Newest Oracle 1Z0-1127-25 Practice Questions at Zero Cost

Get a glimpse of the real 1Z0-1127-25 certification exam challenges with our free Oracle 1Z0-1127-25 practice test questions.

Question 1

What is the primary purpose of LangSmith Tracing?

ATo generate test cases for language models

BTo analyze the reasoning process of language models

CTo debug issues in language model outputs

DTo monitor the performance of language models

Correct Answer: 1

Question 2

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

AEncoder models and decoder models both convert sequences of words into vector representations without generating new text.

BEncoder 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.

CEncoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to generate a sequence of words.

DEncoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.

Correct Answer: 2

Question 3

What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

ASupport for tokenizing longer sentences

BImproved retrievals for Retrieval Augmented Generation (RAG) systems

CEmphasis on syntactic clustering of word embeddings

DCapacity to translate text in over 100 languages

Correct Answer: 3

Question 4

What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?

AProviding the exact k words in the prompt to guide the model's response

BExplicitly providing k examples of the intended task in the prompt to guide the model's output

CThe process of training the model on k different tasks simultaneously to improve its versatility

DLimiting the model to only k possible outcomes or answers for a given task

Correct Answer: 4

Question 5

How does a presence penalty function in language model generation?

AIt penalizes all tokens equally, regardless of how often they have appeared.

BIt penalizes only tokens that have never appeared in the text before.

CIt applies a penalty only if the token has appeared more than twice.

DIt penalizes a token each time it appears after the first occurrence.

Correct Answer: 5

Master the Oracle Cloud Infrastructure 2025 Generative AI Professional 1Z0-1127-25 exam like never before! You’ve reviewed the free 1Z0-1127-25 practice questions, but the actual Oracle Cloud certification exam demands more. Elevate your preparation with Certsmarket premium Oracle Cloud 1Z0-1127-25 practice exam questions.

Explore Free Oracle Cloud 1Z0-1127-25 Practice Questions for Exam Mastery

Our Community

What our students say about us?