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IBM C1000-185 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: IBM watsonx.ai and Platform Capabilities | - watsonx.ai core features - Model selection and deployment workflows - Prompt Lab usage and tooling |
| Topic 2: Model Evaluation and Governance | - Model monitoring and lifecycle management - Evaluation metrics for LLMs - Bias, fairness, and responsible AI |
| Topic 3: Foundations of Generative AI | - Tokenization and embeddings - Large Language Models (LLMs) fundamentals - Transformer architecture overview |
| Topic 4: Retrieval-Augmented Generation (RAG) | - Document ingestion and retrieval pipelines - Vector databases and embeddings - Grounding and hallucination mitigation |
| Topic 5: Prompt Engineering | - Few-shot and zero-shot prompting - Prompt design techniques - Prompt tuning and optimization strategies |
IBM watsonx Generative AI Engineer - Associate Sample Questions:
1. IBM Watsonx Tuning Studio offers several benefits when fine-tuning pre-trained models for specific tasks.
Which of the following is not a key benefit of using Tuning Studio?
A) Tuning Studio integrates with existing AI infrastructure to streamline model fine-tuning without requiring complex deployment processes.
B) Tuning Studio allows selective parameter updates, reducing the need to retrain the entire model for each new task.
C) Tuning Studio offers fine-tuning with minimal data, allowing users to adapt models to niche tasks without needing large datasets.
D) Tuning Studio enables real-time, dynamic adjustments to the model's architecture during inference to handle new tasks.
2. While customizing an LLM in InstructLab to generate more human-like responses for a customer service chatbot, you notice that the responses are too formal and lack empathy.
Which of the following techniques will best address this problem and help tailor the model to generate more empathetic responses?
A) Adjust the model's max sequence length to encourage longer responses
B) Change the decoder strategy from greedy decoding to beam search to increase response quality
C) Use prompt engineering to guide the model towards empathetic responses
D) Apply transfer learning with a dataset containing casual language
3. 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) Use highly abstract and open-ended prompts to allow the model more freedom in generating responses.
B) Increase the temperature parameter to introduce more diversity and creativity into the model's output.
C) Include explicit instructions and specific constraints within the prompt to limit the scope of the model's generation.
D) Set the minimum token length high to ensure the model has enough time to fully develop its response.
4. A healthcare organization is deploying a generative AI model to assist in summarizing patient records. Given the sensitivity of patient data, the model must not output Personally Identifiable Information (PII) such as names, addresses, or Social Security numbers.
Which of the following strategies would best help prevent the model from unintentionally generating PII?
A) Increase the temperature and top-k values to reduce the probability of PII being generated.
B) Use a post-processing step to detect and mask PII after the model generates the text.
C) Fine-tune the model specifically on anonymized datasets with PII removed.
D) Disable the model's ability to use context from previous tokens to prevent PII from being included in the output.
5. You are tasked with securing an endpoint for a generative AI model that interacts with external applications.
Which of the following practices is MOST effective in ensuring both security and stability of the model's endpoint in a production environment?
A) Implement strict IP whitelisting and authentication, but allow unfiltered prompts for flexibility.
B) Focus exclusively on endpoint authentication without enforcing any rate limits to prevent user frustration.
C) Allow all external applications unrestricted access to the endpoint to facilitate wider model usage.
D) Limit the number of API calls per minute, restrict prompt lengths, and apply input validation to prevent malicious queries.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: D |
