You’re working with a large dataset in Databricks where you need to train a machine learning model that requires extensive hyperparameter tuning. The dataset is too large to fit into the memory of a single machine, and you want to leverage Spark’s distributed computing capabilities for both data processing and model training. Which of the following approaches would be the most appropriate and efficient way to build and tune your model in this scenario?

A colleague shows you a feature engineering pipeline that seems to be performing poorly. They’re using StringIndexer followed by OneHotEncoder on a high-cardinality categorical column with over 10,000 distinct values. What would be the most significant concern you’d raise about this approach, and what alternative would you likely recommend?

Imagine you’ve deployed a churn prediction model using MLflow Model Serving. After several weeks, you notice the model’s performance metrics in production have degraded significantly, but your retrained model on recent data performs well in validation. What should be your first systematic step to investigate this discrepancy?

When working with time-series data in Databricks for forecasting, you need to create features that capture seasonal patterns. Your data has daily observations with clear weekly and yearly seasonality. Which of these feature engineering strategies would be most effective for helping a model learn these patterns?

You’re building a binary classification model to detect fraudulent transactions. The dataset is highly imbalanced with only 0.1% positive (fraud) cases. Your initial model achieves 99.9% accuracy but fails to detect most fraud cases. Which combination of approaches would be most practical to address this issue in a Spark ML pipeline?

In a collaborative Databricks environment, your team needs to track experiments, compare model performance, and ensure reproducibility across different runs. What Databricks-native functionality provides the most comprehensive solution for these requirements without requiring extensive custom infrastructure?

After training a gradient boosted trees model on a large dataset, you want to understand feature importance to explain the model’s predictions to business stakeholders. Which approach would provide the most reliable and actionable insights?

You’re preparing to deploy a real-time recommendation model that needs to serve predictions with low latency. The model uses a combination of collaborative filtering and content-based features. Which deployment strategy in Databricks would best meet the latency requirements while maintaining model management capabilities?

While monitoring your production model, you discover that one of the key input features has suddenly started containing null values for 30% of the prediction requests due to a change in an upstream data source. Your model wasn’t trained on data with nulls in this feature. What’s the most robust immediate action to maintain service continuity while you address the root cause?

You’re working with a large dataset in Databricks where you need to train a machine learning model that requires extensive hyperparameter tuning. The dataset is too large to fit into the memory of a single machine, and you want to leverage Spark’s distributed computing capabilities for both data processing and model training. Which of the following approaches would be the most appropriate and efficient way to build and tune your model in this scenario?0