IBM C1000-185題庫介紹

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C1000-185 考試必讀：考生最常問的幾個問題

最新的 IBM Certified watsonx Generative AI Engineer - Associate C1000-185 免費考試真題：

問題 #1

A generative AI model designed for healthcare content generation is being evaluated for ethical risks. The model tends to give preference to certain demographic groups when recommending treatments.
What is the most effective method to identify and mitigate this bias during the prompt engineering phase?

A. Adjust the temperature to 1.0 to ensure the model generates more balanced and less biased outputs.
B. Limit the model's context window to prevent it from over-relying on demographic information.
C. Use adversarial debiasing techniques to adjust the model's internal representations during training.
D. Train the model on a smaller dataset that excludes demographic information, to remove bias from its learned patterns.

問題 #2

You have applied a set of prompt tuning parameters to a language model and collected the following statistics: ROUGE-L score, BLEU score, and memory utilization.
Based on these metrics, how would you prioritize further optimizations to balance the model's performance in terms of output relevance and resource efficiency?

A. Focus on improving the ROUGE-L score while increasing memory utilization
B. Increase memory utilization to reduce BLEU and ROUGE-L scores
C. Reduce memory utilization and maintain BLEU and ROUGE-L scores
D. Maximize BLEU score and reduce memory utilization

問題 #3

You are working on a generative AI model for a wide variety of language generation tasks. Your team is debating whether to use soft prompts for optimizing the model's performance.
Which of the following is an accurate benefit of using soft prompts, and what is a potential drawback?

A. Benefit: Soft prompts reduce the amount of explicit data needed for training. Drawback: Soft prompts might not generalize well across domains.
B. Benefit: Soft prompts offer more control over the output. Drawback: Soft prompts require additional manual engineering for each task.
C. Benefit: Soft prompts allow for fine-grained control during inference. Drawback: Soft prompts may lead to lower performance with large datasets.
D. Benefit: Soft prompts can optimize performance without retraining the model. Drawback: They are computationally expensive to tune during inference.

問題 #4

When leveraging existing data for fine-tuning an LLM in IBM watsonx, you want to optimize the model for a highly specialized domain. You also want to generate additional synthetic data to augment your dataset.
Which of the following approaches would best help you achieve your goal?

A. Using the watsonx UI to generate synthetic data that mirrors your existing dataset, filling any data gaps
B. Manually crafting complex datasets by sampling individual instances from unrelated domains
C. Relying exclusively on pre-trained general models without making domain-specific modifications
D. Using unsupervised learning on your existing dataset without adding synthetic data

問題 #5

A financial institution is deploying a generative AI model to generate loan approval recommendations based on applicant profiles, including factors like income, credit score, and employment history. The organization is concerned about ensuring that the model does not introduce bias in its recommendations, particularly related to gender and race. You have been asked to design a process to evaluate the model's inferences during deployment and mitigate any potential bias.
Which method would be most effective for evaluating the model's inferences for bias in this deployment scenario?

A. Implement a fairness audit, where a sample of the model's inferences is checked for disparate impact across protected groups such as gender and race.
B. Manually review all loan decisions generated by the model for signs of bias before releasing them to customers.
C. Periodically retrain the model with updated datasets that exclude sensitive attributes such as gender and race.
D. Use greedy decoding in the inference phase to ensure deterministic outputs, avoiding potential bias from probabilistic sampling methods.

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