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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q105-Q110):

NEW QUESTION # 105
When debating the drawbacks of soft prompts in a generative AI application, which of the following is the most significant challenge compared to hard prompts?

A. Soft prompts significantly limit the flexibility of the model because they are tied to specific tasks, unlike hard prompts which can generalize to various scenarios.
B. Soft prompts offer simpler debugging processes because the learned embeddings are directly linked to specific model behaviors and outputs.
C. Soft prompts require more human intervention during generation because they depend on predefined patterns and rules for guidance.
D. Soft prompts introduce more complexity during the training phase, as the model must learn embeddings that are not inherently interpretable by humans.

Answer: D

NEW QUESTION # 106
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 works best when combined with temperature scaling to increase randomness
B. Greedy decoding guarantees the highest overall probability for the output sequence
C. Greedy decoding maximizes local probabilities but can lead to suboptimal global coherence
D. Greedy decoding is highly effective when multiple equally probable tokens are available at each step

Answer: C

NEW QUESTION # 107
IBM Watsonx Tuning Studio allows users to fine-tune pre-trained models for their specific use cases.
Which of the following correctly describes the primary benefits of using Tuning Studio for optimizing a generative AI model?

A. It fully retrains the base model from scratch, ensuring the highest possible accuracy for each new task, regardless of prior training.
B. It allows users to add new architectural layers to the model to improve accuracy without retraining the entire model.
C. It enables on-the-fly model optimization during inference, adjusting model weights dynamically based on real-time data input.
D. It significantly reduces the computational costs associated with model fine-tuning by only updating the model's parameters relevant to the specific task, preserving the general knowledge of the base model.

Answer: D

NEW QUESTION # 108
You want a generative AI model to summarize a lengthy text in one sentence. You provide the following prompt: "Summarize the following paragraph in one sentence: 'Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn. The field of AI includes everything from speech recognition to problem-solving and robotics.'" No prior examples are given.
What type of prompting is being used, and what are the expectations?

A. Zero-shot prompting, but the model may fail without detailed instructions on what aspects of the text to focus on.
B. Zero-shot prompting, but it requires the addition of a few examples for the model to generate a summary.
C. Few-shot prompting, but the model will likely struggle without a few examples of summaries provided.
D. Zero-shot prompting, with the expectation that the model can summarize common concepts like AI due to pre-training.

Answer: D

NEW QUESTION # 109
Which of the following is the most effective approach when planning for data elements to optimize application usage in IBM watsonx generative AI models?

A. Use feature selection techniques to reduce dimensionality, enhancing model efficiency without sacrificing performance.
B. Aggregate similar data types to minimize the need for feature selection during model optimization.
C. Ensure that all features are included in the model to capture as much data context as possible, regardless of their relevance.
D. Select only high-dimensional features to increase the complexity of the model and boost its predictive power.

Answer: A

NEW QUESTION # 110
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