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IBM C1000-185 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Retrieval-Augmented Generation (RAG) | 17% | - Generate vector embeddings utilizing models - Describe embeddings in the context of GenAI - Develop using libraries - Describe when to use a vector database |
| Topic 2: Prompt Engineering & Output Quality | 25% | - Understanding foundational Prompt Engineering techniques - Improving output quality using prompt design techniques - Writing effective and professional prompts - Controlling response style, length, and format - Reducing hallucinations and improving overall output accuracy |
| Topic 3: Deployment & Enterprise Readiness | - Preparing GenAI solutions for enterprise usage - Improving solutions based on user feedback - Understanding basic security and access control requirements - Managing usage and monitoring at a basic level | |
| Topic 4: Analyze and Design a Generative AI Solution | 15% | - Understand security risks associated with LLMs, prompt engineering, prompt, and data - Understand how to choose the appropriate model for a use case - Articulate the optimal model architecture based on a use case - Understand use cases and identify Gen AI application opportunities - Understand the limitations of GenAI/LLMs - Articulate the components in Gen AI Patterns - Understand the five capabilities of GenAI/LLMs - Identify and apply various tools and techniques like AI agents, RAG, LangChain, etc. |
| Topic 5: Deployment | 13% | - Plan for a deployment based on client needs - Deploy AI Assets - High level architecture for deployment options - Plan out deployment of prompts for versioning - Deploy a custom model |
| Topic 6: 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 |
IBM watsonx Generative AI Engineer - Associate Sample Questions:
Question 1
Your company is working on deploying a Watsonx Generative AI model for a client, and you have been asked to define the roles involved in the deployment process.
Which of the following roles is responsible for ensuring that the model is properly integrated into the client's existing systems and that data pipelines are established for continuous model improvement?
A. Solution Architect
B. DevOps Engineer
C. Data Engineer
D. Data Scientist
Question 2
You are tasked with building a Retrieval-Augmented Generation (RAG) system to assist users in retrieving relevant documents from a vast knowledge base. The first step in this process is to generate vector embeddings for the documents using a pre-trained model. After generating embeddings, you notice that the model is sometimes failing to retrieve semantically similar documents.
Which of the following is the most appropriate approach to ensure that semantically similar documents are retrieved effectively?
A. Use Greedy Decoding during the embedding generation to avoid irrelevant tokens in the vectors.
B. Choose a model with a smaller embedding dimension to reduce the memory footprint of embeddings.
C. Convert all documents into embeddings using cosine similarity directly instead of using a vector search algorithm.
D. Fine-tune the model on a task-specific dataset to improve the quality of the embeddings for your domain.
Question 3
A generative AI model designed for healthcare content generation is being evaluated for ethical risks. The model tends to give preference to certain demographic groups when recommending treatments.
What is the most effective method to identify and mitigate this bias during the prompt engineering phase?
A. Train the model on a smaller dataset that excludes demographic information, to remove bias from its learned patterns.
B. Limit the model's context window to prevent it from over-relying on demographic information.
C. Use adversarial debiasing techniques to adjust the model's internal representations during training.
D. Adjust the temperature to 1.0 to ensure the model generates more balanced and less biased outputs.
Question 4
In the context of decoding methods in IBM Watsonx Generative AI, both top-k and top-p sampling are used to control the output of the model.
Which of the following statements correctly distinguishes between top-k and top-p sampling?
A. Both top-k and top-p sampling ensure that no tokens are selected with a probability below the set threshold, regardless of their context.
B. Top-k sampling samples from a dynamic range of tokens, while top-p sampling restricts choices to a fixed number of tokens.
C. Top-p sampling allows for a more flexible token selection process based on cumulative probabilities, while top-k limits token selection to a fixed number of top choices.
D. Top-p sampling ensures deterministic outputs, whereas top-k sampling guarantees random selection from the entire vocabulary.
Question 5
In IBM Watsonx's Prompt Lab, you are refining a prompt to improve the clarity and relevance of the AI's responses. You need to understand which prompt editing options are available to optimize your results.
Which of the following is NOT an available prompt editing option?
A. Using tone adjustments to modify the emotional tone or style of the AI's responses.
B. Setting conditions within the prompt to handle different scenarios based on detected input patterns.
C. Adding dynamic variables to the prompt, allowing for flexible and context-specific responses.
D. Adjusting the context window to include or exclude specific sections of input text.
Solutions:
| Question 1 Answer: A | Question 2 Answer: D | Question 3 Answer: C | Question 4 Answer: C | Question 5 Answer: A |
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