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Exam Cloud Digital Leader topic 1 question 218 discussion

Actual exam question from Google's Cloud Digital Leader
Question #: 218
Topic #: 1
[All Cloud Digital Leader Questions]

A cinema company wants to build a model to predict customer visit patterns for the coming year. They have three years of customer visit data across 300 theaters; however, the data has been stored in different formats by different theaters. They must train the ML model. What should they do?

  • A. Use the last year of data so there are fewer inconsistencies for the model to handle.
  • B. Transform the data into a consistent format.
  • C. Group different format types and train a different model for each group.
  • D. Choose an ML model type that can process different formats of input data.
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Suggested Answer: B 🗳️
by Vivek007 at April 30, 2024, 6:58 a.m.

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joshnort
13 hours, 4 minutes ago
Selected Answer: B
B. Transform the data into a consistent format Consistency in Data: To effectively train a machine learning model, the data must be in a consistent format across all sources. This allows the model to learn patterns from the entire dataset without being confused by variations in the data structure. Inconsistent formats can introduce noise and lead to poor model performance.
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joshnort
13 hours, 3 minutes ago
Why the other options are wrong: Option A (Use the last year of data): This option would limit the model to only a small portion of the available data, ignoring potentially valuable insights from the earlier years. Option C (Group different format types and train a different model for each group): While this might be feasible, it's often inefficient and complex. A single model trained on a consistent dataset is usually more effective than multiple models, as it can learn more generalizable patterns. Option D (Choose an ML model type that can process different formats of input data): While some models may be more flexible in handling diverse input types, this is not typically the best
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Vivek007
2 months, 2 weeks ago
B: Data Consistency: Before any effective machine learning modeling can occur, it is crucial that the data used is consistent and standardized. This means transforming all the data collected from different theaters into a uniform format. This process, often called data cleaning or preprocessing, ensures that the machine learning model has reliable and comparable input data across all samples, leading to more accurate and meaningful predictions.
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