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The correct answer is B. Transform the data into a consistent format. Here's why:

Context of the Question: The cinema company wants to build a machine learning model to predict customer visit patterns using three years of customer visit data stored in different formats. For effective ML model training, the input data must be in a consistent format.

Google Cloud Product Relevance:

To build a robust and accurate ML model, the data used for training needs to be cleaned, pre-processed, and transformed into a consistent format. This process ensures that the model can interpret and learn from the data correctly.

Google Cloud provides services like Dataflow for data transformation and processing, and Dataprep for data cleaning and preparation. These tools help standardize data formats before feeding it into a machine learning model.

Why Not Other Options:

A. Choose an ML model type that can process different formats of input data: Most ML models require data to be in a uniform format; choosing a model that processes multiple formats is not standard practice and can lead to inaccuracies.

C. Use the last year of data so there are fewer inconsistencies for the model to handle: This would limit the amount of data available for training, reducing the model’s accuracy and effectiveness.

D. Group different format types and train a different model for each group: Training multiple models for different data formats is inefficient and complex compared to transforming the data into a single consistent format.

Google Cloud Digital Leader References:

Refer to Dataflow and Dataprep documentation for details on data transformation and preparation services in Google Cloud.