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IBM C1000-185 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deployment & Enterprise Readiness | - Understanding basic security and access control requirements - Preparing GenAI solutions for enterprise usage - Managing usage and monitoring at a basic level - Improving solutions based on user feedback | |
| Topic 2: Integration with Model Orchestration | 8% | - Develop LLM based applications with LangChain - Integrate watsonx.ai with Other Services/Manage APIs and SDKs - Orchestrate AI Workflows - Understand real-world Integration Scenarios |
| Topic 3: Prompt Engineering & Output Quality | 25% | - Controlling response style, length, and format - Understanding foundational Prompt Engineering techniques - Reducing hallucinations and improving overall output accuracy - Improving output quality using prompt design techniques - Writing effective and professional prompts |
| Topic 4: Deployment | 13% | - High level architecture for deployment options - Plan out deployment of prompts for versioning - Deploy a custom model - Deploy AI Assets - Plan for a deployment based on client needs |
| Topic 5: Analyze and Design a Generative AI Solution | 15% | - Identify and apply various tools and techniques like AI agents, RAG, LangChain, etc. - Understand the limitations of GenAI/LLMs - Understand the five capabilities of GenAI/LLMs - Understand use cases and identify Gen AI application opportunities - Articulate the components in Gen AI Patterns - Understand how to choose the appropriate model for a use case - Understand security risks associated with LLMs, prompt engineering, prompt, and data - Articulate the optimal model architecture based on a use case |
| Topic 6: Retrieval-Augmented Generation (RAG) | 17% | - Describe embeddings in the context of GenAI - Generate vector embeddings utilizing models - Develop using libraries - Describe when to use a vector database |
IBM watsonx Generative AI Engineer - Associate Sample Questions:
Question 1
You are tasked with generating creative text outputs using an AI language model for a marketing campaign. You want to ensure that the responses are diverse and unexpected but still somewhat relevant to the prompt.
Which combination of temperature and random seed should you use to achieve this?
A. Temperature: 1.0, Random Seed: 0
B. Temperature: 0.1, Random Seed: 42
C. Temperature: 0.7, Random Seed: None
D. Temperature: 1.5, Random Seed: 123
Question 2
In a system that generates product recommendations using a generative AI model, you are tasked with creating prompts that incorporate dynamic variables to ensure more relevant and personalized responses.
Prompt Template: "Recommend products for [customer_name] based on their interest in [product_category]." Which portion of the prompt is the best candidate to be replaced with variables to achieve greater personalization and flexibility? (Select two)
A. "[product_category]"
B. "[customer_name]"
C. "Recommend products"
D. "based on their interest"
E. "for"
Question 3
You are working on a project that involves deploying a series of prompt templates for a large language model on the IBM Watsonx platform. The team has requested a system that supports prompt versioning so that updates to the prompts can be tracked and tested over time.
Which of the following is the most important consideration when planning prompt versioning for deployment?
A. The versioning system should automatically downgrade to the previous prompt version if the model returns a confidence score below a certain threshold during inference.
B. Prompts should be stored in a proprietary IBM format, as other formats are not compatible with the Watsonx platform when using versioning.
C. Version control should focus exclusively on the syntactical structure of the prompts, as changes to prompt content rarely impact the model's performance.
D. Each version of the prompt must have a unique identifier that can be referenced during model inference, to avoid conflicting results from different prompt versions.
Question 4
You are tasked with designing an AI prompt to extract specific data from unstructured text. You decide to use either a zero-shot or a few-shot prompting technique with an IBM Watsonx model.
Which of the following statements best describes the key difference between zero-shot and few-shot prompting?
A. Zero-shot prompting provides the model with examples, while few-shot prompting does not.
B. Zero-shot prompting requires retraining the model with additional data, while few-shot prompting uses a pre-trained model without retraining.
C. Few-shot prompting is used when the model is trained on supervised learning, while zero-shot prompting works only with unsupervised models.
D. Zero-shot prompting requires no examples in the prompt, while few-shot prompting provides the model with one or more examples to clarify the task.
Question 5
You are tasked with fine-tuning a pre-trained language model for a customer support chatbot. The dataset you're using is mostly unstructured text from chat logs.
What steps should you take to prepare the dataset for fine-tuning to ensure optimal model performance?
A. Randomly split the data into training, validation, and test sets.
B. Use domain-specific tokenization to better capture important keywords and phrases relevant to customer support.
C. Use data augmentation techniques like paraphrasing to artificially increase the dataset size.
D. Normalize the text by removing all punctuation, special characters, and converting text to lowercase.
Solutions:
| Question 1 Answer: C | Question 2 Answer: A,B | Question 3 Answer: D | Question 4 Answer: D | Question 5 Answer: B |
