Exam Professional Machine Learning Engineer topic 1 question 236 discussion

B is the definitive answer. By breaking down the question we know it is a classification problem, so A and C are wrong since they're linear regression. Using confusion matrix to evaluate the model is not wrong (actually it's even the textbook answer to do it), but it is not enough if you want to thoroughly evaluate its performance. Hence the best way to do it is with Vertex AI.

logistic since it's classification, and Vertex AI because we need to "thoroughly evaluate its performance"

Logistic Regression: While linear regression (option C) can be used for continuous prediction tasks, customer churn is a binary classification problem (churned/not churned). Logistic regression is a better fit for this scenario. Vertex AI Model Registry: Registering the model in Vertex AI Model Registry provides a centralized location for model management, versioning, and potentially future deployment to other Vertex AI services. Vertex AI Evaluation: Vertex AI offers more comprehensive evaluation tools than BigQuery ML's ML.EVALUATE function (option C) or ML.CONFUSION_MATRIX function (option D). Vertex AI can provide metrics like accuracy, ROC-AUC, precision, and recall, which are crucial for churn prediction evaluation.

My Answer: B predict customer churn, which is a binary classification problem (whether a customer will churn or not). And, the phrase "thoroughly evaluate its performance" does suggest a more comprehensive approach, and in that sense, option B could be seen as a better answer than D.

My Answer: B predict customer churn, which is a binary classification problem (whether a customer will churn or not). And, the phrase "thoroughly evaluate its performance" does suggest a more comprehensive approach, and in that sense, option B could be seen as a better answer than D.

B because Vertex AI provides us with more functions to evaluate model performance then just CONFUSION_MATRIX https://cloud.google.com/vertex-ai/docs/evaluation/introduction#classification_1

B. Linear regression because customer churn is a number of customers (not just 1/0). The key here imo is "thoroughly evaluate performance", which Vertex AI seems to be better suited for than BQ (including the possibility of tracking experiment lineage, inspecting parameter selection of each run, etc)

Customer churn prediction involves a binary classification task (whether a customer will churn or not). Logistic regression is specifically designed for this type of problem, making it the appropriate model. BigQuery ML allows building and training logistic regression models directly within BigQuery, leveraging its scalability and SQL-like syntax for model development.