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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q46-Q51):

NEW QUESTION # 46
Which statement best describes the role of encoder and decoder models in natural language processing?

A. Encoder models and decoder models both convert sequences of words into vector representations without generating new text.
B. Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.
C. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.
D. Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to generate a sequence of words.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In NLP (e.g., transformers), encoders convert input text into a vector representation (encoding meaning), while decoders generate text from such vectors (e.g., in translation or generation). This makes Option C correct. Option A is false-decoders generate text. Option B reverses roles-encoders don't predict next words, and decoders don't encode. Option D oversimplifies-encoders handle text, not just numbers. This is the foundation of seq2seq models.
OCI 2025 Generative AI documentation likely explains encoder-decoder roles under model architecture.

NEW QUESTION # 47
What is the primary purpose of LangSmith Tracing?

A. To generate test cases for language models
B. To debug issues in language model outputs
C. To monitor the performance of language models
D. To analyze the reasoning process of language models

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangSmith Tracing is a tool for debugging and understanding LLM applications by tracking inputs, outputs, and intermediate steps, helping identify issues in complex chains. This makes Option C correct. Option A (test cases) is a secondary use, not primary. Option B (reasoning) overlaps but isn't the core focus-debugging is. Option D (performance) is broader-tracing targets specific issues. It's essential for development transparency.OCI 2025 Generative AI documentation likely covers LangSmith under debugging or monitoring tools.

NEW QUESTION # 48
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

A. Determines the maximum number of tokens the model can generate per response
B. Assigns a penalty to tokens that have already appeared in the preceding text
C. Specifies a string that tells the model to stop generating more content
D. Controls the randomness of the model's output, affecting its creativity

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
The "temperature" parameter adjusts the randomness of an LLM's output by scaling the softmax distribution-low values (e.g., 0.7) make it more deterministic, high values (e.g., 1.5) increase creativity-Option A is correct. Option B (stop string) is the stop sequence. Option C (penalty) relates to presence/frequency penalties. Option D (max tokens) is a separate parameter. Temperature shapes output style.
OCI 2025 Generative AI documentation likely defines temperature under generation parameters.

NEW QUESTION # 49
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?

A. The level of incorrectness in the model's predictions, with lower values indicating better performance
B. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
C. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
D. The improvement in accuracy achieved by the model during training on the user-uploaded dataset

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Loss measures the discrepancy between a model's predictions and true values, with lower values indicating better fit-Option D is correct. Option A (accuracy difference) isn't loss-it's a derived metric. Option B (error percentage) is closer to error rate, not loss. Option C (accuracy improvement) is a training outcome, not loss's definition. Loss is a fundamental training signal.
OCI 2025 Generative AI documentation likely defines loss under fine-tuning metrics.

NEW QUESTION # 50
Which is a key advantage of using T-Few over Vanilla fine-tuning in the OCI Generative AI service?

A. Increased model interpretability
B. Faster training time and lower cost
C. Reduced model complexity
D. Enhanced generalization to unseen data

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few, a Parameter-Efficient Fine-Tuning method, updates fewer parameters than Vanilla fine-tuning, leading to faster training and lower computational costs-Option D is correct. Option A (complexity) isn't directly affected-structure remains. Option B (generalization) may occur but isn't the primary advantage. Option C (interpretability) isn't a focus. Efficiency is T-Few's hallmark.
OCI 2025 Generative AI documentation likely compares T-Few and Vanilla under fine-tuning benefits.

NEW QUESTION # 51
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Oracle - Authoritative 1Z0-1127-25 - Oracle Cloud Infrastructure 2025 Generative AI Professional Customizable Exam Mode

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q46-Q51):

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Nick West Nick West

Biography

Oracle - Authoritative 1Z0-1127-25 - Oracle Cloud Infrastructure 2025 Generative AI Professional Customizable Exam Mode

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q46-Q51):

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