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Solutions Architect's Interview

Solutions Architect's Interview

By : Saurabh Shrivastava, Sanjeet Sahay, Neelanjali Srivastav, Dhiraj Thakur
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Solutions Architect's Interview

Solutions Architect's Interview

By: Saurabh Shrivastava, Sanjeet Sahay, Neelanjali Srivastav, Dhiraj Thakur

Overview of this book

The Solutions Architect role sits at the intersection of technology, business strategy, and communication. Excelling in it takes more than technical expertise—it demands architectural thinking, leadership, and problem-solving skills. This book guides you in achieving this balance, walking you through every stage of becoming a Solutions Architect. It begins by unpacking what the role truly involves and then progresses into the nuances of interview preparation across multiple domains. You’ll learn how to structure your responses to scenario-based questions, highlight both your technical proficiency and soft skills, and position yourself as a strategic problem solver. Each chapter offers practical exercises, frameworks, and real interview examples that help you build the confidence and insight to stand out. The final chapter guides you through your first 90 days as a new Solutions Architect, aligning with business goals, understanding organizational architecture, and building credibility. By the end of this book, you’ll have the technical, behavioral, and strategic foundation to ace your interviews and thrive in any Solutions Architect role.
Table of Contents (19 chapters)
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1
Part 1: Solutions Architect Role and Interview
5
Part 2: Specialist Solution Architect Role and Interview
14
Part 3: Industry Solutions Architect Role and Interview
16
Part 4: Starting in a new role

Data Preprocessing and Feature Engineering

Creating a robust ML model begins long before any algorithmic magic occurs. It starts with data preprocessing and feature engineering, two critical phases that lay the groundwork for successful ML implementations. In these stages, raw data is transformed into a refined format that ML models can understand and use effectively to make accurate predictions or decisions.

Data Preprocessing

Data is the foundation of ML Models. This initial step involves addressing missing values, removing duplicates, and correcting errors in the dataset. It's like selecting raw ingredients and ensuring they're quality before cooking. Normalization and Standardization techniques scale the data to a standard range or distribution, eliminating biases due to varying scales of features. Data transformation can include encoding categorical variables, discretizing continuous variables, or creating polynomial features. This step ensures that the data is in a format...

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