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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. In the context of Generative AI (GenAI), various embedding models are used to represent textual data.
Which of the following best describes the difference between Word2Vec, BERT, and Sentence-BERT embedding models?

A) Word2Vec captures both word and sentence meanings in a single vector space, BERT generates only word embeddings, and Sentence-BERT generates embeddings for entire documents.
B) Word2Vec captures contextual relationships between words, while BERT and Sentence-BERT generate sentence-level embeddings based on the overall document length.
C) Word2Vec uses a transformer architecture for embedding generation, whereas BERT and Sentence-BERT use neural networks to model context.
D) Word2Vec creates static word embeddings, BERT generates dynamic embeddings based on context, and Sentence-BERT produces embeddings specifically optimized for sentence-level tasks like semantic similarity.

2. 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

3. You are working on optimizing a generative AI model that will handle large-scale text generation tasks. The current model is slow during inference, and you need to improve its performance without increasing operational costs. You decide to use IBM Tuning Studio for optimization.
Which of the following is the most significant benefit of using Tuning Studio in this scenario?

A) It provides guidance on reducing the number of parameters in the model to improve inference speed.
B) It optimizes hyperparameters such as learning rate and batch size to reduce computational overhead during inference.
C) It automatically scales the model up or down depending on the input data size.
D) It pre-loads commonly used datasets, reducing the need for data handling during the training process.

4. You are deploying a new version of a generative AI model in IBM Watsonx, and you want to maintain the integrity of prompt versioning throughout the deployment lifecycle.
Which of the following methods is the most effective for ensuring that the correct prompt version is used with the corresponding model version in production?

A) Use semantic versioning for both the model and the associated prompts, and track them independently in separate systems.
B) Implement model registry tags that associate a specific prompt version with each model version during deployment.
C) Hard-code the prompt within the model deployment script to ensure that the correct prompt is always used
D) Use the latest available prompt version for every deployment, without specifying an exact version

5. You are tasked with explaining the outcomes produced by a Watsonx Generative AI model based on specific prompts.
Which of the following approaches is most effective in ensuring transparency and understanding of how the model arrives at its decisions?

A) Providing an interpretable
B) Explaining the optimization process that minimized the model's loss function

Solutions: