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IBM C1000-185 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Integration and Orchestration | 8% | - Workflow orchestration with LangChain - API and SDK usage - Integration with external services |
| Model Customization and Fine-Tuning | 31% | - Parameter-Efficient Fine-Tuning (PEFT), LoRA - Synthetic data generation - Model quantization and optimization - Data preparation and dataset creation - Customization with InstructLab - Fine-tuning concepts and approaches |
| Analyze and Design a Generative AI Solution | 15% | - Model architecture and selection criteria - Generative AI and LLM capabilities - Use case analysis and requirements definition - Evaluation metrics and success criteria |
| Deployment and Operationalization | 13% | - Model and prompt deployment - Versioning and lifecycle management - Monitoring and performance optimization - Deployment planning and architecture |
| Retrieval-Augmented Generation (RAG) | 17% | - RAG architecture and implementation - Vector databases and similarity search - Integration with watsonx.data - Embedding models and vector representations |
| Prompt Engineering | 16% | - Prompt optimization and cost reduction - Prompt Lab usage and best practices - Model parameters and hyperparameter tuning - Prompt design and template creation - Prompting techniques: zero-shot, few-shot, chain-of-thought |
IBM watsonx Generative AI Engineer - Associate Sample Questions:
You are tasked with optimizing a generative AI model's usage in a chatbot that provides troubleshooting instructions for software issues. The current prompt template is:
"Please provide step-by-step troubleshooting instructions for the following issue: [Issue Description]. Be detailed, include specific commands or settings the user should check, and provide potential reasons for failure." To reduce the token count and ensure cost efficiency, which of the following prompt template modifications would best manage token usage while preserving essential information?
- A. "Provide step-by-step instructions for troubleshooting the issue: [Issue Description]. Include commands and reasons for failure."
- B. "Provide detailed troubleshooting instructions for the issue: [Issue Description], with steps, commands, and potential reasons for failure."
- C. "Give troubleshooting instructions for [Issue Description], including steps, commands, and reasons for failure."
- D. "Troubleshoot the following issue: [Issue Description]. Offer step-by-step commands and reasons for failure."
Correct Answer: A 🗳️
You are using IBM's Tuning Studio to fine-tune a generative AI model for a custom text classification task. The model was pre-trained on a large corpus but shows suboptimal performance when applied to your domain-specific data. You aim to improve both accuracy and computational efficiency.
Which of the following is a primary benefit of using Tuning Studio to optimize this model?
- A. Tuning Studio allows for the customization of training data at runtime without needing pre-processing.
- B. Tuning Studio helps reduce overfitting by applying regularization techniques during the fine-tuning process.
- C. Tuning Studio provides detailed performance analytics that allow you to adjust hyperparameters in real-time.
- D. Tuning Studio automatically generates prompt templates that can be used for different tasks without further configuration.
Correct Answer: C 🗳️
You are working on enhancing the search functionality in a customer service chatbot by implementing the Retrieval-Augmented Generation (RAG) pattern. The chatbot needs to answer customer queries about various technical issues by retrieving relevant information from a knowledge base. Your team is discussing different ways to structure the RAG system and how to implement the pattern efficiently using existing tools.
Which of the following statements best describes the RAG pattern, and how it should be implemented in the context of this chatbot?
- A. The RAG pattern enhances a generative model by retrieving relevant documents, which are then used as context for generating a final response.
- B. The RAG pattern prioritizes generating answers based on the frequency of document appearances in the retrieval phase, improving precision.
- C. The RAG pattern combines dense retrieval with a vector store, where retrieved documents are directly presented as final answers.
- D. The RAG pattern integrates sparse retrieval with a rule-based system for generating responses based on exact document matches.
Correct Answer: A 🗳️
In the context of Tuning Studio in IBM watsonx, what is one of the key benefits of using Compute Unit Hours (CUHs) during the fine-tuning process?
- A. It reduces the time required to train models by lowering the accuracy threshold.
- B. It allows for the precise allocation of computational resources to manage budget constraints.
- C. It provides real-time feedback on model deployment success rates.
- D. It limits the number of model versions stored, improving system performance.
Correct Answer: B 🗳️
You are using a generative AI model in a healthcare application to generate personalized treatment recommendations based on patient data.
Which of the following scenarios represent valid concerns related to model risks when deploying the AI in this setting? (Select two)
- A. The model generates text that includes private patient information, violating data privacy regulations.
- B. The model fails to generate treatment recommendations for some patients due to exceeding token limits during inference.
- C. The model includes probabilistic estimates for treatment outcomes, which adds uncertainty to the recommendations and reduces their usability.
- D. The model generates biased recommendations based on incomplete or skewed training data, which disproportionately impacts certain patient demographics.
- E. The model produces highly creative treatment recommendations that are not based on standard medical guidelines.
Correct Answer: A,D 🗳️
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