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NEW QUESTION 48
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

• A. Mean absolute percentage error (MAPE)
• B. Area Under the ROC Curve (AUC)
• C. Recall
• D. Misclassification rate

Answer: C

Explanation:
Explanation/Reference: https://docs.aws.amazon.com/machine-learning/latest/dg/multiclass-model-insights.html

NEW QUESTION 49
A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions - Here is an example from the dataset
"The quck BROWN FOX jumps over the lazy dog "
Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Select THREE)

• A. Correct the typography on "quck" to "quick."
• B. One-hot encode all words in the sentence
• C. Tokenize the sentence into words.
• D. Remove stop words using an English stopword dictionary.
• E. Normalize all words by making the sentence lowercase
• F. Perform part-of-speech tagging and keep the action verb and the nouns only

Answer: C,D,E

NEW QUESTION 50
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS Which approach should the Specialist use for training a model using that data?

• A. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
• B. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in
• C. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
• D. Write a direct connection to the SQL database within the notebook and pull data in

Answer: A

NEW QUESTION 51
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers. Currently, the company has the following data in Amazon Aurora:
* Profiles for all past and existing customers
* Profiles for all past and existing insured pets
* Policy-level information
* Premiums received
* Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?

• A. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
• B. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
• C. Use regression on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
• D. Use clustering on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

Answer: B

NEW QUESTION 52
A global financial company is using machine learning to automate its loan approval process. The company has a dataset of customer information. The dataset contains some categorical fields, such as customer location by city and housing status. The dataset also includes financial fields in different units, such as account balances in US dollars and monthly interest in US cents.
The company's data scientists are using a gradient boosting regression model to infer the credit score for each customer. The model has a training accuracy of 99% and a testing accuracy of 75%. The data scientists want to improve the model's testing accuracy.
Which process will improve the testing accuracy the MOST?

• A. Use a one-hot encoder for the categorical fields in the dataset. Perform standardization on the financial fields in the dataset. Apply L1 regularization to the data.
• B. Use tokenization of the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Remove the outliers in the data by using the z-score.
• C. Use a logarithm transformation on the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Use imputation to populate missing values in the dataset.
• D. Use a label encoder for the categorical fields in the dataset. Perform L1 regularization on the financial fields in the dataset. Apply L2 regularization to the data.

Answer: B

NEW QUESTION 53
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