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Exam Cloud Digital Leader topic 1 question 255 discussion

Actual exam question from Google's Cloud Digital Leader
Question #: 255
Topic #: 1
[All Cloud Digital Leader Questions]

A retail organization is training a model to recommend products to customers for an ecommerce website. The model was trained on previous purchases, but did not include demographic information on each buyer. What dimension of the data is responsible for the model's poor performance?

  • A. Validity
  • B. Accuracy
  • C. Timeliness
  • D. Completeness
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Suggested Answer: D 🗳️
by joshnort at Jan. 17, 2025, 3:19 a.m.

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joshnort
3 months, 1 week ago
Selected Answer: D
D. Completeness. Completeness refers to the extent to which all necessary data is available for the model. In this case, the model is trained on previous purchases but lacks demographic information about the buyers, which is likely an important factor for making more personalized and accurate product recommendations. The absence of this data can lead to poor performance, as the model doesn't have all the relevant information it needs to make better predictions for each individual customer.
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joshnort
3 months, 1 week ago
Why the other options are incorrect: A. Validity: Validity refers to whether the data is correct and represents what it is intended to measure. In this scenario, the model is trained on purchase data, which is presumably valid and appropriate for the task. The problem is not with the correctness of the data but with missing data (demographics), which affects the model's completeness. B. Accuracy: Accuracy refers to the correctness of the predictions or the model's ability to make correct recommendations based on the data. However, the issue in this case is not the accuracy of the model's predictions, but rather that it was trained without all the necessary data (i.e., demographic data). The model might still be accurate based on the data it has, but it is underperforming due to missing features.
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joshnort
3 months, 1 week ago
C. Timeliness: Timeliness refers to the recency or relevance of the data. While the model's performance could potentially improve with more up-to-date data, the primary issue in this scenario is the missing demographic data, not the timing of the data.
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