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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q269-Q274):

NEW QUESTION # 269
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. Maximize BLEU score and reduce memory utilization
B. Reduce memory utilization and maintain BLEU and ROUGE-L scores
C. Focus on improving the ROUGE-L score while increasing memory utilization
D. Increase memory utilization to reduce BLEU and ROUGE-L scores

Answer: B

NEW QUESTION # 270
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. Periodically retrain the model with updated datasets that exclude sensitive attributes 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. 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.
D. Use greedy decoding in the inference phase to ensure deterministic outputs, avoiding potential bias from probabilistic sampling methods.

Answer: C

NEW QUESTION # 271
You've conducted a prompt-tuning experiment, and after reviewing the generated outputs, you observe issues such as incomplete responses, irrelevant content, and occasional factual inaccuracies.
What is the most appropriate action to address these data quality problems?

A. Introduce temperature tuning to adjust the randomness of the model's output and reduce irrelevant content.
B. Fine-tune the model on domain-specific data to improve factual accuracy and relevance.
C. Lower the model's perplexity score to improve both completeness and factual accuracy.
D. Increase the length of the input prompt to ensure that responses are more complete.

Answer: B

NEW QUESTION # 272
You are working on generating creative text responses using IBM watsonx's generative AI model. You need to adjust the output so that it is more diverse and creative without losing coherence.
Which of the following model parameter settings would best achieve this objective?

A. Set temperature to 0
B. Set temperature to 2
C. Set temperature to 1.5
D. Set temperature to 0.2

Answer: C

NEW QUESTION # 273
You are training a generative AI model using IBM's Tuning Studio and want to optimize its performance. You aim to avoid both overfitting and underfitting by carefully selecting the appropriate number of epochs.
Which of the following strategies would best help you set the optimal number of epochs during the tuning process?

A. Use a single epoch to avoid overfitting
B. Set the number of epochs equal to the size of the dataset
C. Gradually increase the number of epochs until the loss on the training set reaches zero
D. Set a high number of epochs and use early stopping to determine the optimal point

Answer: D

NEW QUESTION # 274
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