SCJP Exam for J2SE 5 : A Concise and Comprehensive Study Guide for The Sun Certified Java Programmer Exam

SCJP Exam for J2SE 5 : A Concise and Comprehensive Study Guide for The Sun Certified Java Programmer Exam

Author
Paul Sanghera
Publish Year
Publisher
Language
Document Type
Main Subject
ISBN

Best selling author Paul Sanghera offers cohesive, concise, yet comprehensive coverage of all the topics included in the Sun Certified Programmer for Java 5 exam (CX 310-055). With a laser sharp focus on the exam objectives, this study guide goes beyond just being an "exam cram." The material is presented in a logical learning sequence: a section builds upon previous sections and a chapter on previous chapters. All concepts, simple and complex, are defined and explained when they appear the first time. There is no hopping from topic to topic and no technical jargon without explanation. No prior knowledge of Java programming is assumed. The single most difficult aspect of this exam is to read and understand the code in the exam questions in a limited amount of time. To help you get fluent and comfortable with the code, the book offers complete runnable code examples distributed over all the chapters and a codewalk quicklet feature at the end of each chapter.



Related Books

img

Scalable data management for future hardware

Presents the results of the DFG priority program on scalable data management for future hardware. It details requirements and solutions of how modern and future hardware architectures can be leveraged to address the challenges in modern data management.the nine chapters of the book present a wide range of data management architectures in conjunction with current hardware developments, often related to applications in data analytics or machine learning. they cover topics such as hardware-accelerated query or event processing on FPGA, GPU, and multicore CPUs, scalable data management in data center networks or on modern memory and storage technologies, and operating system support.

img

New challenges in software engineering ; Vol 1

Explores the key challenges shaping the future of software development, including automation, AI-driven development, security-focused engineering, resilient and autonomous architectures, business process optimization, cloud computing, microservices, high-performance distributed systems, and sustainable technologies. Software engineering is undergoing a constant transformation, driven by rapid technological advances and evolving market demands. additionally, it delves into the ethical considerations of AI, the evolution of intuitive user interfaces, and the importance of multidisciplinary collaboration.

img

Fundamentals of manufacturing engineering using digital visualization

Offers a guide to core principles and practices of manufacturing engineering. It covers the design of, together with technological and measurement issues for, technical systems. Locating charts and setup schemes describing different machining processes are included. Concepts of product quality, with a focus on accuracy indicators, machining accuracy, roughness, and the impact of surface quality on exploitation properties are also explained. Furthermore, key machining methods, including turning, milling, hole machining, grinding, and gear machining, are analyzed in depth, covering their principles, applications, and techniques. The book is enriched by QR codes, linking to a mobile application presenting additional information about the content, for an interactive and extended learning experience. It also uses illustrations visualized with digital tools to promote a better understanding of the concepts.

img

AI in drug discovery

Constitutes the refereed proceedings of the First international workshop on ai in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024, in Lugano, Switzerland, on September 19, 2024. These papers focus on various aspects of the rapidly evolving field of Artificial Intelligence (AI)-driven drug discovery in chemistry, including Big Data and advanced Machine Learning, eXplainable AI (XAI), Chemoinformatics, Use of deep learning to predict molecular properties, Modeling and prediction of chemical reaction data and Generative models.