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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. In what situation might greedy decoding fail to generate an optimal output, even though it consistently chooses the most probable token at each step?

A) Greedy decoding guarantees the highest overall probability for the output sequence
B) Greedy decoding is highly effective when multiple equally probable tokens are available at each step
C) Greedy decoding maximizes local probabilities but can lead to suboptimal global coherence
D) Greedy decoding works best when combined with temperature scaling to increase randomness

2. You are tasked with developing a generative AI application for customer service, using a Watsonx model to generate responses based on user queries. The dataset includes user-provided data such as past interaction histories, query types, and feedback ratings.
Which two approaches should you implement to optimize the usage of these data elements for generating highly relevant responses? (Select two)

A) Use query type as an additional input feature to ensure the model tailors responses accordingly.
B) Apply data augmentation techniques to artificially expand the dataset with new, synthetic interaction histories.
C) Prioritize the use of user feedback ratings over query type for generating more personalized responses.
D) Normalize feedback ratings data by converting them into discrete categories (e.g., low, medium, high).
E) Filter out user interaction history data that is more than six months old to focus on recent patterns.

3. When fine-tuning a model in Tuning Studio, which of the following is a key advantage of this tool in reducing resource costs while improving model performance?

A) It optimizes the model for multilingual capabilities by adding new embeddings.
B) It automatically increases the number of layers for more complex tasks.
C) It expands the model's architecture to handle larger datasets.
D) It allows incremental training, saving computational resources by reusing checkpoints.

4. When generating data for prompt tuning in IBM watsonx, which of the following is the most effective method for ensuring that the model can generalize well to a variety of tasks?

A) Use a diverse set of prompts covering multiple task domains with varying levels of complexity.
B) Focus on generating prompts specific to a single domain to train the model on specialized tasks.
C) Generate a single highly-detailed prompt that covers all potential use cases to maximize generalization.
D) Prioritize prompts with repetitive patterns to help the model memorize key responses.

5. You are using InstructLab to fine-tune a large language model (LLM) for generating technical documentation. The model's output is inconsistent, sometimes too verbose and other times lacking critical details.
Which of the following actions within InstructLab will best help customize the model to consistently produce balanced, concise, yet informative outputs?

A) Use prompt engineering to provide more explicit instructions for the model
B) Lower the batch size during fine-tuning to force the model to focus on smaller chunks of data
C) Increase the number of fine-tuning epochs to ensure the model converges
D) Adjust the token length limit in the model's configuration

Solutions: