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IBM C1000-185 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Analyze and Design a Generative AI Solution | 15% | - Articulate the components in Gen AI Patterns - Understand use cases and identify Gen AI application opportunities - Understand security risks associated with LLMs, prompt engineering, prompt, and data - Understand the five capabilities of GenAI/LLMs - Understand how to choose the appropriate model for a use case - Identify and apply various tools and techniques like AI agents, RAG, LangChain, etc. - Articulate the optimal model architecture based on a use case - Understand the limitations of GenAI/LLMs |
| Topic 2: Integration with Model Orchestration | 8% | - Understand real-world Integration Scenarios - Orchestrate AI Workflows - Develop LLM based applications with LangChain - Integrate watsonx.ai with Other Services/Manage APIs and SDKs |
| Topic 3: Prompt Engineering & Output Quality | 25% | - Writing effective and professional prompts - Improving output quality using prompt design techniques - Controlling response style, length, and format - Reducing hallucinations and improving overall output accuracy - Understanding foundational Prompt Engineering techniques |
| Topic 4: Deployment | 13% | - Plan out deployment of prompts for versioning - High level architecture for deployment options - Deploy a custom model - Deploy AI Assets - Plan for a deployment based on client needs |
| Topic 5: Retrieval-Augmented Generation (RAG) | 17% | - Describe embeddings in the context of GenAI - Develop using libraries - Generate vector embeddings utilizing models - Describe when to use a vector database |
| Topic 6: Deployment & Enterprise Readiness | - Preparing GenAI solutions for enterprise usage - Managing usage and monitoring at a basic level - Improving solutions based on user feedback - Understanding basic security and access control requirements |
IBM watsonx Generative AI Engineer - Associate Sample Questions:
You are tasked with optimizing a large language model (LLM) for deployment in a resource-constrained environment where memory usage and computational cost need to be minimized without significantly compromising model accuracy.
Which quantization technique would be the most appropriate to achieve this balance?
- A. Full integer quantization with 8-bit precision
- B. Post-training weight clustering
- C. Mixed-precision floating-point quantization
- D. Post-training dynamic quantization
You are optimizing a generative AI chatbot for concise responses to user queries, ensuring that it doesn't over-generate unnecessary content. However, you observe that the model occasionally stops prematurely, cutting off relevant information.
What configuration best addresses this issue without allowing for excessive output?
- A. Stop Sequence = '</end>', Max Tokens = 250
- B. Stop Sequence = '.', Max Tokens = 150
- C. Stop Sequence = '.', Max Tokens = 500
- D. Stop Sequence = '.' (period followed by a space), Max Tokens = 300
You are configuring an LLM for a product recommendation chatbot. The goal is to balance creativity and relevance, ensuring the chatbot suggests diverse but appropriate products.
Which combination of model parameters will best achieve this? (Select two)
- A. Set a high penalty for repetition to encourage varied recommendations
- B. Increase the temperature to 1.5 to maximize creativity in suggestions
- C. Set the temperature to 0.1 for highly deterministic responses
- D. Use a top-p (nucleus) sampling value of 0.95 for diverse, relevant outputs
- E. Apply a low top-k value (e.g., k=10) to restrict randomness
You are tasked with designing a prompt template to assist a chatbot in generating professional email responses for customer service inquiries. The system should prioritize politeness, clarity, and conciseness.
What elements should be included in the prompt template to achieve the best results, considering optimal behavior of a large language model (LLM)? (Select two)
- A. Instruct the model to limit responses to a specific character count
- B. Specify the output tone as polite and professional
- C. Include examples of informal customer service responses for variability
- D. Ask the model to generate multiple versions of the response and rank them
- E. Provide the customer's emotional context for better alignment with the tone
You are tasked with deploying a watsonx Generative AI solution for a client who requires real-time inference with minimal latency. The solution will be used for large-scale text generation tasks, where throughput is critical. The client also requires the system to handle fluctuating loads efficiently.
What would be the most appropriate deployment strategy?
- A. Deploy the model on a single GPU-based cloud instance with auto-scaling enabled.
- B. Use a CPU-based deployment for cost efficiency, and manually adjust the number of instances as needed.
- C. Deploy the model in a hybrid architecture, splitting inference tasks between on-premise servers and cloud resources.
- D. Deploy the model on a multi-node GPU cluster with Kubernetes to handle auto-scaling and fault tolerance.
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