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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. When using few-shot prompting, which of the following is a common limitation you may encounter while working with Watsonx AI for text generation tasks?

A) Few-shot prompting requires the model to be retrained with additional examples.
B) The model may overfit to the few examples provided, leading to overly specific responses.
C) Few-shot prompting is only effective for classification tasks and cannot be used for text generation.
D) Few-shot prompting cannot handle open-ended tasks, as it only works for structured data inputs.

2. In the context of Retrieval-Augmented Generation (RAG) models, embeddings play a crucial role in retrieving relevant documents.
Which of the following best describes the purpose of embeddings in GenAI, particularly within a RAG system?

A) Embeddings are dense vector representations of words, phrases, or entire documents that capture semantic relationships, making it easier to find similar content in the knowledge base during retrieval.
B) Embeddings represent each word in a fixed-length vector based on its semantic meaning, which allows for precise retrieval of documents by directly matching words between the query and the documents.
C) Embeddings are primarily used to measure the grammatical correctness of the generated responses by checking the sentence structure in the generated text.
D) Embeddings are used to compress large documents into a smaller format, enabling faster storage and retrieval within a RAG system.

3. You are tasked with integrating third-party embedding models into a Retrieval-Augmented Generation (RAG) system for document retrieval. Several models offer pre-trained embeddings that can be leveraged for a variety of downstream tasks.
Which of the following third-party models is designed for generating embeddings that capture semantic meaning and context, making it ideal for a RAG-based GenAI system?

A) BERT
B) Naive Bayes Classifier
C) WordNet
D) GPT-3 Embeddings

4. You are fine-tuning a pre-trained language model on a dataset of financial news articles to improve its ability to generate summaries of financial reports. After several epochs of training, you observe that the model performs well on the training data, achieving near-perfect accuracy. However, the model's performance on the validation set is much lower, indicating potential overfitting.
What is the most effective adjustment to reduce overfitting while continuing to fine-tune the model?

A) Increase the learning rate
B) Apply dropout during training
C) Increase the number of epochs
D) Reduce the size of the training dataset

5. You are building a customer support chatbot for an e-commerce company using IBM watsonx and LangChain. The chatbot will interact with an external database that holds customer order history, shipping details, and product catalog data. You need to create a LangChain chain that dynamically generates responses using prompt templates tailored to customer queries, retrieves data from the external database, and incorporates LLMs to refine the answers. The goal is to provide accurate, context-aware responses to questions about order status and product details.
Which LangChain strategy will best ensure that the chatbot provides accurate, dynamic responses based on real-time customer data?

A) Use a RetrievalChain to query the external database and combine the retrieved data with a dynamic prompt template before sending it to an LLM.
B) Design a ParallelChain where multiple LLMs process different aspects of the customer query, such as order history and product details, combining them in the final answer.
C) Implement a SimpleChain that directly connects the chatbot to the external database and generates responses from pre-defined LLM outputs.
D) Apply a MemoryChain that remembers past customer queries and uses this memory to answer future questions more accurately.

Solutions: