IBM Generative AI (Watsonx.ai COE) Test

This skill focuses on the core principles of NLP, including tokenization, part-of-speech tagging, and word embeddings like Word2Vec and GloVe. It delves into sequence models, such as RNNs, LSTMs, and GRUs, to understand how models learn and process sequential data. The skill also covers attention mechanisms in NLP tasks like machine translation and summarization, with practical applications in sentiment analysis and named entity recognition.

This skill is critical for understanding the inner workings of Transformer models, which underpin modern generative AI. It explores self-attention, multi-head attention, and positional encoding, enabling efficient sequential data processing. The skill looks at key models like GPT, BERT, and T5, examining their applications in generative tasks and emphasizing fine-tuning for domain-specific tasks.

This skill evaluates knowledge of integrating external knowledge bases into generative models using RAG. It covers the mechanics of retrieving documents from structured and unstructured data sources, using retrieval systems like FAISS and ElasticSearch to enhance text generation. Applications include question-answering systems and chatbots, where RAG dynamically pulls in relevant information to guide model outputs.

This skill assesses the ability to adapt pre-trained models for specific tasks through fine-tuning and transfer learning. It covers key topics like hyperparameter tuning, task-specific training, and optimization strategies. The skill tests how candidates balance computational costs and model performance during fine-tuning and prevent overfitting with smaller datasets.

This skill reviews the tooling ecosystems used to develop and deploy generative AI models. It evaluates practical knowledge of platforms like IBM Watsonx.ai, Hugging Face Transformers, and libraries like PyTorch and TensorFlow, focusing on integrating these tools into end-to-end AI pipelines from training to deployment.

This skill tests the ability to deploy generative AI models in cloud environments, focusing on platforms like IBM Cloud, AWS, Azure, and GCP. It covers best practices for containerizing models using Docker, orchestrating workloads with Kubernetes, and deploying serverless applications for real-time model inference.

This skill delves into advanced data handling techniques for generative AI, exploring how document loaders and vector databases are used to manage large-scale unstructured data. It includes the creation and storage of embeddings in vector databases, enabling rapid retrieval and ranking of information for real-time AI systems.

This skill involves crafting precise inputs for LLMs to guide models toward desirable outputs. It tests the ability to construct and refine prompts for diverse tasks and explores synthetic data generation to overcome data limitations, focusing on tools like Snorkel for weak supervision.

This skill assesses understanding of responsible AI practices, ensuring fairness, transparency, and accountability in generative models. It covers bias detection, model interpretability, and adherence to regulatory standards, evaluating integration of AI governance frameworks into AI pipelines.