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IBM C1000-185 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Model Evaluation and Governance | - Bias, fairness, and responsible AI - Model monitoring and lifecycle management - Evaluation metrics for LLMs |
| Topic 2: IBM watsonx.ai and Platform Capabilities | - Prompt Lab usage and tooling - Model selection and deployment workflows - watsonx.ai core features |
| Topic 3: Foundations of Generative AI | - Transformer architecture overview - Large Language Models (LLMs) fundamentals - Tokenization and embeddings |
| Topic 4: Prompt Engineering | - Few-shot and zero-shot prompting - Prompt design techniques - Prompt tuning and optimization strategies |
| Topic 5: Retrieval-Augmented Generation (RAG) | - Vector databases and embeddings - Document ingestion and retrieval pipelines - Grounding and hallucination mitigation |
IBM watsonx Generative AI Engineer - Associate Sample Questions:
1. A business is implementing a RAG solution to enhance its chatbot capabilities. The chatbot needs to answer queries using a large collection of unstructured documents.
Which scenario best highlights when to use a vector database to augment this system?
A) When storing highly structured relational data, as a vector database excels at managing tabular information efficiently.
B) When working with large amounts of unstructured text data, to enable semantic search through embeddings that represent the meaning of documents.
C) When you need to provide answers based on the keywords present in the documents, as vector databases are designed for keyword-based retrieval.
D) When performing traditional database operations like sorting and filtering based on numeric or categorical values.
2. You are tasked with integrating third-party embedding models into a Retrieval-Augmented Generation (RAG) system for document retrieval. Several models offer pre-trained embeddings that can be leveraged for a variety of downstream tasks.
Which of the following third-party models is designed for generating embeddings that capture semantic meaning and context, making it ideal for a RAG-based GenAI system?
A) GPT-3 Embeddings
B) WordNet
C) Naive Bayes Classifier
D) BERT
3. You are tasked with creating a prompt template for IBM Watsonx that will help generate product reviews for a new line of smartphones. The prompt needs to be adaptable across various product features and sentiment (positive, neutral, or negative) while maintaining a consistent structure.
Which of the following prompt templates would be the most effective for generating well-structured, detailed reviews?
A) "Tell a story about the user's experience with the smartphone."
B) ":Generate a review mentioning the best features of the smartphone."
C) "Write a review about the smartphone."
D) "Write a [sentiment] review of the [product name], focusing on the [specific feature]. Include reasons for the sentiment and detailed examples."
4. You are working on a generative AI model that helps customers generate personalized responses to legal queries. The model is trained on a large corpus of publicly available legal documents. However, users often input personal information when interacting with the AI.
What is the most effective strategy to mitigate the risk of exposing personal information in the model's responses?
A) Use a privacy-preserving tokenization method to mask personal data in the input before feeding it into the model.
B) Apply a rule-based content filter to the model's outputs to remove any phrases that appear to contain personal information.
C) Train the model on anonymized data to ensure that personal information is never present in the training set.
D) Limit the model's ability to retain memory of previous user inputs by resetting its state after every query.
5. You are tasked with designing prompts for an IBM Watsonx Generative AI model to minimize hallucinations in responses. One of the ways to reduce hallucinations is by improving the quality of the prompt to guide the model more effectively.
Which of the following prompt engineering strategies would be most effective in reducing the likelihood of hallucinations?
A) Increase the temperature parameter to introduce more diversity and creativity into the model's output.
B) Set the minimum token length high to ensure the model has enough time to fully develop its response.
C) Include explicit instructions and specific constraints within the prompt to limit the scope of the model's generation.
D) Use highly abstract and open-ended prompts to allow the model more freedom in generating responses.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: C |
