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IBM C1000-185 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Retrieval-Augmented Generation (RAG) | - Grounding and hallucination mitigation - Vector databases and embeddings - Document ingestion and retrieval pipelines |
| IBM watsonx.ai and Platform Capabilities | - Prompt Lab usage and tooling - Model selection and deployment workflows - watsonx.ai core features |
| Prompt Engineering | - Prompt design techniques - Prompt tuning and optimization strategies - Few-shot and zero-shot prompting |
| Model Evaluation and Governance | - Evaluation metrics for LLMs - Model monitoring and lifecycle management - Bias, fairness, and responsible AI |
| Foundations of Generative AI | - Large Language Models (LLMs) fundamentals - Tokenization and embeddings - Transformer architecture overview |
IBM watsonx Generative AI Engineer - Associate Sample Questions:
In which of the following scenarios would zero-shot prompting be more effective than few-shot prompting when interacting with a generative AI model?
- A. When the task requires highly domain-specific knowledge that the model has not been exposed to before.
- B. When the goal is to adjust the model's response based on few labeled examples that help refine its predictions.
- C. When the prompt is designed for a general task like summarizing a text, which the model is pre-trained on.
- D. When the model is expected to perform a novel task it has never seen, but the prompt can include several examples for guidance.
Correct Answer: C 🗳️
Which of the following techniques is the most effective for reducing bias in generative AI models through prompt engineering?
- A. Allowing the model to auto-correct its own responses by cross-referencing with other outputs
- B. Fine-tuning the model using training data that explicitly includes examples of biased outputs
- C. Using neutral and carefully phrased prompts to avoid triggering biased outputs
- D. Applying Greedy Decoding to ensure the most likely tokens are selected during generation
Correct Answer: C 🗳️
You are working on a Retrieval-Augmented Generation (RAG) system to enhance the performance of a generative model. The RAG model needs to leverage a document corpus to generate answers to complex questions.
Which of the following steps is critical in the RAG pipeline to ensure accurate and relevant answer generation?
- A. Fine-tuning the generative model on the entire document corpus without retrieval components.
- B. Indexing the document corpus using embeddings, retrieving relevant documents, and feeding them as context into the generative model.
- C. Retrieving only the longest document in the corpus as the generative model can synthesize information more effectively from detailed content.
- D. Using keyword-based search to retrieve documents and then allowing the generative model to synthesize answers from those documents.
Correct Answer: B 🗳️
You are implementing a Retrieval-Augmented Generation (RAG) system to enhance a large language model's (LLM) ability to answer questions based on an external document store.
What role do embeddings play in the RAG architecture, and how can you optimize them for more relevant document retrieval? (Select two)
- A. Fine-tuning the embedding model with task-specific data can lead to more accurate retrievals.
- B. Increasing the dimensionality of embeddings always improves retrieval accuracy by providing a more detailed representation of the documents.
- C. Embeddings are only used during the generation phase of RAG to enhance language model outputs.
- D. The cosine similarity metric is typically less effective than Euclidean distance for comparing embeddings in RAG systems.
- E. Embeddings are vector representations of text, used to measure the similarity between queries and documents.
Correct Answer: A,E 🗳️
You are optimizing a generative AI chatbot for concise responses to user queries, ensuring that it doesn't over-generate unnecessary content. However, you observe that the model occasionally stops prematurely, cutting off relevant information.
What configuration best addresses this issue without allowing for excessive output?
- A. Stop Sequence = '.', Max Tokens = 150
- B. Stop Sequence = '</end>', Max Tokens = 250
- C. Stop Sequence = '.', Max Tokens = 500
- D. Stop Sequence = '.' (period followed by a space), Max Tokens = 300
Correct Answer: B 🗳️
