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Download AWS Certified Machine Learning - Specialty Exam Dumps

NEW QUESTION 30
A term frequency-inverse document frequency (tf-idf) matrix using both unigrams and bigrams is built from a text corpus consisting of the following two sentences:
1. Please call the number below.
2. Please do not call us.
What are the dimensions of the tf-idf matrix?

  • A. (2, 10)
  • B. (2, 16)
  • C. (8, 10)
  • D. (2, 8)

Answer: B

Explanation:
There are 2 sentences, 8 unique unigrams, and 8 unique bigrams, so the result would be (2,16).
The phrases are "Please call the number below" and "Please do not call us." Each word individually (unigram) is "Please," "call," "the," "number," "below," "do," "not," and "us." The unique bigrams are "Please call," "call the," "the number," "number below," "Please do," "do not," "not call," and "call us."

 

NEW QUESTION 31
A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?
MLS-C01-0aed26f30220bc25c564566a5ec09d49.jpg

  • A. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
  • B. The precision of the model is 86%, which is greater than the accuracy of the model.
  • C. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
  • D. The precision of the model is 86%, which is less than the accuracy of the model.

Answer: D

 

NEW QUESTION 32
A web-based company wants to improve its conversion rate on its landing page Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker However there is an overfitting problem training data shows 90% accuracy in predictions, while test data shows 70% accuracy only The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?

  • A. Allocate a higher proportion of the overall data to the training dataset
  • B. Apply L1 or L2 regularization and dropouts to the training.
  • C. Reduce the number of layers and units (or neurons) from the deep learning network.
  • D. Increase the randomization of training data in the mini-batches used in training.

Answer: B

Explanation:
If this is a ComputerVision problem augmentation can help and we may consider A an option. However in analyzing customer historic data, there is no easy way to increase randomization in training. If you go deep into modelling and coding. When you build model with tensorflow/pytorch, most of the time the trainloader is already sampling in data in random manner (with shuffle enable). What we usually do to reduce overfitting is by adding dropout.
https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html

 

NEW QUESTION 33
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