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IBM C1000-185 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Analyze and Design a Generative AI Solution | 15% | - Model architecture and selection criteria - Generative AI and LLM capabilities - Evaluation metrics and success criteria - Use case analysis and requirements definition |
| Deployment and Operationalization | 13% | - Monitoring and performance optimization - Model and prompt deployment - Versioning and lifecycle management - Deployment planning and architecture |
| Model Customization and Fine-Tuning | 31% | - Parameter-Efficient Fine-Tuning (PEFT), LoRA - Model quantization and optimization - Fine-tuning concepts and approaches - Data preparation and dataset creation - Synthetic data generation - Customization with InstructLab |
| Integration and Orchestration | 8% | - API and SDK usage - Workflow orchestration with LangChain - Integration with external services |
| Prompt Engineering | 16% | - Prompting techniques: zero-shot, few-shot, chain-of-thought - Model parameters and hyperparameter tuning - Prompt Lab usage and best practices - Prompt design and template creation - Prompt optimization and cost reduction |
| Retrieval-Augmented Generation (RAG) | 17% | - Integration with watsonx.data - Vector databases and similarity search - RAG architecture and implementation - Embedding models and vector representations |
IBM watsonx Generative AI Engineer - Associate Sample Questions:
You are tasked with designing prompts for an IBM Watsonx Generative AI model to minimize hallucinations in responses. One of the ways to reduce hallucinations is by improving the quality of the prompt to guide the model more effectively.
Which of the following prompt engineering strategies would be most effective in reducing the likelihood of hallucinations?
- A. Use highly abstract and open-ended prompts to allow the model more freedom in generating responses.
- B. Increase the temperature parameter to introduce more diversity and creativity into the model's output.
- C. Set the minimum token length high to ensure the model has enough time to fully develop its response.
- D. Include explicit instructions and specific constraints within the prompt to limit the scope of the model's generation.
Correct Answer: D 🗳️
You are using IBM's Tuning Studio to fine-tune a large-scale foundation model for a customer service chatbot. The goal is to optimize the model for performance in handling a wide variety of customer queries while minimizing computational costs. Before making any changes, you want to understand how Tuning Studio can help achieve your optimization goals.
Which of the following is the most significant benefit provided by Tuning Studio when optimizing a generative AI model?
- A. Tuning Studio automatically deploys the fine-tuned model to production environments without requiring further testing.
- B. Tuning Studio allows the user to implement custom model architectures from scratch to meet specific task requirements.
- C. Tuning Studio provides real-time monitoring of model performance metrics during the fine-tuning process, allowing you to adjust hyperparameters effectively.
- D. Tuning Studio reduces the dataset size needed for training by implementing automated data augmentation strategies.
Correct Answer: C 🗳️
You are working on a Retrieval-Augmented Generation (RAG) system using IBM watsonx. The system needs to retrieve relevant documents based on a user's query and generate a response using a language model. To optimize retrieval, you are tasked with generating vector embeddings for documents and queries using a pre-trained model. Your goal is to ensure that the embeddings are semantically meaningful to improve the retrieval accuracy.
Which of the following steps should be taken to ensure the vector embeddings are correctly generated and effective for document retrieval in a RAG system? (Select two)
- A. Generate embeddings for documents only and skip embeddings for user queries, relying on traditional keyword-based retrieval for queries.
- B. Manually adjust the embedding vectors to emphasize certain keywords that are more important for retrieval.
- C. Use a generative language model to generate embeddings without any fine-tuning, as it captures all the necessary context.
- D. Use a pre-trained model designed specifically for embedding generation rather than general-purpose language models.
- E. Normalize the vector embeddings after generation to ensure they are comparable during retrieval.
Correct Answer: D,E 🗳️
In a Retrieval-Augmented Generation (RAG) system, embeddings play a central role in linking input queries with relevant external knowledge. Different embedding models can be used to generate these embeddings.
Which of the following embedding models is best suited for capturing semantic meaning in text for use in a RAG system?
- A. Word2Vec
- B. One-Hot Encoding
- C. Latent Dirichlet Allocation (LDA)
- D. Bag-of-Words (BoW)
Correct Answer: A 🗳️
You are designing a prompt to converse with a model for multilingual translation.
How would you frame the prompt to translate an English business email to Japanese, ensuring that the translated email is formal and appropriate for a business setting in Japan?
- A. "Translate this business email into Japanese using informal language."
- B. "Translate the following business email into Japanese, focusing on word-for-word accuracy."
- C. "Translate this business email into Japanese, making sure the tone is formal and culturally appropriate for a business context."
- D. "Translate this business email into Japanese but do not consider the tone or formalities."
Correct Answer: C 🗳️
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