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AI-900: Optimizing Model Training Essential Data Transformation Steps

Explore the fundamental stages of data transformation crucial for effective model training. Learn how cleaning, scaling, encoding, and engineering refine AI model performance.

Table of Contents

Question

What are the four typical steps of data transformation for model training?

A. Feature selection
B. Finding and removing data outliers
C. Split data
D. Impute missing values
E. ML algorithm selection
F. Normalize numeric features

Answer

A. Feature selection
B. Finding and removing data outliers
D. Impute missing values
F. Normalize numeric features

Explanation

After we ingest the data, we need to do a data preparation or transformation before supplying it for model training. There are four typical steps for data transformation such as Feature selection, Finding and removing data outliers, Impute missing values, and Normalize numeric features.

Split data is coming after data transformation.

ML algorithm selection data is coming after data transformation and Split Data steps.

After we ingest the data, we need to do a data preparation or transformation before supplying it for model training. There are four typical steps for data transformation: Feature selection, Finding and removing data outliers, Impute missing values, and Normalize numeric features.

Option C is incorrect because Split data is coming after data transformation.
Option D is incorrect because ML algorithm selection data is coming after data transformation and Split Data steps.

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump

Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook

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