Google Professional Machine Learning Engineer
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Ultimate access to all questions.
You are a data scientist working for a large retailer. The management has tasked you with segmenting the customer base based on their purchasing habits to inform targeted marketing strategies. They have provided you with the purchase history of all customers in a BigQuery database. You believe that multiple distinct customer segments exist, but the exact number and specific behaviors defining these segments are unknown. Given the need for an efficient and scalable solution, what approach should you take?
A
Create a k-means clustering model using BigQuery ML. Allow BigQuery to automatically optimize the number of clusters.
B
Create a new dataset in Dataprep that references your BigQuery table. Use Dataprep to identify similarities within each column.
C
Use the Data Labeling Service to label each customer record in BigQuery. Train a model on your labeled data using AutoML Tables. Review the evaluation metrics to understand whether there is an underlying pattern in the data.
D
Get a list of the customer segments from your company’s Marketing team. Use the Data Labeling Service to label each customer record in BigQuery according to the list. Analyze the distribution of labels in your dataset using Data Studio.