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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q42-Q47):

NEW QUESTION # 42
In the context of large-scale synthetic data generation for fine-tuning a generative AI model, which of the following practices can lead to data that effectively improves the model's performance on downstream tasks?

A. Avoiding any post-processing or filtering of synthetic data to retain diversity
B. Using domain-specific templates to generate synthetic data that reflects the target use case
C. Generating synthetic data that overrepresents the simplest task cases to reduce computational load
D. Randomly generating sentences without adhering to the task-specific instructions

Answer: B

NEW QUESTION # 43
You are configuring an LLM for a product recommendation chatbot. The goal is to balance creativity and relevance, ensuring the chatbot suggests diverse but appropriate products.
Which combination of model parameters will best achieve this? (Select two)

A. Set the temperature to 0.1 for highly deterministic responses
B. Use a top-p (nucleus) sampling value of 0.95 for diverse, relevant outputs
C. Increase the temperature to 1.5 to maximize creativity in suggestions
D. Apply a low top-k value (e.g., k=10) to restrict randomness
E. Set a high penalty for repetition to encourage varied recommendations

Answer: B,D

NEW QUESTION # 44
You are tasked with generating synthetic data for a fine-tuning task on an IBM watsonx model. The goal is to mimic the distribution of existing training data while ensuring the synthetic data maintains its statistical similarity to the original. You are provided with two algorithms, Algorithm A (Kolmogorov-Smirnov Test) and Algorithm B, to assess the similarity between the original and synthetic data distributions.
Which of the following best describes how you should implement synthetic data generation using the User Interface and choose the correct algorithm?

A. Use Algorithm A (Kolmogorov-Smirnov Test) to compare the original and synthetic data distributions, checking for deviations across the entire data range.
B. Use the User Interface to generate synthetic data and validate it using Algorithm A, which compares the distributions' mean values to ensure close alignment.
C. Use Algorithm A (Kolmogorov-Smirnov Test) to match the covariance matrix of the original and synthetic data distributions, ensuring high correlation between data points.
D. Use the User Interface to generate synthetic data and validate it using Algorithm B, which assesses the overall shape of the distributions but does not provide a significance test for statistical similarity.

Answer: A

NEW QUESTION # 45
You are designing a document search application using IBM Watson's RAG architecture. You need to generate vector embeddings for your document corpus, which consists of unstructured text data. The vector embeddings will be used for similarity search in conjunction with a retriever model.
Which of the following steps is a prerequisite to generating effective vector embeddings from the unstructured text using an embedding API?

A. Compressing the text data to reduce its dimensionality before passing it to the embedding API
B. Ensuring that the text data is fully normalized, including removing punctuation and converting all text to lowercase
C. Preprocessing the text by tokenizing it into individual words and removing stop words
D. Choosing a transformer-based model pre-trained on a domain-specific corpus to generate the vector embeddings

Answer: D

NEW QUESTION # 46
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. Apply model quantization to reduce the complexity of the output
B. Increase the number of training epochs to further refine the model
C. Analyze token distribution patterns in the generated outputs
D. Use a higher learning rate to improve model convergence

Answer: C

NEW QUESTION # 47
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