IBM C1000-185

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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. In a project where watsonx.ai is deployed as the core generative model for text generation, you need to augment its capabilities by integrating it with IBM Watson Discovery to access a large corpus of documents for fact-checking and data retrieval.
What is the best way to integrate these two services to ensure that generated responses are both creative and factual?

A) Train watsonx.ai to automatically search Watson Discovery for factual information during the text generation process, eliminating the need for an external integration layer.
B) Have Watson Discovery preprocess user inputs to identify relevant documents, and then feed the retrieved information into watsonx.ai as a context for text generation.
C) Deploy a custom API layer that combines watsonx.ai with Watson Assistant, letting the latter manage queries and retrieve documents from Watson Discovery on demand.
D) Use watsonx.ai to generate all responses, and periodically train the model using data indexed by Watson Discovery.

2. You are tasked with generating synthetic data for fine-tuning a large language model (LLM) using the IBM Watsonx User Interface. You want to generate relevant training samples to improve the model's accuracy in a text classification task.
Which action should you prioritize to generate high-quality synthetic data using the interface?

A) Customize prompt templates to closely mimic the structure and format of the original training data.
B) Avoid using domain-specific prompts to keep the synthetic data unbiased and more generalizable.
C) Select only a few generic prompts to generate the largest volume of data possible, ensuring variety.
D) Choose a diverse set of prompts that span different domains unrelated to the original dataset.

3. In a Retrieval-Augmented Generation (RAG) system, embeddings play a central role in linking input queries with relevant external knowledge. Different embedding models can be used to generate these embeddings.
Which of the following embedding models is best suited for capturing semantic meaning in text for use in a RAG system?

A) Word2Vec
B) Latent Dirichlet Allocation (LDA)
C) One-Hot Encoding
D) Bag-of-Words (BoW)

4. In the context of prompt engineering, how does the repetition penalty parameter influence the behavior of a generative AI model, and which of the following scenarios demonstrates its correct usage?

A) A repetition penalty of 0.5 is ideal for generating creative text, as it promotes repetition of key phrases for emphasis
B) A repetition penalty higher than 1.0 will discourage the model from repeating the same words or phrases, while a value lower than 1.0 encourages repetition
C) Setting the repetition penalty to 1.0 disables any influence on repetition and allows the model to generate text without penalizing previously used tokens
D) A repetition penalty of 1.0 encourages the model to introduce more varied word choices in its output

5. During the fine-tuning of a large language model (LLM) with InstructLab for a legal document classification task, you notice that the model performs exceptionally well on the training set but poorly on the validation set.
What could be done to address the overfitting issue and improve the model's generalization? (Select two)

A) Fine-tune the model for more epochs to ensure the model has fully learned from the training data.
B) Remove all regularization and fine-tune the model on the training set until convergence.
C) Increase the size of the model to better capture complex patterns in the training data.
D) Use early stopping based on the validation set performance.
E) Introduce dropout regularization during fine-tuning to prevent overfitting.

Solutions: