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IBM C1000-185 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Model Evaluation and Governance | - Bias, fairness, and responsible AI - Evaluation metrics for LLMs - Model monitoring and lifecycle management |
| Topic 2: IBM watsonx.ai and Platform Capabilities | - Model selection and deployment workflows - watsonx.ai core features - Prompt Lab usage and tooling |
| Topic 3: Prompt Engineering | - Prompt design techniques - Prompt tuning and optimization strategies - Few-shot and zero-shot prompting |
| Topic 4: Retrieval-Augmented Generation (RAG) | - Grounding and hallucination mitigation - Vector databases and embeddings - Document ingestion and retrieval pipelines |
| Topic 5: Foundations of Generative AI | - Large Language Models (LLMs) fundamentals - Tokenization and embeddings - Transformer architecture overview |
IBM watsonx Generative AI Engineer - Associate Sample Questions:
You are working with IBM Watsonx and have developed a custom machine learning model using Watson Studio. Now, you need to deploy the model so that it can be accessed via an application.
Which step is NOT required when deploying the custom model for application access?
- A. Registering the model in Watson Machine Learning (WML)
- B. Defining a scoring endpoint for the model in Watson Machine Learning
- C. Verifying model accuracy before deployment.
- D. Assigning appropriate IAM roles for users who need access to the model
Correct Answer: C 🗳️
In the context of IBM Watsonx Generative AI models, hallucinations refer to outputs where the model generates text that is factually incorrect or not grounded in the provided input or training data. Understanding the underlying causes of hallucinations is critical for maintaining the reliability of the model.
Which of the following best describes a primary cause of hallucinations in generative models?
- A. The model's incapacity to follow the temperature parameter settings.
- B. The model's training on incomplete or unstructured datasets leading to incorrect generalizations.
- C. The model's use of a greedy decoding strategy without beam search.
- D. The model's over-reliance on token repetition to form coherent sentences.
Correct Answer: B 🗳️
You are developing a Retrieval-Augmented Generation (RAG) system to enhance the responses of a legal chatbot by integrating it with a vast legal document repository. You are using LangChain to build the pipeline, Watson ML for model hosting, and Elasticsearch as your document store.
What would be the most appropriate approach for combining these components into a RAG pipeline?
- A. Use LangChain to chain together query encoding, document retrieval from Elasticsearch, and Watson ML for response generation.
- B. Use LangChain to pre-process documents -> Use Elasticsearch for model storage -> Use Watson ML to retrieve documents and generate responses.
- C. Use Watson ML for document retrieval and response generation -> Use Elasticsearch to store model responses -> Use LangChain for chaining the responses together.
- D. Use Elasticsearch for document retrieval -> Use LangChain to encode the documents -> Generate the response using Watson ML.
Correct Answer: A 🗳️
You are deploying a generative AI model for a financial services company. The model is responsible for automating customer support and providing recommendations. Due to the sensitive nature of financial data, the company emphasizes the need for robust AI governance.
What governance mechanism should you prioritize to ensure compliance with data privacy regulations and maintain trust in AI outputs?
- A. Implementing role-based access control (RBAC) to restrict who can interact with the model.
- B. Using AI explainability techniques to make the model's decisions transparent to regulators and customers.
- C. Ensuring model version control to track changes and updates made to the model during the deployment process.
- D. Regularly retraining the model to avoid performance degradation due to data drift.
Correct Answer: B 🗳️
You are tasked with generating a product description for an e-commerce platform using a generative AI model. However, you notice that the generated text tends to repeat phrases excessively, leading to verbose output. To address this, you decide to adjust the model's temperature parameter.
Which of the following changes would help reduce the repetitiveness of the generated text while maintaining a balance between creativity and coherence?
- A. Decrease the temperature from 0.9 to 0.3
- B. Set the temperature to 0.0
- C. Decrease the temperature from 0.8 to 0.6
- D. Increase the temperature from 0.5 to 1.5
Correct Answer: A 🗳️
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