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NEW QUESTION 33
A Machine Learning Specialist deployed a model that provides product recommendations on a company's website. Initially, the model was performing very well and resulted in customers buying more products on average. However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less.
The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago.
Which method should the Specialist try to improve model performance?

• A. The model's hyperparameters should be periodically updated to prevent drift.
• B. The model should be periodically retrained using the original training data plus new data as product inventory changes.
• C. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
• D. The model needs to be completely re-engineered because it is unable to handle product inventory changes.

Answer: B

NEW QUESTION 34
You work for a large social network service provider whose users post articles and discuss news. Millions of comments are posted online each day, and more than 200 human moderators constantly review comments and flag those that are inappropriate. Your team is building an ML model to help human moderators check content on the platform. The model scores each comment and flags suspicious comments to be reviewed by a human. Which metric(s) should you use to monitor the model's performance?

• A. Number of messages flagged by the model per minute confirmed as being inappropriate by humans.
• B. Number of messages flagged by the model per minute
• C. Precision and recall estimates based on a random sample of 0.1% of raw messages each minute sent to a human for review
• D. Precision and recall estimates based on a sample of messages flagged by the model as potentially inappropriate each minute

Answer: A

NEW QUESTION 35
A manufacturing company has a large set of labeled historical sales data. The manufacturer would like to predict how many units of a particular part should be produced each quarter.
Which machine learning approach should be used to solve this problem?

• A. Principal component analysis (PCA)
• B. Random Cut Forest (RCF)
• C. Logistic regression
• D. Linear regression

Answer: B

NEW QUESTION 36
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