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IBM C1000-185 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Integration and Orchestration | 8% | - Integration with external services - Workflow orchestration with LangChain - API and SDK usage |
| Analyze and Design a Generative AI Solution | 15% | - Model architecture and selection criteria - Evaluation metrics and success criteria - Generative AI and LLM capabilities - Use case analysis and requirements definition |
| Deployment and Operationalization | 13% | - Deployment planning and architecture - Versioning and lifecycle management - Monitoring and performance optimization - Model and prompt deployment |
| Prompt Engineering | 16% | - Model parameters and hyperparameter tuning - Prompt design and template creation - Prompt optimization and cost reduction - Prompt Lab usage and best practices - Prompting techniques: zero-shot, few-shot, chain-of-thought |
| Model Customization and Fine-Tuning | 31% | - Customization with InstructLab - Fine-tuning concepts and approaches - Data preparation and dataset creation - Model quantization and optimization - Synthetic data generation - Parameter-Efficient Fine-Tuning (PEFT), LoRA |
| 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:
1. You are tasked with fine-tuning a pre-trained generative AI model for customer support automation. The goal is to enhance the model's performance in generating concise, relevant answers to frequently asked questions (FAQs). To do this, you need to optimize the prompt-tuning process.
Which two of the following techniques would be most effective for creating a prompt-tuned model for this purpose? (Select two)
A) Limit the training data to 100 samples of FAQs to prevent overfitting and keep the prompt-tuning process computationally efficient.
B) Shorten the prompts to the minimum number of words needed to address the FAQ directly, focusing on the key terms that drive the correct output.
C) Introduce randomness in prompts by using variations in the wording for similar FAQs to improve the model's adaptability to different styles.
D) Use a large set of domain-specific FAQs and fine-tune the model using those examples, ensuring that prompts are tailored to each type of question.
E) Utilize reinforcement learning to penalize long or irrelevant responses during the tuning phase, optimizing the model for concise output.
2. You've conducted a prompt-tuning experiment, and after reviewing the generated outputs, you observe issues such as incomplete responses, irrelevant content, and occasional factual inaccuracies.
What is the most appropriate action to address these data quality problems?
A) Increase the length of the input prompt to ensure that responses are more complete.
B) Fine-tune the model on domain-specific data to improve factual accuracy and relevance.
C) Lower the model's perplexity score to improve both completeness and factual accuracy.
D) Introduce temperature tuning to adjust the randomness of the model's output and reduce irrelevant content.
3. You are tasked with fine-tuning a language model using a prompt-tuning approach on a dataset consisting of customer service chat logs. The goal is to optimize the model's ability to generate polite and contextually appropriate responses.
Which of the following steps are essential when preparing the dataset for prompt-tuning in this context? (Select two)
A) Ensure all examples in the dataset follow the exact same input-output format.
B) Remove any conversations that contain excessive user slang or misspellings.
C) Separate the dataset into training, validation, and test subsets.
D) Ensure each conversation includes both customer input and agent response as context for the model.
E) Convert all user queries into lowercase to reduce noise in the dataset.
4. You are building a generative AI system that uses synthetic data to mimic an existing dataset. You have learned about two primary algorithms: one that focuses on ensuring the synthetic data passes statistical normality tests and another designed to generate realistic-looking data without focusing on distribution conformity.
Which algorithm should you choose if your primary concern is statistical accuracy and passing the Anderson-Darling test?
A) Bootstrapping Algorithm
B) Anderson-Darling Based Synthetic Data Generation (ADS-DG)
C) K-Nearest Neighbors (KNN)
D) Gaussian Mixture Models (GMMs)
5. In which of the following scenarios would zero-shot prompting be more effective than few-shot prompting when interacting with a generative AI model?
A) When the model is expected to perform a novel task it has never seen, but the prompt can include several examples for guidance.
B) When the goal is to adjust the model's response based on few labeled examples that help refine its predictions.
C) When the prompt is designed for a general task like summarizing a text, which the model is pre-trained on.
D) When the task requires highly domain-specific knowledge that the model has not been exposed to before.
Solutions:
| Question # 1 Answer: B,D | Question # 2 Answer: B | Question # 3 Answer: C,D | Question # 4 Answer: B | Question # 5 Answer: C |
