ML-ASSOC Sample Questions & Answers

A data scientist is using Hyperopt to perform Bayesian hyperparameter optimization for a machine learning model. They need to supply the correct search algorithm to the algo parameter of the fmin function. Which of the following options implements a Bayesian approach, specifically Tree-structured Parzen Estimator?

An MLOps team is managing a critical fraud detection model registered in Unity Catalog. The current production model is aliased as 'Champion'. A new version has been validated and is ready to be promoted. To minimize risk, the team needs a strategy that allows for an immediate rollback to the previous version if the new model underperforms. Which steps should they perform? (Select TWO)

During exploratory data analysis for a demand forecasting model, a data scientist observes that a key feature, user_daily_logins, has a strong positive skew, with most users logging in 1-2 times a day but a small number of power users logging in over 50 times. This skew could negatively impact the performance of a linear regression model. Which feature transformation is most appropriate to apply to this feature?

A machine learning team is using GridSearchCV from scikit-learn to tune a Gradient Boosting model. The parameter grid is defined as follows:
param_grid = {'n_estimators': [100, 200], 'learning_rate': [0.01, 0.1, 0.2], 'max_depth': [3, 5, 7]}
They are using 5-fold cross-validation (cv=5). How many individual models will be trained during this entire hyperparameter tuning process?

A logistics company needs to deploy a model for real-time anomaly detection in its package delivery event stream. The pipeline must process a high volume of events with fluctuating loads and requires a solution that simplifies infrastructure management and automatically handles cluster scaling. What is the primary advantage of using Delta Live Tables (DLT) for this streaming inference task compared to a manually configured Structured Streaming job?

A small data science team is starting a new project to predict customer lifetime value. They have a well-prepared dataset but limited time for initial model exploration. What is the key benefit of using Databricks AutoML at the beginning of this project?

A fraud detection model is being developed for a financial institution. The dataset is highly imbalanced, with fraudulent transactions representing only 0.1% of the data. The primary business objective is to minimize financial losses, meaning the cost of a false negative (missing a fraudulent transaction) is thousands of times higher than the cost of a false positive (flagging a legitimate transaction). Which technique for handling data imbalance is most directly aligned with this business objective?

During a project kickoff meeting, a senior ML engineer recommends that the team use the Databricks ML Runtime for their development cluster instead of the standard Databricks Runtime. What is a key advantage of the ML Runtime that justifies this recommendation?

A data analyst is performing exploratory data analysis to understand how sales performance varies across different geographical regions. They have a Spark DataFrame with a categorical feature 'region' (e.g., 'North', 'South', 'East', 'West') and a continuous feature 'total_sales'. Which type of visualization is most appropriate for comparing the distribution of 'total_sales' across the different 'region' categories?

A rapidly growing tech company has multiple data science teams that are developing models independently. This has led to several problems: inconsistent model deployment processes, difficulty reproducing past experiments, and no clear line of sight into which model versions are serving production traffic. The Head of MLOps has been tasked with designing and implementing a standardized, end-to-end MLOps workflow.

Which of the following proposed workflows represents the most robust and best-practice MLOps strategy on Databricks?