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IBM C1000-185 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Analyze and Design a Generative AI Solution | 15% | - Use case analysis and requirements definition - Evaluation metrics and success criteria - Generative AI and LLM capabilities - Model architecture and selection criteria |
| Prompt Engineering | 16% | - Prompt design and template creation - Prompt Lab usage and best practices - Prompt optimization and cost reduction - Prompting techniques: zero-shot, few-shot, chain-of-thought - Model parameters and hyperparameter tuning |
| Model Customization and Fine-Tuning | 31% | - Fine-tuning concepts and approaches - Model quantization and optimization - Data preparation and dataset creation - Parameter-Efficient Fine-Tuning (PEFT), LoRA - Customization with InstructLab - Synthetic data generation |
| Deployment and Operationalization | 13% | - Deployment planning and architecture - Monitoring and performance optimization - Versioning and lifecycle management - Model and prompt deployment |
| Retrieval-Augmented Generation (RAG) | 17% | - Embedding models and vector representations - Integration with watsonx.data - Vector databases and similarity search - RAG architecture and implementation |
| Integration and Orchestration | 8% | - Integration with external services - Workflow orchestration with LangChain - API and SDK usage |
IBM watsonx Generative AI Engineer - Associate Sample Questions:
You are tasked with building a generative AI model to help create automated marketing copy for a business. A key concern is the potential generation of biased or legally sensitive content, which could negatively impact the company's reputation.
Which of the following strategies would be the most effective in mitigating these model risks?
- A. Implement a post-processing filter to remove any potentially offensive or legally sensitive content.
- B. Use reinforcement learning to fine-tune the model based on user feedback to eliminate bias in the long term.
- C. Use a comprehensive training dataset that includes diverse business domains to reduce biases.
- D. Include fairness metrics in the model evaluation stage to monitor for biased outputs.
Correct Answer: D 🗳️
A company is using IBM's InstructLab to fine-tune a large language model (LLM) to perform customer support tasks, such as answering frequently asked questions (FAQs) and troubleshooting product issues.
Which of the following components of InstructLab plays the most crucial role in ensuring the model learns to align its responses with the specific format and tone required for customer interactions?
- A. The prompt-tuning engine, which fine-tunes model outputs based on pre-defined instructions.
- B. The user simulation environment, which provides real-time testing of model outputs.
- C. The evaluation metrics dashboard, which tracks model performance on customer interaction tasks.
- D. The instruction optimizer, which tunes hyperparameters to improve task-specific performance.
Correct Answer: A 🗳️
You are deploying an AI model to a production environment using watsonx.ai. The model is a fine-tuned GPT-based generative model designed to support real-time customer interactions. The deployment must ensure scalability, security, and maintain high availability.
Which deployment strategy should you choose to best meet these requirements?
- A. Deploy the model on an on-premises server with an isolated environment to ensure full control and security.
- B. Deploy the model on a single cloud-based GPU instance, and enable scheduled maintenance to minimize downtime.
- C. Use a containerized deployment with Kubernetes on a multi-node GPU cluster to support auto-scaling and fault tolerance.
- D. Deploy the model on multiple virtual machines, manually balancing traffic between instances.
Correct Answer: C 🗳️
You are optimizing the generative AI model in IBM watsonx to balance creativity and coherence. You want to use a decoding method that dynamically adjusts the token probability threshold based on cumulative probabilities, thus ensuring the model generates coherent outputs while still allowing for some creativity.
Which parameter should you adjust, and what is the optimal setting?
- A. Set temperature to 0 and top-p to 0.5
- B. Set temperature to 1.5 and top-p to 0.9
- C. Set temperature to 1 and top-p to 0.2
- D. Set top-p to 0.9 and temperature to 0.7
Correct Answer: D 🗳️
When addressing bias in a generative AI model, which of the following strategies is least likely to be effective in reducing biased outputs during text generation?
- A. Using temperature control during generation to manage diversity in responses
- B. Leveraging prompt rephrasing techniques to remove bias-inducing keywords or phrases
- C. Training the model on a diverse and representative dataset
- D. Incorporating fairness constraints during the model's training phase
Correct Answer: A 🗳️
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