Name: Oracle Cloud Infrastructure 2024 Generative AI Professional
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Free Oracle 1Z0-1127-24 Exam Actual Questions

The questions for 1Z0-1127-24 were last updated On Nov 20, 2024

Question No. 1

Which is a key characteristic of the annotation process used in T-Few fine-tuning?

AT-Few fine-tuning uses annotated data to adjust a fraction of model weights.

BT-Few fine-tuning requires manual annotation of input-output pain.

CT- Few fine-tuning involves updating the weights of all layers in the model.

DT-Few fine-tuning relies on unsupervised learning techniques for annotation.

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Correct Answer: D

Question No. 2

What does a dedicated RDMA cluster network do during model fine-tuning and inference?

AIt leads to higher latency in model inference.

BIt enables the deployment of multiple fine-tuned models.

CIt limits the number of fine-tuned model deployable on the same GPU cluster.

DIt increases G PU memory requirements for model deployment.

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Correct Answer: B

Question No. 3

Which is NOT a typical use case for LangSmith Evaluators?

AMeasuring coherence of generated text

BAliening code readability

CEvaluating factual accuracy of outputs

DDetecting bias or toxicity

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Correct Answer: B

Question No. 4

Which is a key advantage of usingT-Few over Vanilla fine-tuning in the OCI Generative AI service?

AReduced model complexity

BEnhanced generalization to unseen data

CIncreased model interpretability

DFoster training time and lower cost

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Correct Answer: D

Question No. 5

What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?

The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

AThe difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

BThe percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation

CThe improvement in accuracy achieved by the model during training on the user-uploaded data set

DThe level of incorrectness in the models predictions, with lower values indicating better performance

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Correct Answer: D