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IBM C1000-185 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Retrieval-Augmented Generation (RAG) | - Vector databases and embeddings - Document ingestion and retrieval pipelines - Grounding and hallucination mitigation |
| Topic 2: IBM watsonx.ai and Platform Capabilities | - Prompt Lab usage and tooling - Model selection and deployment workflows - watsonx.ai core features |
| Topic 3: Prompt Engineering | - Prompt design techniques - Few-shot and zero-shot prompting - Prompt tuning and optimization strategies |
| Topic 4: Foundations of Generative AI | - Tokenization and embeddings - Large Language Models (LLMs) fundamentals - Transformer architecture overview |
| Topic 5: Model Evaluation and Governance | - Model monitoring and lifecycle management - Evaluation metrics for LLMs - Bias, fairness, and responsible AI |
IBM watsonx Generative AI Engineer - Associate Sample Questions:
1. You have completed a prompt-tuning experiment for a large language model (LLM) using IBM Watsonx, aimed at improving its ability to generate accurate responses to customer support queries. After the tuning process, you are analyzing the performance statistics of the model.
Which statistical metric is the most appropriate to prioritize when evaluating the success of the prompt-tuning experiment?
A) BLEU score
B) Perplexity score
C) Token generation speed
D) Log-likelihood of generated responses
2. You are using IBM Watsonx to generate answers for a customer service chatbot. However, the responses generated sometimes include fabricated details that are inaccurate (hallucinations).
Which of the following strategies will best mitigate the risk of hallucination when designing your prompt?
A) Ask open-ended questions without providing reference information: "What are the latest features of our product?"
B) Supply explicit product details and constraints in the prompt: "List the latest features of the XYZ smartphone released in 2024, including its AI-powered camera and battery-saving mode."
C) Rely on the AI model to fact-check and correct hallucinations in its responses automatically: "What can you tell me about our product?"
D) Use subjective language in your prompts: ":Can you describe why our product is the best on the market?"
3. When using IBM Watsonx Tuning Studio, what is the recommended approach to determining the number of training data examples required for effective model fine-tuning?
A) Use no more than 100 examples per task to avoid overwhelming the model's general capabilities with task-specific data.
B) Use at least 10,000 examples for each unique task to ensure the model retains its general knowledge and effectively adapts to the new task.
C) Use a minimum of 1,000 to 5,000 examples for each task, but focus on the quality and relevance of examples rather than quantity.
D) Use at least 50% of the original training data to ensure the fine-tuned model generalizes well across both new and existing tasks.
4. When leveraging existing data for fine-tuning an LLM in IBM watsonx, you want to optimize the model for a highly specialized domain. You also want to generate additional synthetic data to augment your dataset.
Which of the following approaches would best help you achieve your goal?
A) Using unsupervised learning on your existing dataset without adding synthetic data
B) Using the watsonx UI to generate synthetic data that mirrors your existing dataset, filling any data gaps
C) Relying exclusively on pre-trained general models without making domain-specific modifications
D) Manually crafting complex datasets by sampling individual instances from unrelated domains
5. You are tasked with designing a prompt to fine-tune an IBM Watsonx model to summarize legal documents. The summaries must include only factual information, highlight key legal terms, and exclude any personal interpretations or subjective analysis.
Which of the following is the best prompt to achieve this goal?
A) "Provide a summary of this legal document, focusing on factual information, including key legal terms and avoiding personal interpretation or subjective analysis."
B) "Create a brief summary of this legal document, ensuring to exclude any legal jargon and simplifying the content for a layperson audience."
C) "Summarize this legal document, focusing on key arguments and providing an analysis of the potential outcomes of the case."
D) "Generate a detailed and engaging summary of this legal document, adding your insights to clarify complex legal points for the reader."
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: A |
