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Understanding functional and technical aspects of AWS Certified Machine Learning - Specialty Modeling

The following will be discussed in AMAZON MLS-C01 exam dumps:

• Frame business problems as machine learning problems
• Train machine learning models
• Perform hyperparameter optimization
• Select the appropriate model(s) for a given machine learning problem
• Evaluate machine learning models

The AWS Machine Learning Specialty certification is designed for developers and data scientists who want to enhance their skills in using machine learning via the AWS platform. >> MLS-C01 Exam Practice <<

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q157-Q162):

NEW QUESTION # 157
A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents How should a Machine Learning Specialist address this issue for future documents'?

• A. Convert current documents to SSML with pronunciation tags
• B. Create an appropriate pronunciation lexicon.
• C. Output speech marks to guide in pronunciation
• D. Use Amazon Lex to preprocess the text files for pronunciation

Answer: A
NEW QUESTION # 158
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 precision of the model is 86%, which is greater than the accuracy of the model.
• B. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
• C. The precision of the model is 86%, which is less than the accuracy of the model.
• 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: C
NEW QUESTION # 159
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?

• A. Uniform distribution
• B. Normal distribution
• C. Binomial distribution
• D. Poisson distribution

Answer: C
NEW QUESTION # 160
Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?

• A. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
• B. Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.
• C. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
• D. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.

Answer: C Explanation:
Explanation/Reference: https://docs.aws.amazon.com/machine-learning/latest/dg/data-transformations-reference.html
NEW QUESTION # 161
A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on 400 patients randomly selected from the population. The disease is seen in 3% of the population.
Which cross-validation strategy should the Data Scientist adopt?

• A. A stratified k-fold cross-validation strategy with k=5
• B. A k-fold cross-validation strategy with k=5
• C. An 80/20 stratified split between training and validation
• D. A k-fold cross-validation strategy with k=5 and 3 repeats

Answer: A
NEW QUESTION # 162
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