Databricks-ML-Assoc ML Workflows — All Questions With Answers

Question 1mediummulti select

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Which TWO of the following statements regarding the MLflow Model Registry in Databricks are true?

Question 2hardmultiple choice

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Refer to the exhibit. You are logging a model to the MLflow Model Registry. What is the primary purpose of providing the model signature and input example shown in the exhibit?

Exhibit

{
  "model_signature": {
    "inputs": [{"name": "feature_a", "type": "double"}, {"name": "feature_b", "type": "double"}],
    "outputs": [{"name": "prediction", "type": "double"}]
  },
  "input_example": {
    "feature_a": [1.0, 2.0],
    "feature_b": [3.0, 4.0]
  }
}

Question 3hardmultiple choice

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When using the Databricks Feature Store, what is the primary benefit of logging the feature table metadata when training a model?

Question 4mediummultiple choice

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Your organization requires that all models deployed to production are signed off by a human reviewer. Which Databricks feature should be used to enforce this workflow?

Question 5mediummultiple choice

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Refer to the exhibit. A data scientist is setting up a Databricks Workflow task. Which statement accurately describes the function of the 'base_parameters' field?

Exhibit

{
  "task_key": "TrainModel",
  "notebook_task": {
    "notebook_path": "/Shared/Train",
    "base_parameters": {
      "learning_rate": "0.01",
      "epochs": "10"
    }
  },
  "job_cluster_key": "ML_Cluster"
}

Question 6hardmultiple choice

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When using the Databricks Feature Store, why is it critical to use the 'feature_lookup' feature when training a model?

Question 7hardmultiple choice

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Refer to the exhibit. Why is this error occurring in the MLflow Model Registry workflow?

Exhibit

Error: Model 'MyModel' (version 1) is in the 'Staging' stage and cannot be deployed to 'Production' because the registry policy requires an 'Approved' status from a team lead.

Question 8hardmulti select

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Which TWO of the following statements are correct regarding the Databricks Model Registry?

Question 9easymultiple choice

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Which component of MLflow provides a centralized UI to compare runs, visualize metrics, and manage model versions?

Question 10mediummultiple choice

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Refer to the exhibit. What is the most likely cause of this error?

Exhibit

2023-10-27 10:00:00 [ERROR] Model deployment failed: Feature lookup for 'user_age' failed. Required feature not found in feature store.

Question 11hardmultiple choice

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You are training a model on a massive dataset and want to use Hyperopt to perform hyperparameter tuning across a distributed cluster. Which approach is most effective for scaling this process on Databricks?

Question 12mediummultiple choice

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A machine learning engineer is building a Databricks Job that trains a model and logs it with MLflow. The engineer wants to ensure that the exact library dependencies and environment used during training are captured so the model can be reliably reproduced later. Which approach should the engineer take?

Question 13hardmulti select

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A data scientist is using Databricks Feature Store to create a feature table for a model that will be served in real time. The feature table will be used both for training and for online inference. Which TWO of the following statements are correct regarding the necessary configuration? (Choose two.)

Question 14mediummultiple choice

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An ML engineer is building a training pipeline in Databricks that reads a large Delta table, applies a scikit-learn model, and logs parameters, metrics, and the model artifact using MLflow. The engineer notices that every run logs the full training dataset as an artifact, which is slow and unnecessary. The engineer wants to log only the model, metrics, and parameters while still enabling full reproducibility. Which MLflow feature should be used to capture the environment and code state without logging the dataset?

Question 15hardmultiple choice

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An ML engineer wants to ensure that a model registered in the Databricks Model Registry automatically transitions to the Production stage after passing a validation notebook that computes accuracy on a holdout set. They want this transition to be triggered from a Databricks Job. Which approach correctly implements this?

Question 16hardmulti select

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An ML engineer is using MLflow Tracking to log experiments for a model. They want to ensure that the experiment can be reproduced later and that the best model can be registered to the Databricks Model Registry. Which TWO actions should they take? (Choose two.)

Question 17hardmulti select

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A machine learning team is using Databricks Feature Store to build a training dataset for a fraud detection model. They want to ensure that the same feature computation logic is used during both training and online inference. Which TWO practices should they follow? (Choose two.)

Question 18mediummultiple choice

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An ML engineer is using MLflow Tracking with Databricks to log experiments for a fraud detection model. After several runs, they notice that the run's parameters, metrics, and artifacts are not appearing in the MLflow experiment UI. The engineer confirms that the code calls mlflow.start_run() and mlflow.log_param(). Which configuration is most likely missing to ensure logs are sent to the Databricks-hosted MLflow tracking server?

Question 19mediummultiple choice

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An ML engineer is deploying a model to production using Databricks Model Serving. The model was trained on a feature table in Databricks Feature Store. The engineer wants to ensure that the same feature transformations are applied during online inference as were used during training. Which approach correctly ensures consistency between training and inference?

Question 20mediummultiple choice

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An ML engineer is evaluating a model with a custom Python function that logs a single numeric metric and a text summary. They want to compare this evaluation across multiple runs in the MLflow experiment UI. Which MLflow tracking function should they use to record both values in one call?

Question 21mediummultiple choice

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A team is using MLflow Projects to package their machine learning code. They want to run the project on a Databricks cluster with a specific environment and parameters. Which method should they use to execute the project?

Question 22easymultiple choice

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A data scientist has trained a scikit-learn model and wants to register it in the Databricks Model Registry with a signature that defines the expected input and output schema. Which MLflow method should they call to create a model signature from a sample input and the model's predictions?

Question 23mediummulti select

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A data scientist is using MLflow Model Registry in Databricks to manage model versions. They want to automate the promotion of models from Staging to Production based on evaluation metrics. Which TWO of the following steps are required to implement this automation? (Choose two.)

Question 24easymultiple choice

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A junior data scientist is using Databricks Repos to manage code for an ML project. They want to collaborate with teammates and track changes to notebooks and Python files. Which Git operation should they use to create a personal copy of the repository where they can make changes without affecting the main branch?

Question 25mediummulti select

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A data scientist is using MLflow to track experiments for a model that predicts customer churn. They want to log the model's hyperparameters, evaluation metrics, and the trained model artifact for each run. Which TWO of the following MLflow operations should they perform to achieve this? (Choose two.)

Question 26mediummultiple choice

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An ML engineer is using MLflow Projects to package a training script. The project must run on a Databricks cluster with a specific Python library version that is not installed by default. Which file in the MLflow Project should the engineer modify to specify the required library dependency?

Question 27mediummultiple choice

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A data scientist is training a model using MLflow on Databricks. They need to ensure that the model artifacts and metrics are logged automatically without adding manual logging code to the training script. Which approach should they use?

Question 28hardmultiple choice

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Refer to the exhibit. A Databricks job failed to start, returning the error shown. The job depends on MLflow for tracking. What is the most likely cause of this failure?

Exhibit

{
  "status": "FAILED",
  "error": "java.lang.ClassNotFoundException: org.mlflow.tracking.MlflowClient",
  "context": "Job cluster initialization",
  "node_type": "i3.xlarge"
}

Question 29easymultiple choice

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When designing an ML workflow, what is the primary benefit of using MLflow Projects over executing raw scripts?

Question 30hardmulti select

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Which THREE actions are best practice when deploying a machine learning model using Databricks Model Serving?

Question 31mediummultiple choice

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You are building a pipeline in Databricks and need to ensure that a training job only runs after the upstream data preparation job has successfully completed. Which Databricks feature should you use?

Question 32mediummultiple choice

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Your team is experiencing 'data drift' in production where the model's accuracy drops over time. What is the most recommended Databricks-native approach to address this?

Question 33easymultiple choice

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Which of the following is an advantage of using Delta Lake for the data storage layer in an ML workflow?

Question 34hardmultiple choice

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You are deploying a model using MLflow, and you want to log the custom pre-processing logic alongside the model so it is automatically applied during inference. How should you achieve this?

Question 35mediummulti select

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Which TWO factors should be considered when choosing between Batch Inference and Real-time Inference for a model in Databricks?

Question 36hardmultiple choice

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Refer to the exhibit. A data scientist is attempting to log a model to an S3 bucket via MLflow, but they receive the error shown. What is the most likely root cause?

Exhibit

{
  "mlflow_config": {
    "run_id": "xyz123",
    "artifact_uri": "s3://my-bucket/mlflow/xyz123",
    "model_flavor": "sklearn"
  },
  "error": "Permission denied: Unable to write to artifact path"
}

Question 37mediummultiple choice

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A data scientist needs to track parameters, metrics, and model artifacts during training on Databricks. Which approach is the industry-standard best practice to ensure reproducibility and lineage?

Question 38hardmultiple choice

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When configuring a Databricks Workflow to automate a machine learning pipeline, which TWO actions are necessary to ensure the pipeline is robust and manageable?

Question 39easymultiple choice

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Which Databricks component is specifically designed to manage the full lifecycle of machine learning models, including registration, versioning, and stage transitions?

Question 40mediummultiple choice

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A team is transitioning from local model development to production in Databricks. Which THREE practices should be implemented to ensure a successful MLOps workflow?

Question 41mediummultiple choice

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Which workflow step is essential before promoting a model from 'Staging' to 'Production' in the MLflow Model Registry?

Question 42easymultiple choice

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What is the primary benefit of using a 'Job Cluster' instead of an 'All-Purpose Cluster' for automated ML workflows?

Question 43mediummultiple choice

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When designing a production-grade machine learning workflow, which THREE of the following are necessary to ensure the pipeline is observable and recoverable?

Question 44mediummultiple choice

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A data scientist wants to share an MLflow experiment with a teammate. What is the most direct way to ensure the teammate can access the metrics and parameters?

Question 45easymultiple choice

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Which of the following is a core characteristic of 'Model Serving' in Databricks?

Question 46hardmultiple choice

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Why should you use a dedicated Feature Store for your ML workflows instead of just storing features as Delta tables?

Question 47mediummultiple choice

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A data scientist is preparing a feature table in Databricks Feature Store. To ensure the feature table can be used for online inference with low latency, which step is mandatory?

Question 48mediummultiple choice

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You are tracking a deep learning model experiment using MLflow. You want to ensure that the model architecture and all hyperparameters are easily reproducible. Which approach is best practice?

Question 49mediummultiple choice

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Refer to the exhibit. Why is the 'signature' parameter included in the log_model call?

Exhibit

log_model(model=my_model, artifact_path='model', signature=model_signature)

Question 50mediummultiple choice

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When designing a feature engineering pipeline in Databricks, why should you use the Feature Store instead of standard Delta tables?

Question 51hardmulti select

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Which THREE of the following are benefits of using Databricks Workflows for ML model retraining?

Question 52hardmultiple choice

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When using the Databricks Feature Store for real-time inference, why is it recommended to use a specific online store (e.g., Cosmos DB) instead of querying the offline store?

Question 53mediummultiple choice

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Which technique should be used to prevent data leakage in ML workflows when performing cross-validation on time-series data?

Question 54mediummultiple choice

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What is the primary advantage of using Databricks Model Serving over deploying a model on a standalone web server?

Question 55mediummultiple choice

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A data scientist is training a machine learning model on Databricks and needs to log parameters, metrics, and model artifacts. Which tracking component should be used to ensure the reproducibility of the experiment runs?

Question 56mediummultiple choice

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You are building a pipeline where a feature table must be updated daily. Which Databricks construct is the most appropriate for orchestrating this periodic feature engineering job?

Question 57hardmulti select

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Which TWO of the following are benefits of using the Databricks Feature Store for machine learning workflows?

Question 58mediummultiple choice

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Refer to the exhibit. A machine learning engineer wants to promote this model to Production. Which MLflow action should be performed to achieve this while ensuring the model is ready for deployment?

Exhibit

{
  "model_name": "revenue_forecast",
  "run_id": "a1b2c3d4e5f6g7h8",
  "stage": "Staging",
  "status": "READY",
  "tags": {
    "team": "finance",
    "project": "q3_forecast"
  }
}

Question 59mediummulti select

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Which THREE actions are essential when preparing a machine learning model for deployment using the Databricks Model Registry?

Question 60mediummultiple choice

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Which Databricks feature allows you to monitor and manage the lineage of data from the source to the final model prediction?

Question 61mediummultiple choice

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Refer to the exhibit. A data scientist receives this error while trying to load a model. What is the most likely cause of this failure in the workflow?

Exhibit

Error: mlflow.exceptions.RestException: RESOURCE_DOES_NOT_EXIST: Model version with name 'demand_forecast' and version '5' not found.

Question 62mediummultiple choice

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An ML engineer is orchestrating an end-to-end machine learning pipeline using Databricks Jobs. The pipeline consists of data preparation, distributed hyperparameter tuning with Hyperopt, and model registration. The engineer needs to pass the best-performing model's run ID from the Hyperopt task to the subsequent model registration task dynamically. Which mechanism should the engineer use in Databricks Jobs to achieve this?

Question 63mediummultiple choice

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An ML engineer is building a recurring batch inference pipeline in Databricks. The model is registered in Unity Catalog as a model version and must always use the version currently tagged as 'champion', which is reassigned after each retraining run. The engineer wants the inference notebook to resolve this alias at runtime rather than hard-coding a version number. Which approach should the engineer use in the notebook?

Question 64mediummultiple choice

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An ML engineer is building a Databricks Job that trains a model with scikit-learn and needs to capture hyperparameters, evaluation metrics, and the resulting model artifact for each run. The team wants to compare runs visually in the workspace and later promote the best model to the Model Registry. Which MLflow capability should the engineer use to record these per-run details?

Question 65hardmultiple choice

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A team trains a model with Databricks Feature Store features and logs the training set using feature_lookups. At inference time, they want the model to automatically retrieve the same feature values from the online store so the serving endpoint does not require the caller to supply those features. What must the team do when logging the model so this automatic lookup works?

Question 66mediummultiple choice

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A data scientist trains a scikit-learn model in a Databricks notebook and logs it with MLflow. She now needs to promote the exact model artifact to the Databricks Model Registry so that it can be served. Which MLflow API call accomplishes this promotion?

Question 67mediummultiple choice

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You are training a scikit-learn model with MLflow in a Databricks notebook. The model's preprocessing includes a custom Python function that you wrote in the notebook. You need to register the model to the Databricks Model Registry and later deploy it with Model Serving, ensuring the preprocessing is applied automatically at inference. Which approach should you use?

Question 68mediummultiple choice

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An ML engineer has a scikit-learn model trained locally and wants to log it to MLflow with a signature so that Databricks Model Serving can enforce input schema validation. The engineer calls mlflow.sklearn.log_model(model, 'model') but does not use infer_signature. What is the most accurate consequence when the model is later served on Databricks Model Serving?

Question 69easymultiple choice

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An ML engineer is using MLflow Tracking to compare multiple runs of a hyperparameter tuning experiment. The engineer wants to quickly identify the run that achieved the best validation accuracy and then promote that run's model to the Model Registry. Which MLflow feature allows the engineer to view and compare runs in a centralized UI?

Question 70hardmultiple choice

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An ML engineer runs an automated hyperparameter sweep with MLflow on a Databricks cluster. The sweep launches 200 runs, and the engineer wants to retrieve, in a notebook, the run ID of the single run that achieved the highest validation accuracy so it can be registered. Which approach correctly identifies that run?

Question 71hardmultiple choice

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An ML engineer runs a hyperparameter tuning job on Databricks using Hyperopt with SparkTrials. The objective function trains a model and returns a validation metric. The engineer notices that each trial logs to the same MLflow run, making it impossible to compare trials. What should the engineer do to ensure each trial appears as a separate MLflow run?

Question 72easymultiple choice

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A data scientist wants to package a training script with its Python dependencies and parameters so that the same code can be rerun on a different Databricks cluster or shared with a colleague and reproduced exactly. Which MLflow component is designed for packaging and reproducing project code in this way?

Question 73mediummultiple choice

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A data scientist trains a scikit-learn model in a Databricks notebook and calls mlflow.sklearn.log_model with the registered_model_name argument set to 'churn_model'. Later, a colleague needs to know which source notebook and Git commit produced run ID 3f8a1c. Where can this information be retrieved?

Question 74mediummultiple choice

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A data scientist trains a scikit-learn model with MLflow tracking in a Databricks notebook. They call mlflow.sklearn.log_model(model, 'model') but later find that the Registered Model's schema shows no input signature, preventing automatic schema enforcement during serving. What should they have done to capture the model signature?

Question 75easymultiple choice

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A data scientist wants to compare the performance of three hyperparameter configurations for a Spark ML model. They need a central place to view metrics like RMSE and MAE across runs, and to filter runs by parameters. Which Databricks capability should they use?

Question 76mediummultiple choice

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A data scientist is building a training pipeline where raw event data lands in a Delta table. They need to transform the data, train a model, and register it to the Databricks Model Registry. The pipeline must run daily on a schedule and send an email alert if training fails. Which Databricks construct should they use to orchestrate the entire workflow?

Question 77mediummultiple choice

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A data science team is using Databricks Jobs to orchestrate a machine learning pipeline. The pipeline includes a task that trains a model and a subsequent task that evaluates the model. The evaluation task must access the model version produced by the training task. Which mechanism should the team use to pass the model version between tasks?

Question 78easymultiple choice

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A data scientist needs to run the same feature-engineering notebook against three different parameter sets in a Databricks Job. She wants each parameter set to execute independently and in parallel, with separate logs. Which Job feature should she use?

Question 79mediummulti select

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An ML engineer is setting up a Databricks Job to retrain a production model nightly. The job must run only when upstream data validation succeeds, notify the team on failure, and avoid retraining when the input data has not changed. Which TWO capabilities of Databricks Jobs directly support these requirements? (Choose two.)

Question 80easymultiple choice

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A data scientist wants to track the progress of a training script that runs for several hours on a Databricks cluster. The script uses MLflow and needs to record metrics such as loss and accuracy at the end of each epoch so they can be visualized in real time. Which MLflow API call should be used inside the training loop?

Question 81mediummulti select

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An ML engineer is configuring a Databricks Job to automate nightly retraining of a model. The job must (1) run only after the upstream feature engineering job succeeds, and (2) notify the team via email if the training task fails. Which TWO configurations satisfy these requirements? (Choose two.)

Question 82hardmultiple choice

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An ML engineer registers a model in Unity Catalog and needs a stable pointer that always resolves to the version currently approved for production, without modifying calling code each time a new version is promoted. Which Model Registry feature should the engineer configure to provide this stable reference?

Question 83mediummultiple choice

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An ML engineer wants to package a training project so it can be run reproducibly from a Databricks Job across environments, with its Python dependencies and entry point defined. Which MLflow capability should be used?

Question 84hardmultiple choice

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An ML engineer registers a model to the Databricks Model Registry and moves it to the 'Production' stage. A downstream batch scoring job references the model as models:/churn_model/Production. A data scientist then registers a new model version and transitions it to 'Production'. What happens to the downstream batch scoring job the next time it runs?

Question 85mediummultiple choice

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You are using Databricks Feature Store to create a feature table that will be used for both batch training and online inference. The feature table must be refreshed daily with new data, and the online store must serve the latest feature values within minutes of the refresh. Which configuration should you use?

Question 86easymultiple choice

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A data scientist is using MLflow Tracking in Databricks to compare multiple runs of a hyperparameter tuning experiment. They want to quickly identify the run with the lowest validation loss and then register that model version in the MLflow Model Registry. Which MLflow UI feature allows sorting runs by a specific metric to find the best run?

Question 87mediummultiple choice

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A machine learning engineer is building a training pipeline in Databricks. They want each run to record the exact Git commit hash, the versions of scikit-learn and MLflow used, and the input data path so the run can be reproduced later. Which MLflow tracking capability should they use to capture this information with the least custom code?

Question 88easymultiple choice

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A data scientist wants to compare the accuracy, F1 score, and training duration of several model training runs side by side in a single table, and visually inspect how a hyperparameter affected the metric across runs. Which MLflow capability should be used?

Question 89hardmulti select

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An ML engineer is using Databricks Jobs to orchestrate a machine learning pipeline that includes data ingestion, feature engineering, model training, and batch scoring. The engineer wants to ensure that the pipeline is reproducible, handles failures gracefully, and allows for easy debugging of individual tasks. Which TWO features of Databricks Jobs should the engineer leverage to meet these requirements? (Choose two.)

Question 90mediummulti select

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A team is using Databricks Feature Store to create a feature table that will be used for both batch training and online inference. They need to ensure the feature table supports point-in-time lookups for training and low-latency reads for serving. Which TWO of the following statements are correct about meeting these requirements? (Choose two.)

Question 91easymultiple choice

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A data scientist has trained a model and wants to deploy it for real-time inference with automatic scaling and without managing infrastructure. Which Databricks feature should they use?

Question 92easymultiple choice

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A data scientist is using MLflow Tracking to log experiments. They want to compare multiple runs of a scikit-learn model and identify the run with the lowest RMSE. Which MLflow feature should they use?

Question 93hardmultiple choice

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An ML engineer needs to deploy a model for real-time inference with automatic scaling and a REST endpoint that requires token-based authentication. The model artifacts are already registered in the Databricks Model Registry. Which Databricks capability should be used?

Question 94mediummultiple choice

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A data scientist wants to package a training script so that it can be run repeatedly with different hyperparameters and shared with colleagues who use different Python library versions. The script must create a reproducible environment. Which MLflow component should they use to define the project and its dependencies?

Question 95mediummultiple choice

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A data scientist is using Databricks AutoML to train a classification model on a dataset with a binary target. They want to understand which features contributed most to the model's predictions and need a human-readable summary. Which AutoML output should they examine?

Question 96mediummultiple choice

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An ML engineer trains a scikit-learn model on a Spark DataFrame in a Databricks notebook using MLflow autologging. The run logs parameters and metrics, but the engineer later opens the MLflow run and cannot find any input dataset lineage. Which action should the engineer take to ensure the training dataset is recorded with the run in the MLflow UI?

Question 97hardmultiple choice

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An ML engineer is using Databricks Jobs to orchestrate a pipeline that includes a notebook for feature engineering and a notebook for model training. The training notebook must run only after the feature engineering notebook completes successfully, and both must run on a schedule. Which configuration in Databricks Jobs achieves this dependency?

Question 98hardmultiple choice

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An ML engineer has registered a model in the Databricks Model Registry. The model must be deployed to a REST endpoint that automatically scales with traffic and provides a stable serving environment. Which Databricks capability should they use?

Question 99hardmultiple choice

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An ML engineer is configuring a Databricks Job to retrain a model daily. The job must run a notebook that reads from a feature table, trains a model, and registers it to the Model Registry. The engineer wants to ensure that the job fails immediately if the model's accuracy drops below a threshold. Which approach should they use?

Question 100hardmultiple choice

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A machine learning team is using Databricks Feature Store to serve features for a real-time model. They have a feature table that is updated daily with new data. To ensure the online store always has the latest feature values for low-latency inference, which approach should they take?

Question 101hardmultiple choice

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A machine learning team is using Databricks Jobs to orchestrate a multi-step ML pipeline: data ingestion, feature engineering, model training, and batch inference. They need to ensure that if the model training step fails, the batch inference step does not run, and that the entire pipeline can be retried from the failed step. Which Databricks Jobs feature should they use to achieve this?

Question 102hardmultiple choice

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A team registers a model in Unity Catalog as main.ml.churn_model and wants production scoring jobs to always load the newest approved version without editing job code when a new version is promoted. The team uses the MLflow Python client inside a Databricks job. Which model URI should the scoring code use?

Question 103easymultiple choice

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A data scientist is comparing multiple hyperparameter configurations for a model and wants to view the resulting metrics side by side in a single interface, sort runs by accuracy, and drill into individual run details. Which MLflow component provides this capability?

Question 104mediummultiple choice

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A data scientist is tuning a scikit-learn random forest with Hyperopt in a Databricks notebook. Each trial trains on a 40 GB Delta table, and the scientist notices that every trial re-reads the full table from cloud storage, making the search slow. Which change best accelerates the hyperparameter search while preserving correctness?

Question 105hardmulti select

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An ML platform team is standardizing how models move from experimentation to production on Databricks. They want promotion decisions to be auditable and to prevent unvalidated models from serving live traffic. Which TWO practices align with Databricks Model Registry and Unity Catalog governance? (Choose two.)

Question 106hardmultiple choice

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An ML engineer is configuring a Databricks Job to retrain a model daily. The job must first run a notebook that creates a feature table, then run a notebook that trains and registers the model. The engineer wants to ensure the training notebook only runs if the feature table creation succeeds. Which Databricks Workflows feature should they use to define this dependency?

Question 107easymultiple choice

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A data scientist finishes a training notebook and wants to capture the source code revision and the Git repository URL on the MLflow run so reviewers can reproduce the exact code state. The repository is already connected to the Databricks workspace through Git integration. Which mechanism records this information automatically?

Question 108hardmultiple choice

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An ML engineer is using Databricks Jobs to orchestrate a multi-step ML pipeline. The pipeline includes a task that trains a model and logs it to MLflow, followed by a task that registers the model in the Model Registry. The engineer wants to ensure that the model is only registered if its accuracy exceeds a threshold. Which approach best implements this conditional logic?

Question 109mediummultiple choice

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An ML engineer is developing a model in a Databricks notebook. They want to log the model and its dependencies to MLflow, ensuring that the exact library versions used during training are captured. They also need to register the model in the Databricks Model Registry. Which approach should they use?

{ "model_signature": { "inputs": [{"name": "feature_a", "type": "double"}, {"name": "feature_b", "type": "double"}], "outputs": [{"name": "prediction", "type": "double"}] }, "input_example": { "feature_a": [1.0, 2.0], "feature_b": [3.0, 4.0] } }

{ "task_key": "TrainModel", "notebook_task": { "notebook_path": "/Shared/Train", "base_parameters": { "learning_rate": "0.01", "epochs": "10" } }, "job_cluster_key": "ML_Cluster" }

{ "mlflow_config": { "run_id": "xyz123", "artifact_uri": "s3://my-bucket/mlflow/xyz123", "model_flavor": "sklearn" }, "error": "Permission denied: Unable to write to artifact path" }