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My Pathway to AI Certification
Hi! My name is Igvir, I'm a Computer Science Engineer, I´ll be here "Printing My Working Directory" That's where the name $PWD comes from. Updates, Articles, and Personal Insights about what I´m doing.
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Te cuento sobre mi libro sobre spec-driven-development con Kiro.
A series on AI-DLC, the methodology for AI-driven software development. Drawing from three workshops as a certified practitioner — what compresses, what breaks, and what changes about the developer's job. This is post three of three.
This is the second post in a three-part series on AI-DLC. The first post covered what I saw running three AI-DLC workshops. This one is about the problem AI-DLC tries to solve. The third will get into
Software teams have been promised compression for thirty years. Faster sprints. Better tooling. Lower ceremony. The methodology layer keeps getting renamed and the actual delivery curve barely moves.
Yes, we're already flooded with coding assistants that promise to accelerate development. I myself have been writing extensively on LinkedIn with the results of my research. But I can't ignore the fact that IBM has introduced Project Bob, a tool I ha...
$PWD
18 posts
This is PWD, Print Working Directory, It shows you the complete path of the technology we are currently working in. Check my website https://www.igvir.com/
The AWS Certified AI Practitioner exam is designed to validate knowledge in AI, machine learning (ML), and generative AI concepts and use cases. It is a foundational certification, similar in level to the Cloud Practitioner certification, it is not too complicated but requires some basic expertise, and having prior knowledge of cloud services is beneficial for success in this exam.
The exam consists of 65 questions to be completed in 90 minutes, with a registration fee of $100. It can be taken in person or online. The exam covers five main domains, including:
Machine Learning Basics: Understanding supervised vs. unsupervised learning, classification vs. regression problems, and the machine learning lifecycle from problem framing to model deployment.
Generative AI Basics: Differentiating between generative AI and traditional AI, understanding fine-tuning, retrieval-augmented generation, and reinforcement learning from human feedback.
AWS Services for AI/ML: Familiarity with AWS services like SageMaker, is crucial for machine learning tasks, and understanding their features and use cases.
Other AI/ML Services: Knowledge of additional AWS services relevant to AI and ML, such as Amazon Rekognition for image recognition and Amazon Lex for chatbot development.
General AWS Knowledge: A broad understanding of AWS services beyond AI/ML, including storage solutions like S3 and networking concepts like VPCs, is also important.
It is always recommended to follow a structured study plan. The AWS four-step plan on SkillBuilder is a good starting point for beginners, offering free and paid resources. This course is an excellent option too, Practicing with official questions is crucial, as they provide insights into the exam format and content.
For those with prior experience in ML and AI, starting with practice questions can help identify knowledge gaps before diving deeper into study materials.
Here you have a list of Practice Tests and additional courses:
Exam Prep Official Practice Question Set: AWS Certified AI Practitioner (AIF-C01 – English)
Exam Prep Standard Course: AWS Certified AI Practitioner (AIF-C01 – English)
Exam Prep Enhanced Course: AWS Certified AI Practitioner (AIF-C01 – English)
Exam Prep Official Pretest: AWS Certified AI Practitioner (AIF-C01) – English)
Read questions carefully, paying attention to qualifiers like "least" or "most."
Eliminate obviously incorrect answers to narrow down choices.
Make sure to answer all questions, as unanswered questions are marked wrong.
Understand the Exam Domains. The exam is likely to cover the following areas:
AI/ML Fundamentals: Understanding key concepts like supervised vs. unsupervised learning, deep learning, NLP, and computer vision.
AWS AI/ML Services: Know how AWS services like Amazon SageMaker, AWS Bedrock, Rekognition, Comprehend, Polly, Lex, and Transcribe work.
Data Preparation: Basics of data collection, cleaning, labeling, and feature engineering.
Responsible AI: Ethical AI principles, fairness, and bias detection.
Use Cases & Applications: Identifying real-world applications of AI/ML in industries.
AWS offers multiple AI/ML services that you should be familiar with:
Amazon Lex (chatbots)
Amazon Kendra (enterprise search)
Use Amazon SageMaker Canvas (a no-code ML tool) to build models without writing code.
Deploy a chatbot using Amazon Lex.
Try Amazon Rekognition for image analysis.
Explore Bedrock to test generative AI models.
Remember to practice with official questions, focus on understanding key concepts, and apply your learning to real-world scenarios. I´m following these guidelines and utilizing the resources available in my journey to prepare for the AWS Certified AI Practitioner exam.
