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Answer: 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.
The question requires forecasting future weekly sales based on historical seasonal patterns to optimize inventory and personnel. Option A is optimal because it directly addresses time series forecasting using appropriate models for predicting future sales values. The community discussion (100% consensus for A) supports this, noting that time series forecasting models are specifically designed for this use case. Option B is less suitable as logistic regression for categorical prediction (high/medium/low) doesn't provide precise numerical forecasts needed for inventory optimization. Option C only enables visualization and analysis of past data without predictive capabilities. Option D is overly complex and inefficient for a straightforward forecasting task, as LLMs are not the most efficient tool for numerical time series prediction compared to dedicated forecasting models.
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As an AI engineer for an apparel retailer, you have observed seasonal sales patterns over the past 5-6 years in weekly sales data stored in CSV files. To optimize inventory and personnel workloads by forecasting future weekly sales, what is the most efficient approach?
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).