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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You're developing a generative AI system for a medical diagnosis application that uses patient data. Your responsibility includes designing prompts that extract valuable insights without exposing sensitive patient information.
Which of the following steps is the most effective way to reduce model risks related to privacy while ensuring useful outputs from the AI?

A) Employ differential privacy techniques to add noise to the model's outputs.
B) Restrict the model's output length to reduce the risk of sensitive information leakage.
C) Utilize a smaller model to minimize the likelihood of overfitting sensitive data.
D) Increase the length of the prompts to provide more context, ensuring more accurate results.

2. You are tasked with fine-tuning a generative AI model for text data using synthetic data created through the IBM watsonx platform's user interface. The data you are working with is skewed, containing mostly outliers, and you need to ensure that the synthetic data mimics the distribution accurately.
Which algorithm would be most appropriate for generating synthetic data that mirrors the original distribution, considering the Anderson-Darling test for normality?

A) Bootstrapping
B) Generative Adversarial Networks (GANs)
C) Anderson-Darling Based Synthetic Data Generation (ADS-DG)
D) Decision Trees

3. You are tasked with building a question-answering system using IBM WatsonX's LLM integrated with LangChain. The system needs to retrieve relevant documents from a corpus of research papers and generate accurate, contextually aware responses.
Which of the following steps is most critical when implementing a RAG pattern in this environment?

A) Setting up the vector database to store embeddings of the research papers for semantic search.
B) Fine-tuning WatsonX's LLM on the research papers before enabling retrieval through LangChain.
C) Configuring LangChain to use WatsonX's LLM for exact keyword matching during document retrieval.
D) Using WatsonX LLM to pre-process the research papers before storing them in LangChain's memory.

4. You are tasked with designing a prompt to translate a sentence from English to French using an AI model.
Which of the following prompt would best guide the AI to achieve accurate translation, while maintaining cultural nuance and avoiding literal word-for-word translation?

A) "Translate the following sentence from English to French: 'The weather is nice today.'"
B) "Translate 'The weather is nice today' to French but ensure that the translation reflects word-for-word accuracy and no cultural considerations."
C) "Explain the meaning of 'The weather is nice today' in French."
D) "Translate the sentence 'The weather is nice today' into French and make sure to avoid literal translation, focusing on cultural nuances."

5. In developing an LLM-based conversational AI application using LangChain, you want the AI to perform complex tasks, such as answering questions based on dynamic knowledge from multiple sources (e.g., databases, APIs, etc.).
Which approach using LangChain best supports this requirement by combining various tools into a structured workflow for the AI to follow?

A) Integrate LangChain memory with an agent to handle all external data retrieval without needing to build complex chains.
B) Create a LangChain chain that connects different tools (e.g., API access, database queries) in a sequential or branching manner to process and combine data dynamically.
C) Use a single LangChain agent to directly query all external data sources, allowing it to gather information on demand.
D) Build a chain of multiple LangChain agents, each handling a specific task (e.g., querying an API, accessing a database) to ensure data from various sources is used effectively.

Solutions: