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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q284-Q289):

NEW QUESTION # 284
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. Naive Bayes Classifier
B. WordNet
C. GPT-3 Embeddings
D. BERT

Answer: D

NEW QUESTION # 285
In which of the following scenarios would zero-shot prompting be more effective than few-shot prompting when interacting with a generative AI model?

A. When the prompt is designed for a general task like summarizing a text, which the model is pre-trained on.
B. When the model is expected to perform a novel task it has never seen, but the prompt can include several examples for guidance.
C. When the goal is to adjust the model's response based on few labeled examples that help refine its predictions.
D. When the task requires highly domain-specific knowledge that the model has not been exposed to before.

Answer: A

NEW QUESTION # 286
You are building a Retrieval-Augmented Generation (RAG) system where documents are converted into embeddings. You decide to use a transformer-based model to convert your text into embeddings. The embeddings will later be used in a vector search engine. After generating the embeddings, you observe that similar documents are not being clustered closely in the vector space, leading to poor retrieval.
What could be a likely reason for this behavior, and how can you address it?

A. The embedding vectors are being generated using sentence-level embeddings instead of word-level embeddings.
B. The model has not been fine-tuned for generating document embeddings, leading to inaccurate representations.
C. The vector search algorithm is using Euclidean distance, which is inappropriate for high-dimensional embedding spaces.
D. The model's output is not normalized, which can cause issues during the vector search process.

Answer: D

NEW QUESTION # 287
A team is fine-tuning a large language model (LLM) for a healthcare application. They have decided to implement a taxonomy tree-based curation to prepare their dataset of medical records and patient interactions.
What is the primary benefit of using a taxonomy tree in the curation process for such a model?

A. It provides a hierarchical structure that helps the model understand the relationships between different medical concepts.
B. It eliminates the need for data cleaning and preprocessing since the taxonomy provides the structure.
C. It reduces the overall size of the dataset by filtering out irrelevant data.
D. It ensures that the model only focuses on specific medical specialties by eliminating other categories.

Answer: A

NEW QUESTION # 288
A generative AI team is optimizing a model for text summarization. The team uses both hard prompts and soft prompts during training.
What is the primary reason a soft prompt might provide less explainability compared to a hard prompt?

A. Soft prompts are explicitly written by humans and include domain-specific instructions, making them harder to explain.
B. Soft prompts involve complex embedding vectors, which are learned and difficult for humans to interpret directly.
C. Soft prompts include manually designed templates that change dynamically, reducing transparency.
D. Soft prompts are used exclusively in unsupervised learning settings, where explainability is inherently lower due to lack of labeled data.

Answer: B

NEW QUESTION # 289
......

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