Self-Assessment
During my internship at First Cloud Journey (FCJ) - AWS Study Group from September 8, 2025 to December 9, 2025, I had the opportunity to learn, practice, and apply AWS cloud computing knowledge to real-world projects. Over 12 weeks, I progressed from basic AWS fundamentals to deploying a complete AI-powered Chatbot system for consultant management using Amazon Bedrock, Lambda, RDS, S3, DynamoDB, API Gateway, CloudFront, and AWS CDK.
Summary of Learning Journey
Phase 1 (Weeks 1-3): AWS Fundamentals & Core Services
- Mastered AWS account management with IAM best practices, CLI/Console operations, and cost optimization using Budgets and Spot Instances
- Deep understanding of AWS Networking: VPC, Subnets, Security Groups, NACL, VPN, Transit Gateway, Load Balancing
- Hands-on with EC2, Auto Scaling Groups, CloudWatch monitoring, and Tags & Resource Groups
- Learned AWS Storage services: S3, Storage Gateway, AWS Backup, FSx, and EFS
- Gained foundational knowledge in AWS Security: IAM Roles, Policies, Permission Boundaries, and KMS encryption
Phase 2 (Weeks 4-5): Security, Identity & Databases
- Advanced IAM concepts: Cognito, AWS Organizations, Identity Center (SSO), and Condition Keys
- Practiced database fundamentals: OLTP vs OLAP, RDBMS vs NoSQL
- Worked with AWS databases: Amazon RDS, Redshift, ElastiCache, and DynamoDB
- Completed hands-on labs for Security Hub, KMS Workshop, and tag-based access control
- Translated 3 technical AWS blog posts to improve technical documentation skills
Phase 3 (Weeks 6-9): Architecture Design & Project Planning
- Learned to design AWS architectures using draw.io and Q Developer CLI
- Studied RAG-based chatbot architectures and AI Agents from AWS Solutions Library
- Attended “Data Science on AWS” workshop to expand knowledge of ML pipelines
- Completed midterm exam covering all AWS service groups
- Designed and proposed a complete chatbot architecture using Bedrock, Lambda, and serverless components
- Estimated project costs using AWS Pricing Calculator
Phase 4 (Weeks 10-12): Full-Stack Deployment & Infrastructure as Code
- Deployed complete infrastructure using AWS CDK (VPC, RDS, Lambda, S3, API Gateway, CloudFront, Cognito)
- Implemented Admin Dashboard with authentication (Cognito), database management (RDS), and analytics (Athena DDL)
- Built Messenger Bot integration with Meta Developers webhook
- Created automated RDS ↔ S3 archiving mechanism with checksum optimization to reduce costs
- Refactored backend code with clean service-layer architecture for maintainability
- Managed EventBridge scheduling for automated data archiving
- Optimized deployment process and resource management through multiple iterations
Throughout the internship, I improved my skills in cloud architecture design, infrastructure as code (CDK), serverless computing, database management, security best practices, cost optimization, technical documentation, and English translation.
I always strived to complete tasks on time, actively researched solutions to technical challenges, and collaborated effectively with team members to deliver a production-ready AWS solution.
Self-Evaluation Based on Key Criteria
| No. | Criteria | Description | Good | Fair | Average |
|---|
| 1 | Professional knowledge & skills | Understanding of the field, applying knowledge in practice, proficiency with tools, work quality | ✅ | ☐ | ☐ |
| 2 | Ability to learn | Ability to absorb new knowledge and learn quickly | ✅ | ☐ | ☐ |
| 3 | Proactiveness | Taking initiative, seeking out tasks without waiting for instructions | ✅ | ☐ | ☐ |
| 4 | Sense of responsibility | Completing tasks on time and ensuring quality | ✅ | ☐ | ☐ |
| 5 | Discipline | Adhering to schedules, rules, and work processes | ✅ | ☐ | ☐ |
| 6 | Progressive mindset | Willingness to receive feedback and improve oneself | ✅ | ☐ | ☐ |
| 7 | Communication | Presenting ideas and reporting work clearly | ✅ | ☐ | ☐ |
| 8 | Teamwork | Working effectively with colleagues and participating in teams | ✅ | ☐ | ☐ |
| 9 | Professional conduct | Respecting colleagues, partners, and the work environment | ✅ | ☐ | ☐ |
| 10 | Problem-solving skills | Identifying problems, proposing solutions, and showing creativity | ✅ | ☐ | ☐ |
| 11 | Contribution to project/team | Work effectiveness, innovative ideas, recognition from the team | ✅ | ☐ | ☐ |
| 12 | Overall | General evaluation of the entire internship period | ✅ | ☐ | ☐ |
Detailed Evaluation
Strengths Achieved:
Professional Knowledge & Skills (Good)
- Successfully mastered AWS services across all domains: Compute, Storage, Networking, Database, Security, Analytics, and AI/ML
- Proficient in using AWS CLI, Console, and CDK for infrastructure deployment
- Demonstrated ability to design, implement, and optimize cloud architectures
- Completed a production-ready chatbot system from scratch using 10+ AWS services
Ability to Learn (Good)
- Quickly absorbed complex AWS concepts and applied them in practice
- Self-studied advanced topics like CDK, Bedrock, Athena DDL, and serverless architectures
- Adapted to changing project requirements and pivoted architecture decisions (e.g., Glue → Athena DDL)
Proactiveness (Good)
- Actively proposed improvements to reduce costs (DynamoDB cache instead of OpenSearch, checksum mechanism for S3)
- Researched and suggested replacing Lex with Custom Webhook + Bedrock for more natural responses
- Took initiative to translate technical documents and create detailed documentation
Sense of Responsibility (Good)
- Consistently completed weekly tasks on schedule
- Maintained detailed worklogs and documentation for all 12 weeks
- Ensured system stability through proper error handling and logging
Discipline (Good)
- Consistently adhered to weekly schedules and delivered worklogs on time for all 12 weeks
- Followed deployment processes and AWS best practices throughout the project
- Maintained organized documentation and version control practices
- Respected team meeting times and internship program requirements
Progressive Mindset (Good)
- Welcomed feedback during team meetings and adjusted architecture accordingly
- Continuously improved code quality through refactoring and service-layer separation
- Learned from mistakes (e.g., VPC multi-AZ requirements, Lambda VPC limitations)
Teamwork (Good)
- Collaborated effectively during team meetings to finalize chatbot direction
- Participated in architecture reviews and incorporated team suggestions
- Shared knowledge through detailed Notion documentation
Communication (Good)
- Created comprehensive written documentation for all 12 weeks of learning
- Translated 3 technical AWS blog posts, demonstrating strong English comprehension
- Effectively presented architecture proposals and cost estimates to the team
- Maintained clear and detailed Notion notes for knowledge sharing
- Communicated technical decisions and trade-offs during team meetings
Professional Conduct (Good)
- Respected team members and maintained professional communication
- Followed AWS best practices and security standards
- Adhered to internship guidelines and program structure
Problem-Solving Skills (Good)
- Resolved technical challenges: timeout issues with Vietnamese prompts, Glue Catalog + Lambda VPC conflicts
- Designed checksum mechanism to optimize S3 costs and reduce unnecessary uploads
- Created data synchronization logic to handle dynamic data (appointments) vs static data (consultants)
- Contribution to Project/Team (Good)
- Delivered a complete, deployable chatbot solution with admin dashboard
- Created comprehensive architecture documentation and cost estimates
- Proposed and implemented cost-saving optimizations
Areas for Continuous Improvement:
While I achieved strong performance across all criteria, I recognize there is always room for growth:
Time Management
- Could further optimize the balance between learning multiple AWS services simultaneously and deep-diving into specific topics
- Opportunity to improve estimation skills for complex deployment tasks
Technical Communication
- While written documentation is comprehensive, I can continue improving verbal explanation of complex architectures to non-technical audiences
- Practice presenting technical trade-offs more concisely in time-constrained meetings
Proactive Problem Prevention
- Although I successfully resolved issues, I can develop better anticipation of potential problems before they occur
- Strengthen pre-deployment validation processes to catch configuration issues earlier
Key Lessons Learned
- Infrastructure as Code (IaC): CDK provides powerful abstraction for AWS resources, but requires careful dependency management and understanding of CloudFormation
- Cost Optimization: Small architectural decisions (Glue vs Athena DDL, checksum mechanism) can significantly impact costs
- Serverless Architecture: Lambda + API Gateway + S3 provides scalable, cost-effective solutions but requires proper VPC and IAM configuration
- Security Best Practices: Always use IAM roles instead of access keys, implement least-privilege policies, and enable encryption at rest
- Iterative Development: Complex systems require multiple iterations; don’t aim for perfection in the first deployment
Future Development Goals
- Expand expertise in AI/ML services (SageMaker, Bedrock, Comprehend) for advanced chatbot capabilities
- Study DevOps practices (CI/CD pipelines, automated testing, monitoring with CloudWatch/X-Ray)
- Contribute to open-source AWS projects and share knowledge through blog posts
- Explore multi-region architectures and disaster recovery strategies
Conclusion: This 12-week internship at First Cloud Journey provided invaluable hands-on experience with AWS cloud services and real-world project development. I successfully transformed from an AWS beginner to being capable of designing and deploying production-ready cloud solutions. The knowledge and skills gained will serve as a strong foundation for my career in cloud computing.