Exam Professional Machine Learning Engineer topic 1 question 332 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 332
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You are an AI engineer with an apparel retail company. The sales team has observed seasonal sales patterns over the past 5-6 years. The sales team analyzes and visualizes the weekly sales data stored in CSV files. You have been asked to estimate weekly sales for future seasons to optimize inventory and personnel workloads. You want to use the most efficient approach. What should you do?

A. Upload the files into Cloud Storage. Use Python to preprocess and load the tabular data into BigQuery. Use time series forecasting models to predict weekly sales.
B. Upload the files into Cloud Storage. Use Python to preprocess and load the tabular data into BigQuery. Train a logistic regression model by using BigQuery ML to predict each product's weekly sales as one of three categories: high, medium, or low.
C. Load the files into BigQuery. Preprocess data by using BigQuery SQL. Connect BigQuery to Looker. Create a Looker dashboard that shows weekly sales trends in real time and can slice and dice the data based on relevant filters.
D. Create a custom conversational application using Vertex AI Agent Builder. Include code that enables file upload functionality, and upload the files. Use few-shot prompting and retrieval-augmented generation (RAG) to predict future sales trends by using the Gemini large language model (LLM).

Show Suggested Answer

Suggested Answer: A 🗳️

by Duke_CT at June 20, 2025, 3:52 a.m.

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qaz09

1 month ago

Selected Answer: A

A. -> timeseries forecasting model is the one needed for this use case B. ->ARIMA/time series forecasting model will be better than logistic regression model for this use case C. -> Nothing about predicting values, it is just about visualising data. D. -> Too complicated solution, there is no need for Agent here.

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Duke_CT

1 month, 3 weeks ago

Selected Answer: A

Hmm, seems like A from what I studied.

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