Associate Computer Scientist (ACS) Practice Exam

1. Fundamentals of Computer Science • Overview of Computer Science o Definition and scope of computer science o Historical development and evolution of computing o Applications of computer science in modern industries • Computer Hardware Basics o Types of computer systems (mainframes, personal computers, embedded systems) o Basic components: CPU, RAM, storage devices, input/output devices o Data representation (binary, hexadecimal systems) o Understanding of buses, clocks, and logic gates • Software and Programming o Overview of software development life cycle (SDLC) o Types of software: system software, application software, development tools o Introduction to programming languages (machine, assembly, high-level languages) • Algorithms and Problem Solving o Definition of an algorithm, problem-solving strategies o Time complexity and space complexity (Big O notation) o Sorting and searching algorithms (QuickSort, MergeSort, Binary Search) o Introduction to recursion and iterative processes 2. Programming and Software Development • Programming Concepts o Variables, data types, and operators o Control structures: conditional statements, loops, and functions o Exception handling and debugging techniques • Object-Oriented Programming (OOP) o Basic OOP concepts: classes, objects, methods, attributes o Principles of OOP: Encapsulation, Inheritance, Polymorphism, Abstraction o Object-oriented design patterns (Singleton, Factory, Observer) • Data Structures o Arrays, Linked Lists, Stacks, Queues, Trees, Graphs o Operations on data structures: insertion, deletion, traversal, search, and sort o Hash tables, Priority Queues, Heaps • Algorithms o Search algorithms: linear search, binary search, breadth-first search (BFS), depth-first search (DFS) o Sorting algorithms: bubble sort, selection sort, insertion sort, merge sort, quicksort o Greedy algorithms, dynamic programming, and divide and conquer • Software Development Methodologies o Waterfall vs Agile development processes o Overview of SCRUM, Kanban, and DevOps practices o Continuous Integration/Continuous Deployment (CI/CD) o Version control systems (Git, GitHub) 3. Computer Systems and Architecture • System Architecture o The Von Neumann architecture: CPU, Memory, I/O o Caches, Registers, and Memory hierarchy o Pipelining and parallel processing techniques o Operating system principles and interactions with hardware • Operating Systems o Types of operating systems: batch, real-time, distributed, multi-user o Process management, multitasking, and concurrency o Memory management techniques: paging, segmentation, virtual memory o File systems and disk management o Input/Output systems: device drivers, interrupt handling • Computer Networks o Basic networking concepts: IP addresses, subnets, routers, switches o OSI model and TCP/IP protocols o Network topologies, LAN, WAN, VPN o Networking protocols: HTTP, FTP, DNS, TCP, UDP o Security aspects: encryption, firewalls, VPNs, authentication methods 4. Databases and Data Management • Database Fundamentals o Definition and importance of databases o Relational database management systems (RDBMS): MySQL, PostgreSQL, SQL Server o Non-relational databases (NoSQL): MongoDB, Cassandra, Redis o Data models: hierarchical, network, relational, and object-oriented • SQL and Query Design o Writing SQL queries (SELECT, INSERT, UPDATE, DELETE) o Advanced SQL topics: JOINs, Subqueries, Indexing, Views o Normalization and denormalization o Transactions, ACID properties (Atomicity, Consistency, Isolation, Durability) • Database Design and Management o Entity-Relationship (ER) diagram creation and interpretation o Database normalization and denormalization principles o Data integrity constraints: Primary keys, Foreign keys, Unique constraints o Database backups, recovery, and security • Big Data and Cloud Databases o Introduction to Big Data frameworks (Hadoop, Spark) o Data warehousing and cloud databases (AWS, Google Cloud, Azure) o NoSQL database advantages and use cases o Data replication, sharding, and partitioning in large-scale databases 5. Software Engineering Principles • Software Design and Architecture o Principles of software architecture (modularity, scalability, maintainability) o Design patterns: MVC, Observer, Singleton, Factory, Strategy o User interface (UI) and user experience (UX) design principles • Software Testing and Quality Assurance o Unit testing, integration testing, and system testing o Testing frameworks and tools (JUnit, Selenium, Postman) o Software performance testing: load, stress, and scalability tests o Bug tracking, issue management, and test automation • Software Project Management o Project planning, resource allocation, and task tracking o Risk management and mitigation strategies o Requirements gathering, documentation, and prototyping o Managing scope creep, stakeholder communication, and project timelines 6. Artificial Intelligence and Machine Learning • Overview of Artificial Intelligence o Definition of AI, types of AI (narrow AI, general AI) o Applications of AI: Natural Language Processing, robotics, autonomous systems o Ethics in AI: bias, fairness, accountability • Machine Learning Basics o Types of machine learning: supervised, unsupervised, reinforcement learning o Data preprocessing, feature selection, and feature extraction o Training, validation, and testing of models • Popular Machine Learning Algorithms o Linear regression, logistic regression, decision trees o K-means clustering, Naive Bayes, Support Vector Machines (SVM) o Neural Networks, Deep Learning, and Convolutional Neural Networks (CNN) • AI Programming Languages and Libraries o Python and libraries: NumPy, Pandas, Scikit-learn, TensorFlow, Keras o R programming for machine learning o Tools for AI: Jupyter notebooks, Apache Spark, IBM Watson 7. Cybersecurity • Introduction to Cybersecurity o Basic principles of cybersecurity: confidentiality, integrity, availability (CIA) o Types of cyber threats: malware, phishing, denial-of-service (DoS), man-in-the-middle attacks o Cybersecurity best practices: firewalls, encryption, multi-factor authentication • Cryptography o Symmetric vs asymmetric encryption o Hashing algorithms: SHA, MD5 o Public Key Infrastructure (PKI), SSL/TLS protocols o Digital signatures and certificates • Network Security o Intrusion detection and prevention systems (IDS/IPS) o Virtual Private Networks (VPNs), Secure Socket Layer (SSL) o Security protocols: HTTPS, IPsec, SSH • Ethical Hacking and Penetration Testing o Phases of penetration testing: reconnaissance, scanning, exploitation, post-exploitation o Common hacking tools: Nmap, Wireshark, Metasploit o Ethical hacking laws and responsible disclosure 8. Web Development • Front-End Development o HTML, CSS, and JavaScript fundamentals o Responsive web design principles o Client-side frameworks: React, Angular, V o Browser compatibility and debugging techniques • Back-End Development o Server-side programming languages: N, Python, Java, Ruby o Web frameworks: Django, Flask, E, Ruby on Rails o RESTful API design and implementation o Database connections and ORM (Object-Relational Mapping) • Web Security o Web application vulnerabilities: SQL injection, XSS, CSRF o Web security best practices: input validation, authentication, HTTPS o Secure coding practices and vulnerability assessment tools • Web Hosting and Deployment o Web hosting services: shared, VPS, dedicated, cloud hosting o Continuous Deployment/Continuous Integration (CI/CD) tools o Domain management, DNS, and server configurations 9. Emerging Technologies • Blockchain Technology o Definition and overview of blockchain o How blockchain works: decentralization, consensus algorithms o Cryptocurrencies and smart contracts (Ethereum, Bitcoin) o Blockchain applications in finance, healthcare, and supply chains • Internet of Things (IoT) o IoT devices and applications in smart homes, healthcare, and manufacturing o Communication protocols for IoT: MQTT, HTTP, CoAP o Security concerns and challenges in IoT systems • Cloud Computing o Overview of cloud computing models: IaaS, PaaS, SaaS o Major cloud service providers: AWS, Google Cloud,