires that all models deployed to production must be signed by a security officer. How can you enforce this requirement within the Databricks MLflow Model Registry?

You are migrating a legacy ML pipeline to Databricks. You need to ensure that the feature engineering logic used during training is identical to the logic used during real-time inference. What is the recommended approach?

Refer to the exhibit. Why is the missing model signature considered an MLOps risk in a production environment?

Which component of MLflow is responsible for keeping track of the different versions of a model as it moves from development to testing and production?

When deploying a model as a real-time REST endpoint on Databricks, how can you ensure the infrastructure scales automatically to handle increased request traffic?

Your team uses Databricks for machine learning and needs to ensure that model training is fully automated and reproducible. Which THREE of the following are necessary components for a production-grade automated ML pipeline?

You are designing a strategy for monitoring model performance after deployment. Which of the following is the most important indicator that a model requires retraining?

Your team is migrating models to Databricks Model Registry. You need to automate the transition of a model version to 'Staging' only after it passes an automated integration test suite in the CI/CD pipeline. Which mechanism should you use to best achieve this?

Refer to the exhibit. You are managing the 'revenue_forecast' model in the registry. A colleague wants to deploy this version to production. Which Databricks command or process is required to move version 4 to the 'Production' stage while ensuring existing production models remain unaffected?

Your organization is implementing an MLOps strategy that requires strict model governance. You need to ensure that no model is deployed to production unless it has been tagged with 'validated=true' in the MLflow Model Registry. How can you enforce this policy within your CI/CD workflow?

You are debugging a model serving issue where the model is failing in production. Which THREE of the following actions should you prioritize to identify the root cause of the failure?

Which component of Databricks ML is best suited for tracking model hyperparameters, metrics, and code versions during the experimentation phase of the ML lifecycle?

Your organization needs to automate the deployment of models to real-time serving endpoints. Which service within the Databricks ecosystem handles the hosting and scaling of these endpoints with managed containerization?

You are implementing model monitoring to detect data drift. Which Databricks feature should you use to automatically track and alert on changes in the distribution of input data features over time?

When designing a robust MLOps pipeline for high-stakes financial applications, why should you prioritize 'reproducibility' over 'speed' during the deployment phase?

Which THREE of the following are key responsibilities of an MLOps engineer when managing a model lifecycle on Databricks?

Your team is migrating models to Databricks Model Registry. You need to ensure that production models are only deployed after passing specific validation tests. Which feature best facilitates this automated governance?

You are auditing a Databricks environment to ensure compliance. Which TWO actions ensure the highest level of model lineage and reproducibility for models registered in MLflow?

Your organization requires a feature store strategy that supports low-latency point-in-time lookups for online inference. Which implementation approach best addresses this?

Which component of MLflow tracks the parameters, metrics, and tags associated with a specific training run?

Your ML pipeline requires a complex environment with specific C++ dependencies. What is the recommended way to manage this in Databricks?

Refer to the exhibit. Which security configuration is the likely culprit for this job failure?

A team wants to track model performance over time to detect drift without writing custom monitoring infrastructure. What is the most efficient Databricks tool for this?

When deploying a model to a production endpoint, your team requires that the model is served from a specific, immutable version. How do you ensure this in MLflow?

Which Databricks feature is specifically designed to prevent data leakage during model training by ensuring feature values are fetched as they existed at a specific point in time?

In an automated MLOps workflow, what is the best practice for handling model training failures?

Your team uses Databricks Model Serving to host a production model. You need to ensure zero-downtime updates while maintaining the ability to revert to the previous version instantly if performance degrades. Which deployment strategy should you implement?

Which TWO actions are required to properly implement MLflow Model Registry stages and governance for a machine learning project?

Refer to the exhibit. Your automated CI/CD pipeline triggered a model deployment to production, but the job failed with the error shown. What is the most likely cause?

When implementing a Feature Store in Databricks, what is the primary benefit of using a Feature Table compared to a standard Delta table for feature engineering?

Your team is experiencing 'training-serving skew' where model performance in production is significantly lower than during training. Which approach should you prioritize to mitigate this issue?

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?

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?

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?

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?

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?

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?

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?

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?

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.)

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?

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?

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?

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?

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.)

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?

A data science team trains a scikit-learn model on a Databricks cluster and needs the same feature-engineering logic to run identically in a nightly batch scoring job and in a real-time Model Serving endpoint. They want a single artifact that encapsulates preprocessing and the estimator. Which approach should they use?

You are preparing a feature-engineering job that reads from a Delta table and writes a feature table. The job must run on a schedule and be idempotent so that reruns after a failure do not duplicate data. Which approach should you use?

A team wants a scheduled Databricks job to automatically retrain a demand-forecasting model whenever the upstream feature table receives new data, and to register the resulting model version only if validation metrics improve. Which Databricks capability should they use to orchestrate this?

A data scientist has completed hyperparameter tuning with Hyperopt on Databricks and now needs to register the best-performing model to the MLflow Model Registry, including its signature and input example, so that downstream scoring jobs can validate incoming data. The training script uses MLflow autologging. Which approach most reliably captures the signature and input example during registration?

A data science team at a retail company has registered a demand forecasting model in the MLflow Model Registry. The model version is currently in the 'Staging' stage and has been validated by the QA team. Before promoting it to 'Production', the ML engineer wants to ensure that the model's performance does not degrade when serving live traffic. They decide to deploy the model to a small percentage of production traffic while continuing to serve the existing model. Which Databricks feature should they use to achieve this?

Your team is using Databricks Feature Store to serve features to a real-time model deployed on Databricks Model Serving. A data scientist updates the feature computation logic for one of the features and publishes a new version to the online store. However, the model endpoint continues to return stale feature values for that feature. What is the most likely cause?

You are building a Databricks job that trains a model and registers it to the MLflow Model Registry. After registration, you need to automatically transition the model version to 'Staging' only if its validation accuracy exceeds 0.95. The job should fail if the accuracy is below this threshold. Which approach should you implement?

A team runs a nightly Databricks job that retrains a demand-forecasting model and registers a new version in the MLflow Model Registry. Compliance requires that the model version used for scoring in production be immutably identified and that any subsequent retraining not silently change what production serves. Which practice best satisfies this requirement?

A machine learning engineer is using MLflow Tracking to log metrics and artifacts for a deep learning model. They notice that the training run logs a large number of metrics (e.g., loss per batch) and want to reduce the storage footprint and improve query performance. They also need to retain the ability to compare runs and reproduce results. Which of the following actions is most appropriate?

A data scientist has registered a new model version in MLflow Model Registry and wants to validate it against a golden dataset before promoting it. The validation notebook must run automatically whenever a new version is registered in the 'ChurnModel' registry model, and the result should block promotion if the validation fails. Which approach should the data scientist use?

A machine learning engineer needs to automate retraining of a model whenever new data lands in a Delta table. The retraining must run on a schedule, use a specific cluster configuration, and send an email alert on failure. Which Databricks feature should be used to orchestrate this workflow?

You are implementing a CI/CD pipeline for a machine learning model on Databricks. The pipeline must automatically retrain the model when new data arrives, validate its performance, and promote it to production if it meets quality thresholds. Which TWO of the following steps are essential to include in the pipeline to ensure safe and automated deployment? (Choose two.)

You are monitoring a model served on a Databricks Model Serving endpoint. You need to track the distribution of incoming request payloads to detect data drift. Which Databricks feature should you use to automatically capture and store inference logs for analysis?

A machine learning engineer needs to schedule a Databricks job that runs a Python wheel task to execute a packaged training pipeline on a recurring basis. The pipeline code is built into a wheel and stored in Unity Catalog volumes. Which job configuration correctly executes this workload?

You maintain a Databricks ML pipeline that trains a model nightly and registers new versions in MLflow Model Registry. A downstream batch scoring job in another workspace loads the model by stage. Auditors require that every production scoring run can be traced back to the exact training data snapshot and code commit. Which approach best satisfies this requirement?

A financial institution is using Databricks to build and deploy a credit risk model. The model must comply with regulations that require full auditability of the model's lineage, including data sources, transformations, and training parameters. The team uses MLflow for tracking and the Feature Store for feature management. Which TWO of the following practices are essential to meet the auditability requirements? (Choose two.)

A fraud detection model was deployed to a Databricks Model Serving endpoint. Over the past week, the endpoint's p99 latency has increased from 45 ms to 320 ms, but the model's predictions remain accurate. The serving logs show that each request now includes a larger JSON payload with additional transaction metadata. What is the most likely cause of the degradation?

A machine learning engineer is using Databricks Model Serving to deploy a model that requires a custom Python library not available in the default environment. The engineer wants to ensure the endpoint uses the exact library version and that the deployment is reproducible. Which approach should the engineer take?

A data scientist has trained a model and logged it with MLflow. They now need to deploy it as a real-time endpoint on Databricks Model Serving. The model requires a custom Python library that is not available in the default environment. What is the correct way to ensure the library is available when serving the model?

A team is preparing to promote a new model version to production in the MLflow Model Registry. They must ensure the model can be served with a consistent environment across staging and production and that dependency drift is detected before promotion. Which TWO practices should they follow? (Choose two.)

Your team uses MLflow Model Registry. A model version currently in Production has a critical flaw and must be rolled back to a previous version. The previous version is in the Archived stage. What is the most operationally sound approach to restore service quickly while preserving the audit trail?

A fraud detection model is served on a Databricks Model Serving endpoint. You notice that predictions for the same input vector differ between two consecutive requests within seconds, and there is no feature store or external cache involved. The model was logged with a fixed random seed and deterministic inference code. Which action should you take first to diagnose the inconsistency?

A data scientist has developed a scikit-learn model and wants to deploy it as a REST API endpoint on Databricks Model Serving. They have logged the model with MLflow and registered it in the MLflow Model Registry. The model requires a specific Python library that is not pre-installed in the Databricks Model Serving environment. What should the data scientist do to ensure the library is available when the model is served?

You are designing a CI/CD pipeline for a machine learning model on Databricks. The pipeline must automatically retrain the model when new data arrives, validate it, and deploy it to a serving endpoint if it passes. Which two components are essential to achieve this? (Choose two.)

You are monitoring a production model deployed on Databricks Model Serving. You notice that the model's predictions have gradually become less accurate over time, likely due to data drift. You need to implement a solution that automatically detects drift and triggers retraining. Which Databricks feature should you use to monitor the model's input data and performance?

A team is using Databricks Feature Store to manage features for a real-time fraud detection model. They need to ensure that the features used during training are consistent with those served at inference time. Which two actions should they take to achieve this? (Choose two.)

An ML engineer is instrumenting a Databricks job that trains and evaluates several models. They want each model's metrics, parameters, and artifacts grouped so that a downstream automated promotion step can compare candidates within the same experiment. Which MLflow practice best supports programmatic comparison across runs in this job?

A data science team uses MLflow Tracking with a remote tracking server backed by a Databricks-hosted MySQL instance for the backend store and an Azure Data Lake Storage Gen2 path for artifacts. A model-training notebook writes metrics and a model artifact, then calls mlflow.register_model to promote the run into Unity Catalog. Reviewers report that the run's metrics appear in the experiment UI, but the model version in the registry cannot be loaded by the deployment job. The deployment job fails when it tries to download the artifact. Which action most directly resolves the deployment failure?

You are responsible for deploying a machine learning model to a Databricks Model Serving endpoint. The model must be updated frequently with new versions. You want to ensure that the endpoint remains available during updates and that traffic can be shifted gradually to the new version. Which deployment strategy should you use?

You are responsible for monitoring a critical model deployed to Databricks Model Serving. You need to detect data drift and model performance degradation. Which TWO of the following actions should you take? (Choose two.)

You are monitoring a model served on Databricks Model Serving. You need to detect data drift in the incoming requests without delaying predictions. Which approach should you use?

A machine learning team is using MLflow Model Registry to manage a model that is deployed to a production endpoint. They need to implement a CI/CD pipeline that automatically transitions a model version from 'Staging' to 'Production' only after it passes a set of validation tests. Which MLflow feature allows them to trigger the transition based on test results?

A data scientist is using MLflow Tracking to log experiments for a model that predicts customer lifetime value. They want to compare runs across multiple experiments and identify the best performing run based on a custom metric called 'rmse'. Which MLflow feature should they use?

A fraud detection model is served via a Databricks Model Serving endpoint. The team wants to capture the incoming request payloads and the model's predictions to a Delta table for monitoring and future retraining. Which approach is most appropriate?

A machine learning engineer is responsible for monitoring a production model deployed to Databricks Model Serving. The model predicts customer churn and is served via a REST endpoint. The engineer needs to detect data drift and model performance degradation over time. Which TWO actions should the engineer take to enable effective monitoring? (Choose two.)

You are implementing a CI/CD pipeline for a machine learning model on Databricks. The pipeline must automatically run unit tests, train the model, and deploy it to a staging endpoint. Which TWO practices should you follow to ensure the pipeline is reproducible and reliable? (Choose two.)

An ML engineering team maintains a feature table in Databricks Feature Store that is populated by a nightly batch job. The same features feed both an offline training pipeline and a Model Serving endpoint. After a schema change adds two columns to the source Delta table, the endpoint begins returning errors during online lookup. The team confirms the offline training pipeline still works. Which change most likely restores the endpoint?

You need to ensure that a model deployed to a Databricks Model Serving endpoint can be rolled back quickly if it starts performing poorly. Which feature should you use?

A data scientist wants to automate the retraining of a model whenever new data arrives in a Delta table. They need to orchestrate a multi-step workflow that includes data validation, feature engineering, model training, and deployment. Which Databricks feature should they use?

A machine learning engineer is setting up a CI/CD pipeline for a model deployed to Databricks Model Serving. The pipeline must automatically update the serving endpoint when a new model version is registered in the MLflow Model Registry and passes a validation job. Which Databricks feature should the engineer use to trigger the update?

A team is using Databricks Feature Store to manage features for a real-time model served via Databricks Model Serving. They need to ensure that the online feature values used at inference time are consistent with the training data. Which TWO practices should they implement? (Choose two.)

A team runs a weekly retraining job that produces a new model version in Unity Catalog. Their production endpoint is currently serving version 4. They want to promote version 5 with zero downtime and the ability to roll back instantly if error rates rise. Which approach best meets these requirements?

You are building a CI/CD pipeline that must promote an MLflow model version from Staging to Production in Databricks only after automated validation. The pipeline runs in a service principal context. Which two actions are required to implement this safely and repeatably? (Choose two.)

Your team uses MLflow Projects to package training code. A colleague runs the project on a Databricks cluster and it fails with a dependency conflict because the cluster has an older version of a library than the project's conda environment specifies. What is the most reliable way to ensure the project uses its declared dependencies without modifying the shared cluster?

A machine learning engineer is using MLflow to track experiments and has logged a model with a signature. They now want to register this model in the MLflow Model Registry and promote it to Production. Which MLflow API call should they use to add the model to the registry?

You are implementing a CI/CD pipeline for ML models on Databricks. The pipeline must automatically retrain, validate, and promote models to Production in MLflow Model Registry. Which TWO practices are essential for maintaining reproducibility and governance in this automated workflow? (Choose two.)

A regulated financial services firm must prove that every model promoted to production on Databricks is traceable and governed. They use Unity Catalog for models and MLflow for experiment tracking. Which two practices most directly satisfy an auditor's requirement to trace a production model version back to its training data and code? (Choose two.)

A team uses MLflow Projects to package training code and runs jobs on Databricks clusters. They want to ensure that a job run today can be reproduced six months later with the same library versions, even if the cluster's base image and PyPI packages have changed. Which practice best achieves this?

A company uses Databricks Model Serving to host a real-time model. They need to perform A/B testing between two model versions, sending 10% of traffic to a new version and 90% to the current version. Which approach should they use?

A machine learning engineer is setting up a Databricks job to retrain a model every night. The job must use a specific Python library version that is not available in the default Databricks Runtime. The engineer wants to ensure that the retraining job has access to this library without affecting other jobs in the workspace. What is the recommended approach?

A data scientist wants to compare multiple runs within the same MLflow experiment to identify the best-performing model based on a custom metric. What is the most efficient way to do this in the MLflow UI?

A team notices that a deployed Model Serving endpoint occasionally returns stale predictions for a subset of customers shortly after the nightly feature refresh completes. The offline feature table is confirmed current. Which is the most likely explanation?

A financial institution has deployed a credit risk model to a Databricks Model Serving endpoint. The model was trained on data that includes sensitive customer attributes. The compliance team requires that all predictions be explainable and that the model's decisions can be audited. The data science team wants to use SHAP (SHapley Additive exPlanations) to generate explanations for each prediction. Which approach should they take to integrate SHAP with the serving endpoint while maintaining low latency?

A team has a production Databricks Model Serving endpoint for a churn model. They retrain weekly and register new model versions in Unity Catalog. They want the endpoint to automatically pick up the newest registered version without manual intervention, while keeping the previous version available for instant rollback. Which approach should they implement?