Free 1Z0-1127-24 Exam Questions - Easiest Way for Success

Prepare for the Oracle 1Z0-1127-24 exam questions with our authentic preparation materials, including free 1Z0-1127-24 practice exam questions and answers. TheExamsLab provides all the support you need to succeed in the Oracle Cloud Infrastructure 2024 Generative AI Professional 1Z0-1127-24 exam. This dedication to student success is why we have the most satisfied 1Z0-1127-24 certification exam candidates worldwide.

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Total 146 Questions | Updated On: Sep 13, 2024

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Question 1

What is a potential drawback of using RAG in OCI Generative AI applications?

A : RAG is computationally expensive due to the retrieval process.

B : RAG can only be used with specific pre-trained models offered by OCI.

C : RAG requires extensive training data for the Retriever component.

D : RAG outputs are limited to factual summaries and lack creativity.

Answer: A

Question 2

Compared to traditional LLM generation, how does RAG potentially impact the training data requirements for OCI Generative AI projects?

A : RAG always requires a significantly larger dataset for training the LLM.

B : RAG can potentially lead to effective results even with smaller training datasets.

C : RAG eliminates the need for training data altogether, relying solely on retrieval.

D : The impact of RAG on training data requirements depends solely on th

Answer: B

Question 3

When using the OCI Generative AI service playground, which of the following functionalities is NOT available?

A : Experimenting with pre-trained models for text generation and summarization.

B : Training a custom LLM from scratch on your uploaded dataset.

C : Fine-tuning a pre-trained model with a small amount of data using T-Few.

D : Deploying a fine-tuned model as an endpoint for real-world applicatio

Answer: B

Question 4

When designing a prompt for an LLM, what is the most important factor to consider?

A : The length of the prompt – shorter is always better.

B : Including technical jargon specific to the desired output.

C : Clearly communicating the task and desired outcome.

D : Using informal language and emojis to make the prompt more engaging.

Answer: C

Question 5

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

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

D : 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.

Answer: C

Page: 1 / 30

Total 146 Questions | Updated On: Sep 13, 2024

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