FreeCourse Logo
FreeCourse.io
Verified CouponsFree CoursesJobsBlog
Categories
Home/Courses/Data Structures & Algorithms (Python): Practice Exams
Data Structures & Algorithms (Python): Practice Exams
Development100% OFF

Data Structures & Algorithms (Python): Practice Exams

Udemy Instructor
0(634 students)
Self-paced
All Levels

About this course

Writing code that works is easy; writing code that scales to millions of users without crashing the server is what separates junior developers from senior engineers. Welcome to the Python Data Structures & Algorithms (DSA) practice assessments! If you are applying for a software engineering role at any major tech company, you will face a technical coding interview.

Interviewers do not just want to see if you can solve the problem—they want to see if you can solve it efficiently. This comprehensive practice test course provides you with 200 realistic, highly technical questions modeled directly after the core conceptual questions asked in FAANG (Facebook, Amazon, Apple, Netflix, Google) technical screens. Across these four rigorous practice exams, you will face scenario-based engineering challenges.

You will determine the most mathematically efficient way to implement undo/redo states for graphic design tools (Stacks), find the shortest path in logistics routing apps (BFS/Graphs), and optimize high-frequency trading matching engines. The questions in this course cut through the fluff and dive straight into computer science fundamentals. You will be challenged on the exact Big O complexity of Merge Sort, the structural rules of a Binary Search Tree, and the memory trade-offs of using Hash Maps.

If you are preparing to tackle LeetCode problems, gearing up for an intense whiteboard interview, or simply wanting to write faster, cleaner Python code, this is your ultimate testing ground. Enroll today and start optimizing!

Skills you'll gain

Software EngineeringEnglish

Available Coupons

Loading...

Course Information

Level: All Levels

Suitable for learners at this level

Duration: Self-paced

Total course content

Instructor: Udemy Instructor

Expert course creator

This course includes:

  • 📹Video lectures
  • 📄Downloadable resources
  • 📱Mobile & desktop access
  • 🎓Certificate of completion
  • ♾️Lifetime access
$0$102.99

Save $102.99 today!

Enroll Now - Free

Redirects to Udemy • Limited free enrollments

Share this course

https://freecourse.io/courses/data-structures-algorithms-python-practice-exams

You May Also Like

Explore more courses similar to this one

500+ AI System Design & Architecture Interview Q&A
Development
0% OFF

500+ AI System Design & Architecture Interview Q&A

Udemy Instructor

AI System Design and Architecture Interview Questions and Answers [2026] is a comprehensive practice assessment designed for software engineers, AI/ML engineers, full-stack developers, and software architects preparing for advanced technical interviews focused on modern AI application architecture.As AI-powered applications become increasingly sophisticated, organizations need engineers who can design scalable, reliable, secure, and cost-efficient AI systems. This course helps you strengthen your understanding of AI system design, distributed systems, RAG architecture, Agentic AI, backend infrastructure, and production-ready application development.With 504 carefully structured interview questions and detailed explanations across 6 practice exams, you will explore essential concepts, real-world engineering scenarios, architectural decisions, and technical trade-offs involved in building and operating modern AI-powered applications.What Will You Learn?This course covers a broad range of AI system design and software architecture topics, including:1. RAG and Knowledge Grounding ArchitectureVector database indexing strategies, including HNSW and IVF.Semantic search, metadata filtering, and hybrid retrieval.Chunking strategies and contextual retrieval.Re-ranking versus Top-K retrieval approaches.Hypothetical Document Embeddings (HyDE).Context window management and retrieval optimization.2. AI Agent and Workflow ArchitectureTool calling and function calling architecture.ReAct versus Plan-and-Execute agent patterns.Agent state management and workflow orchestration.Self-correction and reflection mechanisms.Agent orchestration concepts using frameworks such as LangChain and AutoGen.Handling tool failures and maintaining reliable agent execution.3. Distributed Systems and ScalabilityIdempotency in asynchronous AI workflows.Distributed state management and consensus concepts.Backpressure management in LLM service queues.Service mesh architecture for AI microservices.Multi-tenant rate limiting and API key management.Circuit breakers for external LLM integrations.4. System Reliability and Cost OptimizationLLM usage tracking and cost attribution across tenants.Model routing and fallback strategies.Token usage monitoring and token drift detection.Graceful degradation during LLM API outages.Synchronous versus asynchronous AI processing.Inference endpoint health checks and reliability.5. Data Persistence and Backend API DesignVector databases versus relational databases.Transactional consistency across metadata and vector stores.Database indexing and query optimization.REST API design for asynchronous job management.OAuth2, JWT, authentication, and authorization.API Gateway responsibilities in multi-tenant AI systems.6. Full-Stack Development and Operational ArchitectureClient-side state management for streaming LLM responses.Frontend and backend integration.CORS, authentication flows, and session management.RBAC enforcement across application services.CI/CD strategies for prompt and configuration versioning.XSS prevention and AI application security.GPU-backed inference load balancing.Observability through metrics, logs, and distributed tracing.Practice Exam Structure504Total interview questions6Practice exams84Questions per examAdvancedDifficulty levelSix structured practice examsPractice Exam 1 – 84 QuestionsPractice Exam 2 – 84 QuestionsPractice Exam 3 – 84 QuestionsPractice Exam 4 – 84 QuestionsPractice Exam 5 – 84 QuestionsPractice Exam 6 – 84 QuestionsWhy Take This Course?504 Interview Questions: Explore a broad collection of AI system design and architecture interview questions.Detailed Explanations: Understand the reasoning behind correct answers, architectural choices, limitations, and technical trade-offs.Real-World Scenarios: Practice evaluating production challenges involving scalability, reliability, security, and performance.Advanced Technical Coverage: Strengthen your knowledge of RAG, AI agents, distributed systems, LLM infrastructure, and full-stack integration.Architecture Decision-Making: Develop a better understanding of technology selection, system boundaries, and infrastructure design.Interview Preparation: Identify knowledge gaps and improve your ability to explain complex technical concepts during interviews.Who Should Enroll?This course is particularly useful for:Senior Software Engineers.Principal Software Architects.Senior Full-Stack Developers.AI/ML Systems Engineers.Generative AI Developers.Backend and Distributed Systems Engineers.Technical Leads and Engineering Managers.Professionals preparing for advanced AI system design interviews.Prepare for Modern AI Architecture InterviewsWhether you are preparing for a Senior Software Engineer, Principal Architect, AI/ML Engineer, or Technical Lead interview, this course provides structured opportunities to review important concepts and practice explaining complex architectural decisions.Work through all six practice exams, review the detailed explanations, and strengthen your understanding of scalable AI applications, RAG systems, agent architectures, distributed infrastructure, and production engineering.Enroll today and take the next step in strengthening your AI System Design and Software Architecture interview preparation!

0.0•5•Self-paced
FREE$85.99
Enroll
AI-Powered SDLC: Requirements to Production
Development
0% OFF

AI-Powered SDLC: Requirements to Production

Udemy Instructor

AI-Powered SDLC: Requirements to ProductionMaster the modern Software Development Life Cycle (SDLC), Agile software delivery, and practical applications of Generative AI—from business requirements to production and continuous improvement.Software development is changing rapidly. Agile, DevOps, cloud-native technologies, and Generative AI are transforming how software teams plan, design, build, test, deploy, and operate applications.But understanding individual tools is not enough. Successful software delivery requires a complete understanding of the lifecycle, the decisions made at each stage, and how people, processes, architecture, and AI work together.This course provides a structured, end-to-end understanding of modern software development, combining established engineering practices with practical AI-assisted workflows.What You'll LearnThroughout the course, you will explore:Modern Software Development Life Cycle: Understand SDLC models, lifecycle phases, Agile delivery, and the transition toward AI-augmented software engineering.Agile, Scrum, and DevOps: Learn how Agile teams organize work, manage backlogs, plan sprints, and deliver software incrementally.Product Discovery and Requirements Engineering: Translate business needs into product goals, user stories, acceptance criteria, and traceable requirements.AI-Powered Requirements: Explore how Generative AI can assist with requirements analysis, ambiguity detection, story generation, backlog refinement, and documentation.Software Architecture and Solution Design: Understand architectural styles, quality attributes, high-level and low-level design, API contracts, data models, security, and architecture decisions.AI-Assisted Architecture: Explore AI-supported design alternatives, architecture trade-offs, ADRs, diagrams, API design, and threat modeling.Planning and Agile Delivery: Understand estimation, sprint planning, dependency management, capacity planning, and AI-assisted reporting.AI-Assisted Development: Explore coding assistants, code generation, code reviews, refactoring, secure coding, and software development best practices.Testing and Quality Engineering: Learn unit, integration, system, and acceptance testing, including AI-assisted test generation and defect analysis.DevSecOps and CI/CD: Understand automated pipelines, security checks, infrastructure as code, deployment strategies, release management, and rollback.Production Operations and Observability: Explore monitoring, logs, metrics, traces, incident management, troubleshooting, and AI-assisted operational analysis.Security, Governance, and Responsible AI: Understand secure SDLC practices, AI governance, human review, compliance, and accountability.Maintenance and Modernization: Explore technical debt, dependency management, legacy modernization, feedback-driven improvements, and continuous delivery.The Future of Software Engineering: Understand how AI-assisted workflows are evolving toward agentic AI and increasingly automated software delivery.Learn Through the SmartShop Case StudyInstead of treating every SDLC phase as an isolated topic, the course follows SmartShop, a fictional eCommerce application, throughout its software delivery journey.You'll see how a business requirement progresses through product discovery, requirements engineering, architecture, Agile planning, development, testing, deployment, production monitoring, incident management, and continuous improvement.The case study also introduces an AI-powered shopping assistant to illustrate the architectural and operational considerations involved in delivering AI-enabled software features.This connected learning approach helps you understand how the different SDLC activities fit together in real software projects.AI-Powered Software Engineering with Human AccountabilityGenerative AI can accelerate many software engineering activities, but its output must be evaluated carefully.Throughout this course, we follow one important principle:AI proposes. Humans review. Humans decide.You'll learn where AI can improve productivity, where its limitations matter, and why human judgment remains essential for architecture, security, quality, and production decisions.Who Should Take This Course?This course is suitable for:Software developers and engineersSolution architects and software architectsBusiness analysts and product ownersScrum Masters and Agile practitionersQA and software testing professionalsDevOps and platform engineersEngineering managers and technical leadsIT students and aspiring software professionalsTechnology professionals exploring Generative AI in software deliveryWhy Enroll?This course connects the complete software development lifecycle with modern Agile, DevSecOps, and AI-assisted engineering practices.With 108 structured video lectures across 14 sections, you'll build a broader understanding of how software moves from an initial idea into a production system—and how AI can support the teams responsible for delivering it.Whether you want to strengthen your software engineering foundation, understand end-to-end delivery, or explore responsible AI adoption across the SDLC, this course provides a practical learning roadmap.Join me and discover how modern software is designed, developed, delivered, and continuously improved in the AI era.Mohammad NaushadArchitect With Purpose

0.0•1•Self-paced
FREE$98.99
Enroll
500+ PyTorch and TensorFlow Interview Questions and Answers
Development
0% OFF

500+ PyTorch and TensorFlow Interview Questions and Answers

Udemy Instructor

Prepare for technical interviews with 500+ PyTorch and TensorFlow Interview Questions and Answers [2026], a comprehensive interview preparation assessment designed for developers, machine learning engineers, deep learning professionals, and AI architects.This course includes 504 carefully structured interview questions across 6 practice exams, with 84 questions in each exam and detailed explanations to help you understand important concepts, strengthen your technical knowledge, and improve your interview preparation.Whether you are preparing for a Senior Machine Learning Engineer, Full-Stack ML Engineer, Principal AI Architect, or Lead MLOps Engineer position, this assessment helps you evaluate your knowledge of modern deep learning frameworks and their integration into production AI systems.What Will You Learn?PyTorch Fundamentals: Understand tensors, Autograd, computational graphs, nn.Module, gradient computation, tensor operations, and model state management.TensorFlow and Keras: Explore framework architecture, execution models, model development workflows, training mechanisms, and framework-specific capabilities.Model Training and Optimization: Understand GPU utilization, mixed precision training, batch size selection, learning rate scheduling, checkpointing, and distributed training.Hugging Face and Transfer Learning: Explore pre-trained models, fine-tuning, feature extraction, tokenizers, vocabulary management, and zero-shot versus few-shot learning.ML API Development: Understand REST API design, authentication, input validation, asynchronous inference, CORS, and model-serving workflows.System Architecture and MLOps: Learn about Docker, CI/CD pipelines, model deployment, version management, monitoring, rollback strategies, and production readiness.Performance and Scalability: Evaluate inference latency, resource utilization, caching, distributed processing, fault tolerance, and system reliability.Software Engineering for AI: Understand database design, application security, testing strategies, concurrency management, and maintainable ML architecture.What's Included in This Course?The assessment is organized into 6 practice exams containing 504 questions, with 84 questions per exam.The questions cover 28 technical interview categories, including:FundamentalsTechnical KnowledgeConceptual QuestionsPractical Technical QuestionsScenario-Based QuestionsProblem-Solving QuestionsDebugging and TroubleshootingArchitecture and DesignDatabase QuestionsAPI and Backend DevelopmentFrontend DevelopmentFull-Stack IntegrationSecurityPerformance and OptimizationTestingDeployment and DevOpsAdvanced TopicsBehavioral and Project-Based QuestionsTechnology ComparisonsBest Practices and Coding StandardsVersion and Feature-Specific QuestionsScalability and ReliabilityReal-World Project ArchitectureProduction Support and Incident ManagementInterview Follow-Up and Deep-Dive QuestionsPlain-Text Technical QuestionsCode-Free Interview PreparationDetailed Answer ExplanationsWhy Take These Practice Exams?1. Comprehensive Interview PreparationEvaluate your understanding of PyTorch, TensorFlow, Keras, deep learning workflows, and production machine learning architecture.2. Detailed Answer ExplanationsUnderstand not just the correct answers but also the underlying reasoning, technical concepts, practical applications, limitations, and engineering trade-offs.3. Real-World Engineering ScenariosExplore production-oriented situations involving GPU performance, distributed training, model serving, deployment failures, security, and scalability.4. Full-Stack AI Engineering KnowledgeGo beyond deep learning frameworks and explore how ML models integrate with backend services, REST APIs, frontend applications, databases, and cloud infrastructure.5. Senior-Level Technical PreparationPractice questions relevant to Senior ML Engineer, Principal AI Architect, Full-Stack ML Engineer, and Lead MLOps Engineer interviews.6. Structured Practice Across Six ExamsUse six practice exams to assess your knowledge across different technical areas and identify concepts that need further revision.Who Should Take This Course?PyTorch developers preparing for technical interviews.TensorFlow and Keras engineers strengthening their framework knowledge.Machine learning engineers targeting senior engineering positions.Full-Stack ML Engineers working on production AI applications.Deep learning professionals preparing for architecture and system design interviews.MLOps engineers working with model deployment and production infrastructure.Software engineers transitioning into AI and machine learning engineering.Experienced developers preparing for Principal AI Architect and Lead ML Engineer roles.Start Your PyTorch and TensorFlow Interview PreparationDeep learning engineering requires more than knowing how to train a model. Modern AI engineers must understand framework internals, optimization, model serving, distributed systems, security, and production architecture.500+ PyTorch and TensorFlow Interview Questions and Answers [2026] provides a structured way to assess these skills through 504 questions, 6 practice exams, and detailed explanations.Strengthen your technical understanding, identify knowledge gaps, and prepare for challenging deep learning and AI engineering interviews.Enroll today and start practicing!

0.0•11•Self-paced
FREE$91.99
Enroll
FreeCourse LogoFreeCourse

Freecourse.io brings you high-quality online courses with free certificates to help you upskill, boost your career, and achieve your goals anytime, anywhere.

Resources

  • Courses
  • Jobs
  • Categories
  • Features

Company

  • About
  • Blog
  • Contact

Legal

  • Privacy
  • Terms
  • Cookies
  • Licenses

© 2026 FreeCourse. All rights reserved.