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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Customization and Fine-Tuning | 31% | - Fine-tuning concepts and approaches - Parameter-Efficient Fine-Tuning (PEFT), LoRA - Model quantization and optimization - Data preparation and dataset creation - Customization with InstructLab - Synthetic data generation |
| Topic 2: Retrieval-Augmented Generation (RAG) | 17% | - Vector databases and similarity search - RAG architecture and implementation - Integration with watsonx.data - Embedding models and vector representations |
| Topic 3: Prompt Engineering | 16% | - Model parameters and hyperparameter tuning - Prompting techniques: zero-shot, few-shot, chain-of-thought - Prompt optimization and cost reduction - Prompt Lab usage and best practices - Prompt design and template creation |
| Topic 4: Integration and Orchestration | 8% | - Integration with external services - API and SDK usage - Workflow orchestration with LangChain |
| Topic 5: Deployment and Operationalization | 13% | - Model and prompt deployment - Deployment planning and architecture - Monitoring and performance optimization - Versioning and lifecycle management |
| Topic 6: Analyze and Design a Generative AI Solution | 15% | - Evaluation metrics and success criteria - Use case analysis and requirements definition - Model architecture and selection criteria - Generative AI and LLM capabilities |
Question 1
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 allows the user to implement custom model architectures from scratch to meet specific task requirements.
B. Tuning Studio reduces the dataset size needed for training by implementing automated data augmentation strategies.
C. Tuning Studio automatically deploys the fine-tuned model to production environments without requiring further testing.
D. Tuning Studio provides real-time monitoring of model performance metrics during the fine-tuning process, allowing you to adjust hyperparameters effectively.
Question 2
In the context of the decoding process for generative AI models in IBM Watsonx, what is the main characteristic of greedy decoding?
A. Greedy decoding selects the highest probability token at each step, leading to deterministic and often coherent outputs.
B. Greedy decoding always selects the token with the lowest probability to encourage diversity in the generated response.
C. Greedy decoding alternates between high and low probability tokens, ensuring a balance between creativity and correctness.
D. Greedy decoding generates multiple possible sequences and selects the most grammatically correct one based on predefined rules.
Question 3
When fine-tuning a model in Tuning Studio, which of the following is a key advantage of this tool in reducing resource costs while improving model performance?
A. It optimizes the model for multilingual capabilities by adding new embeddings.
B. It automatically increases the number of layers for more complex tasks.
C. It allows incremental training, saving computational resources by reusing checkpoints.
D. It expands the model's architecture to handle larger datasets.
Question 4
You are deploying a large language model in a financial advisory platform to assist users in making investment decisions.
Which of the following represent significant risks that should be mitigated before full deployment? (Select two)
A. The model generates recommendations that align with historical financial trends but fail to account for recent economic disruptions.
B. The model is trained on open-source financial data, which results in slower response times during inference.
C. The model provides longer-than-expected responses, potentially causing user frustration and increasing abandonment rates on the platform.
D. The model occasionally generates offensive or inappropriate content when responding to user queries.
E. The model offers speculative advice without indicating the associated level of uncertainty, which may mislead inexperienced investors.
Question 5
You are implementing a few-shot prompting strategy with IBM Watsonx to improve the model's performance in generating customer service responses. The goal is to ensure the model understands the tone and format required for polite and concise replies.
Which of the following strategies best illustrates the correct way to use few-shot prompting?
A. Include a large number of examples, typically over 10, in the input prompt to ensure the model learns from diverse cases.
B. Provide example prompts with multiple different output styles to give the model a range of responses to choose from.
C. Use only negative examples in the prompt to show the model what not to generate in terms of tone and format.
D. Provide one or two well-structured examples that demonstrate the expected tone and format of the customer service responses within the prompt.
Solutions:
| Question 1 Answer: D | Question 2 Answer: A | Question 3 Answer: C | Question 4 Answer: A,E | Question 5 Answer: D |
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