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How to Prepare for the AWS Certified Machine Learning Specialty Exam

Preparation Guide for For AWS Certified Machine Learning Specialty Exam

Introduction

Amazon Web Services is the current market leader in the field of cloud computing. Many organizations are boarding the AWS train for very promising benefits. Profitability, flexibility, ease of use and comprehensive support are the pillars of AWS's popularity. As AWS gained popularity, many companies began looking for AWS certified professionals.

AWS certified machine learning specialty certification is one of many AWS certifications popular today. AWS provides certification to validate an individual's skills and experience in AWS specific tools, resources and technologies. The following discussion will focus on the details of the AWS certified machine learning specialty certification. The purpose of the following discussion is to support the preparation of candidates for the certification exam.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q175-Q180):

NEW QUESTION # 175
A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among
200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.
What type of machine learning model should be used?

  • A. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
  • B. Classification month-to-month using supervised learning of the 200 categories based on claim contents.
  • C. Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.
  • D. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.

Answer: A


NEW QUESTION # 176
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:
AWS-Certified-Machine-Learning-Specialty-9d5ac3991a66c1e34a897228a0048a77.jpg
Based on the model evaluation results, why is this a viable model for production?

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

Answer: A


NEW QUESTION # 177
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 has been asked to reduce the number of false negatives.
Predicted 0 1
Actual 0 99,966| 34 1 877|123
Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Select TWO.)

  • A. Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.
  • B. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
  • C. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
  • D. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
  • E. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.

Answer: D


NEW QUESTION # 178
An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?

  • A. Create word embedding factors that store edit distance with every other word.
  • B. Download word embedding's pre-trained on a large corpus.
  • C. Create one-hot word encoding vectors.
  • D. Produce a set of synonyms for every word using Amazon Mechanical Turk.

Answer: D


NEW QUESTION # 179
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.
AWS-Certified-Machine-Learning-Specialty-89a4d4f0cfa2a42c64e003efff2bd513.jpg
Based on this information, which model would have the HIGHEST accuracy?

  • A. Long short-term memory (LSTM) model with scaled exponential linear unit (SELU)
  • B. Logistic regression
  • C. Single perceptron with tanh activation function
  • D. Support vector machine (SVM) with non-linear kernel

Answer: B

Explanation:
Explanation/Reference: https://machinelearningmastery.com/logistic-regression-for-machine-learning/


NEW QUESTION # 180
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