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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. In which scenario would using a soft prompt be more beneficial than a hard prompt in optimizing generative AI outputs?

A) When the model needs to generate a strictly factual output with minimal deviation from the prompt.
B) When the task requires explicit and consistent user instructions to ensure deterministic outcomes.
C) When fine-tuning a pre-trained model for domain-specific tasks, allowing the system to adapt its understanding through learned embeddings.
D) When the prompt needs to be manually adjusted by the user in real time during interaction with the AI.

2. You are optimizing a large language model (LLM) for deployment on edge devices with limited computational resources.
To reduce the model size and improve efficiency without significantly compromising performance, which of the following quantization techniques is most appropriate for this scenario?

A) 32-bit floating point quantization with fine-tuning
B) Binary quantization (1-bit)
C) Post-training 16-bit floating point quantization
D) Post-training 8-bit integer quantization

3. Which of the following statements best describes the primary advantage of applying quantization to a large language model (LLM) during inference?

A) Quantization lowers computational requirements, enabling faster inference with minimal impact on model accuracy.
B) Quantization primarily reduces the size of the training dataset required for an LLM.
C) Quantization allows the model to learn more efficiently during training by focusing on fewer parameters.
D) Quantization automatically improves the accuracy of an LLM by converting all weights to higher precision.

4. Which of the following factors is most likely to contribute to biased outputs in a generative AI system?

A) Prompt length that exceeds the model's context window.
B) Repeatedly using the same prompt for multiple generations.
C) Insufficient diversity in the training dataset used to train the model.
D) The use of too many input parameters in the prompt.

5. You are using IBM Watsonx to control the randomness of a language model's output by adjusting the top-k parameter.
What happens when you reduce the top-k value from 50 to 5 during text generation?

A) Reducing the top-k value reduces the model's ability to predict rare or uncommon words, leading to less accurate outputs.
B) Reducing the top-k value increases the temperature of the model, making the outputs more creative and diverse.
C) The model will now consider only the top 5 most probable tokens at each step, making the output more deterministic and focused.
D) Lowering the top-k value forces the model to generate shorter outputs by limiting the number of tokens available for selection.

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