**Julia** is a high level, high performance dynamic programming language designed for technical computing. It brings together speed via **LLVM-based JIT** compilation, multiple dispatch, and rich numerical libraries with a friendly syntax. The upcoming MCQ set targets core competencies: language syntax and semantics, type system and type inference, memory management, and practical usage in data science workflows. The exam structure includes five questions that cover performance boosters like **JIT** and the role of the **Pkg** package manager, array handling and vector creation, and common Julia features such as matrix literals and vector construction. You will be tested on fundamental concepts and practical recall through concise questions and clear distractors. Understanding how **Julia** achieves speed with just-in-time compilation and how to manage packages with **Pkg** is essential for data scientists who rely on rapid prototyping and scalable code. The notes emphasize the importance of learning **arrays** and **vectors**, the difference between dynamic typing and optional type annotations, and the use of **LLVM** as the backend for performance. Regular practice with MCQs reinforces memory, improves problem-solving pace, and clarifies misconceptions about common pitfalls in Julia programming. After completing these questions, learners should feel more confident applying Julia to data analysis, machine learning, and scientific computing tasks. Regular review and hands-on coding will complement this knowledge and help solidify best practices in Julia development.