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IBM C1000-185 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Output Quality | 25% | - Understanding foundational Prompt Engineering techniques - Reducing hallucinations and improving overall output accuracy - Writing effective and professional prompts - Improving output quality using prompt design techniques - Controlling response style, length, and format |
| Topic 2: Retrieval-Augmented Generation (RAG) | 17% | - Develop using libraries - Describe when to use a vector database - Generate vector embeddings utilizing models - Describe embeddings in the context of GenAI |
| Topic 3: Deployment & Enterprise Readiness | - Managing usage and monitoring at a basic level - Preparing GenAI solutions for enterprise usage - Improving solutions based on user feedback - Understanding basic security and access control requirements | |
| Topic 4: Deployment | 13% | - Plan out deployment of prompts for versioning - Deploy a custom model - Plan for a deployment based on client needs - Deploy AI Assets - High level architecture for deployment options |
| Topic 5: Analyze and Design a Generative AI Solution | 15% | - Understand how to choose the appropriate model for a use case - Articulate the optimal model architecture based on a use case - Understand the limitations of GenAI/LLMs - Identify and apply various tools and techniques like AI agents, RAG, LangChain, etc. - Understand the five capabilities of GenAI/LLMs - Understand security risks associated with LLMs, prompt engineering, prompt, and data - Understand use cases and identify Gen AI application opportunities - Articulate the components in Gen AI Patterns |
| Topic 6: Integration with Model Orchestration | 8% | - Develop LLM based applications with LangChain - Understand real-world Integration Scenarios - Orchestrate AI Workflows - Integrate watsonx.ai with Other Services/Manage APIs and SDKs |
IBM watsonx Generative AI Engineer - Associate Sample Questions:
1. You're developing a generative AI system for a medical diagnosis application that uses patient data. Your responsibility includes designing prompts that extract valuable insights without exposing sensitive patient information.
Which of the following steps is the most effective way to reduce model risks related to privacy while ensuring useful outputs from the AI?
A) Employ differential privacy techniques to add noise to the model's outputs.
B) Restrict the model's output length to reduce the risk of sensitive information leakage.
C) Increase the length of the prompts to provide more context, ensuring more accurate results.
D) Utilize a smaller model to minimize the likelihood of overfitting sensitive data.
2. 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.
3. You are designing a generative AI model to generate customer support responses. During testing, you notice that the model frequently outputs gendered language when referring to certain professions, reinforcing stereotypes.
Which of the following strategies would most effectively reduce bias in the model' responses?
A) Apply a post-processing filter that removes any gendered language after the model generates the response.
B) Increase the diversity of the dataset used to train the model, ensuring that all professions are equally represented.
C) Train the model with a lower learning rate to make it less sensitive to biased patterns in the data.
D) Reduce the maximum token limit so that the model generates shorter responses, minimizing the chance for bias.
4. In a generative AI model, you are tasked with producing creative yet coherent text for a marketing campaign. You want to ensure that the output contains varied word choices and diverse sentence structures while still maintaining some degree of logical consistency.
Which of the following settings for the temperature parameter would most likely achieve this balance?
A) Temperature = 0.7
B) Temperature = 2.0
C) Temperature = 0.2
D) Temperature = 0.0
5. While optimizing the cost of running a Generative AI model, you are instructed to adjust the prompt structure.
Which of the following changes to a prompt would most reduce computational costs while still maintaining effective results?
A) Switching from a narrative-style prompt to a bulleted list format.
B) Including multiple tasks in a single prompt to maximize efficiency.
C) Breaking complex prompts into simpler, sequential prompts.
D) Using stop tokens early in the prompt to minimize generation length.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: D |
