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NEW QUESTION 53
A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?

  • A. Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
  • B. Use the event tracker in Amazon Personalize to include real-time user interactions.
  • C. Add event type and event value fields to the interactions dataset in Amazon Personalize.
  • D. Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.

Answer: C

 

NEW QUESTION 54
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)

  • A. Decrease feature combinations.
  • B. Decrease regularization.
  • C. Decrease dropout.
  • D. Increase feature combinations.
  • E. Increase dropout.
  • F. Increase regularization.

Answer: C,D,F

 

NEW QUESTION 55
A gaming company has launched an online game where people can start playing for free, but they need to pay if they choose to use certain features. The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year. The company has gathered a labeled dataset from 1 million users.
The training dataset consists of 1,000 positive samples (from users who ended up paying within 1 year) and
999,000 negative samples (from users who did not use any paid features). Each data sample consists of 200 features including user age, device, location, and play patterns.
Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set. However, the prediction results on a test dataset were not satisfactory Which of the following approaches should the Data Science team take to mitigate this issue? (Choose two.)

  • A. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
  • B. Change the cost function so that false negatives have a higher impact on the cost value than false positives.
  • C. Change the cost function so that false positives have a higher impact on the cost value than false negatives.
  • D. Include a copy of the samples in the test dataset in the training dataset.
  • E. Add more deep trees to the random forest to enable the model to learn more features.

Answer: A,B

 

NEW QUESTION 56
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?

  • A. Linear regression
  • B. Classification
  • C. Reinforcement learning
  • D. Clustering

Answer: A

 

NEW QUESTION 57
A gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million users The training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and
999.000 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user age, device, location, and play patterns Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set However, the prediction results on a test dataset were not satisfactory.
Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

  • A. Add more deep trees to the random forest to enable the model to learn more features.
  • B. Change the cost function so that false negatives have a higher impact on the cost value than false positives
  • C. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
  • D. indicate a copy of the samples in the test database in the training dataset
  • E. Change the cost function so that false positives have a higher impact on the cost value than false negatives

Answer: B,D

 

NEW QUESTION 58
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