Google Professional Machine Learning Engineer
Comments
Loading comments...
Ultimate access to all questions.
As a data scientist for a large retailer, you're tasked with segmenting customers based on their purchasing habits to tailor marketing strategies effectively. All purchase histories are stored in BigQuery. The challenge is to uncover distinct customer segments without predefined labels, considering the constraints of scalability and cost-efficiency. The solution should also provide insights into the common behaviors within each segment to inform strategic decisions. Given these requirements, what is the most efficient approach to achieve this goal? Choose the best option.
A
Collaborate with the Marketing team to manually define customer segments based on their expertise. Then, use the Data Labeling Service to label each customer in BigQuery accordingly and analyze label distributions with Data Studio for insights.
B
Use Dataprep to preprocess and clean the data in BigQuery, then manually analyze column-wise similarities to identify potential segments without employing machine learning.
C
Employ the Data Labeling Service for labeling each customer record in BigQuery based on preliminary analysis, then use AutoML Tables to train a supervised model. Evaluate the model's metrics to infer underlying data patterns and segments.
D
Develop a k-means clustering model via BigQuery ML, leveraging its capability to automatically determine the optimal number of clusters based on the data, thus identifying distinct customer segments and their common purchasing behaviors efficiently.