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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q149-Q154):
NEW QUESTION # 149
You are preparing a dataset to fine-tune a language model for sentiment analysis. The dataset consists of user reviews with a mix of neutral, positive, and negative sentiments.
Which of the following strategies will best ensure that the model learns balanced sentiment detection?
- A. Convert neutral examples into either positive or negative to simplify the task
- B. Focus only on neutral and negative examples to challenge the model
- C. Increase the number of positive sentiment examples in the dataset
- D. Ensure an equal distribution of positive, negative, and neutral sentiment examples
Answer: D
NEW QUESTION # 150
You are tasked with implementing a Retrieval-Augmented Generation (RAG) pipeline for a customer service chatbot. Your goal is to ensure that the model can query a vast knowledge base, retrieve relevant documents, and generate responses that reference these documents. You are using the transformers and faiss libraries in Python to achieve this. After retrieving the relevant passages, the generative model will then process them to formulate a final response.
What is the most appropriate sequence of steps for implementing this RAG pipeline?
- A. Initialize the generative model -> Use FAISS to encode the retrieved documents -> Pass the encoded documents to the transformer model for response generation.
- B. Encode the query using FAISS -> Use the transformer model to retrieve documents from the knowledge base -> Generate a response using the encoded documents.
- C. Use the FAISS library to create an index -> Encode the input query using the transformer model -> Retrieve the most relevant documents -> Pass the retrieved documents to the generative model for response generation.
- D. Generate the response first using the transformer model -> Retrieve documents from the knowledge base -> Encode the documents using FAISS -> Use the retrieved documents to fine-tune the generated response.
Answer: C
NEW QUESTION # 151
When tuning model parameters for a generative AI prompt, which of the following adjustments would most likely increase the model's tendency to generate coherent but less creative responses?
- A. Increasing the temperature parameter to 1.5
- B. Reducing the beam size in beam search from 5 to 1
- C. Using Top-k Sampling with a k value of 100
- D. Decreasing the value of the temperature parameter to 0.2
Answer: D
NEW QUESTION # 152
You have fine-tuned a model and notice several issues in the output, including repeated phrases, incomplete sentences, and factual inaccuracies.
Which of the following methods would best help you detect and resolve these data quality problems?
- A. Analyze token distribution patterns in the generated outputs
- B. Use a higher learning rate to improve model convergence
- C. Apply model quantization to reduce the complexity of the output
- D. Increase the number of training epochs to further refine the model
Answer: A
NEW QUESTION # 153
You are tuning a generative AI model to control the length of the generated responses.
Which of the following parameter configurations will ensure that the model generates responses that are at least 50 tokens long but no longer than 150 tokens?
- A. Setting the minimum tokens to 150 and maximum tokens to 50
- B. Setting no minimum token value but configuring the maximum tokens to 150
- C. Setting the maximum tokens to 50 and minimum tokens to 150
- D. Setting the minimum tokens to 50 and maximum tokens to 150
Answer: D
NEW QUESTION # 154
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