Domain 1: Databricks ML Workflow & MLflow

Databricks Certified Machine Learning Associate · 66 questions

• Which MLflow component is responsible for logging parameters, metrics, and artifacts during a model training run?
• When using MLflow in a Databricks notebook, what is the recommended way to start a new tracking run and ensure it is properly closed?
• In the MLflow Model Registry, which sequence correctly represents the typical model lifecycle stages?
• A data scientist calls mlflow.autolog() before training a scikit-learn model. Which items will be automatically logged without any additional mlflow.log calls?
• What does Databricks AutoML automatically produce after completing an experiment?
• What is the key difference between mlflow.logartifact() and mlflow.sklearn.logmodel()?
• A team has run 500 MLflow experiments and wants to programmatically find all runs where validation accuracy exceeded 0.90. Which approach is correct?
• During hyperparameter tuning, you want to group all trial runs under a single parent run in MLflow. How do you create a child run?
• What advantage does Unity Catalog provide for ML models compared to the workspace-level MLflow Model Registry?
• In a Databricks workspace, where does MLflow store experiment metadata (parameters, metrics) by default?
• In Databricks, what is the default experiment associated with a notebook when mlflow.startrun() is called without specifying an experiment?
• When logging training loss during a neural network's epochs using MLflow, which parameter in mlflow.logmetric() enables plotting the metric over training iterations?
• Which evaluation metric does Databricks AutoML optimize by default for a binary classification problem?
• How can you add searchable metadata to an MLflow run to mark it as part of a specific project or team?
• What is an MLflow model 'flavor', and why is the pythonfunction flavor particularly useful?
• A data scientist runs 20 experiments and wants to identify the run with the highest validation F1 score. Which is the fastest programmatic approach?
• When using Databricks Repos for ML project versioning, which MLflow tag is automatically set to help link runs to specific code versions?
• What benefit does logging an inputexample with mlflow.sklearn.logmodel() provide?
• In Unity Catalog, how is a model registered using the three-level namespace?
• In Unity Catalog's model registry, what replaces stage-based lifecycle management (Staging/Production) for model versioning?
• During a single MLflow run, which API pattern correctly logs multiple hyperparameters and metrics?
• Databricks AutoML runs on a labeled table whose target has three discrete classes. What problem type is this?
• Unity Catalog governed models in Databricks are referenced using which pattern?
• Why should you log an MLflow model signature?
• Parent/child (nested) MLflow runs are MOST useful when:
• A key benefit of Databricks AutoML output is:
• Confusion matrices and ROC plots should be stored in MLflow as:
• mlflow.searchruns filterstring is used to:
• MLflow model aliases (e.g., champion) help deployments by:
• mlflow.autolog() helps teams by:
• Transitioning a model version to Production in the registry should typically require:
• MLflow Projects help reproducibility by:
• Using the MLflowClient API, transitioning model stages/aliases is done to:
• Setting an AutoML timeout/compute budget primarily controls:
• Unity Catalog lineage for ML assets helps with:
• Logging an input example with the model helps:
• Consistent MLflow experiment naming and tagging helps:
• MLflow model flavors exist to:
• dbutils.secrets should be used in ML notebooks to:
• MLflow run tags/descriptions should capture:
• MLflow registry webhooks can notify systems when:
• Pointing MLflow tracking URI to a remote server/workspace enables:
• ML code quality on Databricks improves when teams:
• The step parameter in logmetric is used to:
• AutoML feature type inference can mis-detect IDs as numeric continuous. You should:
• To allow a service principal to deploy a UC model, you typically need:
• When comparing nested HPO runs, focus on:
• When hyperparameters are nested dicts, best practice is to:
• Very large model artifacts impact:
• Marking MLflow runs failed/killed correctly helps:
• Ordering searchruns by metrics.valf1 DESC is used to:
• Databricks Repos/Git integration helps ML teams by:
• Organizing MLflow artifact paths (e.g., plots/, reports/) helps:
• Restricting who can overwrite production experiments/registry helps:
• Logging system metrics during training can reveal:
• When running AutoML on imbalanced data, you should still:
• UC Volumes can be used in ML to:
• MLflow compare runs UI is used to:
• When optimizing Hyperopt, you must specify whether to maximize or minimize because:
• Tagging runs with env=prod|dev and dataversion helps:
• Keeping a changelog per model version supports:
• Consistent metric names (valf1 vs f1validation vs F1) matter because:
• Storing MLflow runid with registered model versions provides:
• Deleting model versions in registry should be:
• Logging a hash/version of the training dataset in MLflow enables:
• Databricks ML Associate success looks like: