Topic 1 Question 65
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?
Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.
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Ans: C; Normalization is correct
👍 34rsimham2021/09/19Guys, I passed the exam today. It is a tough one but there are many questions here. Good luck everyone! Thank examtopics
👍 12Phong2021/10/19QUESTION 66 A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena. The dataset contains more than 800,000 records stored as plaintext CSV files. Each record contains 200 columns and is approximately 1.5 MB in size. Most queries will span 5 to 10 columns only. How should the Machine Learning Specialist transform the dataset to minimize query runtime? A. Convert the records to Apache Parquet format. B. Convert the records to JSON format. C. Convert the records to GZIP CSV format. D. Convert the records to XML format. Correct Answer: A
👍 11nez152021/09/21
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