Easy — Databricks-ML-Associate Practice Quiz

Question 1

A data analyst wants to quickly benchmark several models for a customer churn classification problem using the Databricks AutoML UI. They have a Delta table registered in Unity Catalog and want AutoML to automatically try multiple algorithms and generate reproducible notebooks. When configuring the AutoML experiment through the UI, which combination of settings must they specify for the experiment to run correctly?

Accepted Answer

The ML problem type (Classification), the input training dataset, and the prediction target column

Answer

The prediction target column, a pre-trained baseline model, and a manually written evaluation notebook

Answer

The ML problem type (Classification), the MLflow Model Registry alias, and the number of Spark executors

Answer

A feature table lookup key, the primary metric only, and a published online store endpoint

Question 2

A data scientist wants to start a new interactive cluster to train a scikit-learn model and track it with MLflow autologging, using libraries like TensorFlow and XGBoost without manually installing them. When creating the cluster, which choice ensures these ML libraries and MLflow come preinstalled?

Accepted Answer

Select the Databricks Runtime for Machine Learning (Databricks Runtime ML)

Answer

Select a standard Databricks Runtime and add the libraries via an init script

Answer

Select a standard Databricks Runtime and enable Photon acceleration

Answer

Select a standard Databricks Runtime and install libraries with %pip in the notebook

Question 3

A data scientist has trained a scikit-learn classifier inside an MLflow run and generated a confusion matrix plot saved locally as 'confusion_matrix.png'. They want this image to be stored with the run so teammates can view it in the MLflow UI under the run's artifacts. Which MLflow API call should they use inside the active run?

Accepted Answer

mlflow.log_artifact('confusion_matrix.png')

Answer

mlflow.log_metric('confusion_matrix', 'confusion_matrix.png')

Answer

mlflow.log_param('confusion_matrix', 'confusion_matrix.png')

Answer

mlflow.set_tag('confusion_matrix', 'confusion_matrix.png')

Question 4

A data scientist is evaluating a scikit-learn RandomForestClassifier on a dataset that comfortably fits in memory on a single node. They run cross_val_score(model, X, y, cv=5, scoring='f1') and want to report a single, robust estimate of the model's generalization performance for stakeholders. Which value should they report?

Accepted Answer

The mean of the five returned F1 scores

Answer

The maximum of the five returned F1 scores, since it reflects the model's best capability

Answer

The F1 score from a single additional fit on the entire dataset X, y

Answer

The minimum of the five returned F1 scores, to be conservative