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[General] Amazon MLS-C01 Valid Exam Labs, MLS-C01 Test Labs

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【General】 Amazon MLS-C01 Valid Exam Labs, MLS-C01 Test Labs

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The AWS Certified Machine Learning - Specialty certification exam is a valuable credential for individuals who want to demonstrate their proficiency in designing, deploying, and managing machine learning solutions on the AWS platform. AWS Certified Machine Learning - Specialty certification demonstrates that you have the skills and knowledge necessary to work with AWS machine learning services and provides you with a competitive edge in the job market.
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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q68-Q73):NEW QUESTION # 68
A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may fraudulent.
How should the Specialist frame this business problem?
  • A. Regression classification
  • B. Binary classification
  • C. Streaming classification
  • D. Multi-category classification
Answer: B
Explanation:
The business problem of predicting whether a new credit card applicant will default on a credit card payment can be framed as a binary classification problem. Binary classification is the task of predicting a discrete class label output for an example, where the class label can only take one of two possible values. In this case, the class label can be either "default" or "no default", indicating whether the applicant will or will not default on a credit card payment. A binary classification model can return the probability that a given applicant belongs to each class, and then assign the applicant to the class with the highest probability. For example, if the model predicts that an applicant has a 0.8 probability of defaulting and a 0.2 probability of not defaulting, then the model will classify the applicant as "default". Binary classification is suitable for this problem because the outcome of interest is categorical and binary, and the model needs to return the probability of each outcome.
References:
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Classification vs Regression in Machine Learning

NEW QUESTION # 69
A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below.
Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values.
What technique should be used to convert this column to binary values.

  • A. Normalization transformation
  • B. Binarization
  • C. Tokenization
  • D. One-hot encoding
Answer: D

NEW QUESTION # 70
A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist needs to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false negative predictions by the model? (Choose two.)
  • A. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
  • B. Change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC).
  • C. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
  • D. Change the XGBoost eval_metric parameter to optimize based on Root Mean Square Error (RMSE).
  • E. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
Answer: B,E
Explanation:
The Data Scientist should increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights and change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC). This will help reduce the number of false negative predictions by the model.
The scale_pos_weight parameter controls the balance of positive and negative weights in the XGBoost algorithm. It is useful for imbalanced classification problems, such as fraud detection, where the number of positive examples (fraudulent transactions) is much smaller than the number of negative examples (non-fraudulent transactions). By increasing the scale_pos_weight parameter, the Data Scientist can assign more weight to the positive class and make the model more sensitive to detecting fraudulent transactions.
The eval_metric parameter specifies the metric that is used to measure the performance of the model during training and validation. The default metric for binary classification problems is the error rate, which is the fraction of incorrect predictions. However, the error rate is not a good metric for imbalanced classification problems, because it does not take into account the cost of different types of errors. For example, in fraud detection, a false negative (failing to detect a fraudulent transaction) is more costly than a false positive (flagging a non-fraudulent transaction as fraudulent). Therefore, the Data Scientist should use a metric that reflects the trade-off between the true positive rate (TPR) and the false positive rate (FPR), such as the Area Under the ROC Curve (AUC). The AUC is a measure of how well the model can distinguish between the positive and negative classes, regardless of the classification threshold. A higher AUC means that the model can achieve a higher TPR with a lower FPR, which is desirable for fraud detection.
References:
XGBoost Parameters - Amazon Machine Learning
Using XGBoost with Amazon SageMaker - AWS Machine Learning Blog

NEW QUESTION # 71
A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.

How should the data scientist split the dataset into a training and test set for this use case?
  • A. Randomly select 10% of the users. Split off all interaction data from these users for the test set.
  • B. Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.
  • C. Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.
  • D. Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.
Answer: C
Explanation:
https://aws.amazon.com/blogs/mac ... n-amazon-sagemaker/

NEW QUESTION # 72
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?

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

NEW QUESTION # 73
......
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