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1 / 30

What is the main advantage of using few-shot model prompting to customize a Large Language Model (LLM)?

It provides examples in the prompt to guide the LLM to better performance with no training cost

It significantly reduces the latency for each model request

It allows the LLM to access a larger dataset

It eliminates the need for any training or computational resources

2 / 30

When should you use the T-Few fine-tuning method for training a model?

For datasets with a few thousand samples or less

For datasets with hundreds of thousands to millions of samples

For models that require their own hosting dedicated AI cluster

For complicated semantic understanding improvement

3 / 30

What is the purpose of Retrieval Augmented Generation (RAG) in text generation?

To retrieve text from an external source and present it without any modifications

To store text in an external database without using it for generation

To generate text based only on the model's internal knowledge without external data

To generate text using extra information obtained from an external data source

4 / 30

You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training data. How many unit hours are required for fine-tuning if the cluster is active for 10 hours?

20 unit hours

30 unit hours

40 unit hours

25 unit hours

5 / 30

Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

Translation models

Embedding models

Generation models

Summarization models

6 / 30

What is the primary purpose of LangSmith Tracing?

To generate test cases for language models

To debug issues in language model outputs

To monitor the performance of language models

To analyze the reasoning process of language models

7 / 30

What is the purpose of Retrievers in LangChain?

To combine multiple components into a single pipeline

To break down complex tasks into smaller steps

To train Large Language Models

To retrieve relevant information from knowledge bases

8 / 30

Which is a distinctive feature of GPUs in Dedicated AI Clusters used for generative AI tasks?

GPUs are used exclusively for storing large datasets, not for computation

GPUs are shared with other customers to maximize resource utilization

The GPUs allocated for a customer’s generative AI tasks are isolated from other GPUs

Each customer's GPUs are connected via a public Internet network for ease of access

9 / 30

How does the structure of vector databases differ from traditional relational databases?

It is based on distances and similarities in a vector space

It uses simple row-based data storage

It is not optimized for high-dimensional spaces

A vector database stores data in a linear or tabular format

10 / 30

Which is a key characteristic of Large Language Models (LLMs) without Retrieval Augmented Generation (RAG)?

They use vector databases exclusively to produce answers

They cannot generate responses without fine-tuning

They always use an external database for generating responses

They rely on internal knowledge learned during pretraining on a large text corpus

11 / 30

Which role does a "model endpoint" serve in the inference workflow of the OCI Generative AI service?

Evaluates the performance metrics of the custom models

Hosts the training data for fine-tuning custom models

Updates the weights of the base model during the fine-tuning process

Serves as a designated point for user requests and model responses

12 / 30

How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language processing?

Dot Product measures the magnitude and direction of vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude

Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance

Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons

Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity

13 / 30

Which is a distinguishing feature of "Parameter-Efficient Fine-Tuning (PEFT)" as opposed to classic "Fine-tuning" in Large Language Model training?

PEFT does not modify any parameters but uses soft prompting with unlabeled data

PEFT modifies all parameters and is typically used when no training data exists

PEFT involves only a few or new parameters and uses labeled, task-specific data

PEFT modifies all parameters and uses unlabeled, task-agnostic data

14 / 30

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

Support for tokenizing longer sentences

Emphasis on syntactic clustering of word embeddings

Capacity to translate text in over 100 languages

Improved retrievals for Retrieval Augmented Generation (RAG) systems

15 / 30

What do prompt templates use for templating in language model applications?

Python's lambda functions

Python's class and object structures

Python's list comprehension syntax

Python's str.format syntax

16 / 30

What is the characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

It updates all the weights of the model uniformly

It increases the training time as compared to Vanilla fine-tuning

It selectively updates only a fraction of weights to reduce the number of parameters

It selectively updates only a fraction of weights to reduce computational load and avoid overfitting

17 / 30

In which scenario is soft prompting appropriate compared to other training styles?

When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training

When the model requires continued pretraining on unlabeled data

When the model needs to be adapted to perform well in a domain on which it was not originally trained

When there is a significant amount of labeled, task-specific data available

18 / 30

Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data

Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive

PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning

Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs

19 / 30

In the simplified workflow for managing and querying vector data, what is the role of indexing?

To categorize vectors based on their originating data type (text, images, audio)

To compress vector data for minimized storage usage

To convert vectors into a non-indexed format for easier retrieval

To map vectors to a data structure for faster searching, enabling efficient retrieval

20 / 30

What does in-context learning in Large Language Models involve?

Conditioning the model with task-specific instructions or demonstrations

Adding more layers to the model

Pretraining the model on a specific domain

Training the model using reinforcement learning

21 / 30

How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

Stored in Key Management service

Stored in an unencrypted form in Object Storage

Shared among multiple customers for efficiency

Stored in Object Storage encrypted by default

22 / 30

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

Determines the maximum number of tokens the model can generate per response

Assigns a penalty to tokens that have already appeared in the preceding text

Specifies a string that tells the model to stop generating more content

Controls the randomness of the model's output, affecting its creativity

23 / 30

Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

Least-to-Most Prompting

In-Context Learning

Step-Back Prompting

Chain-of-Thought

24 / 30

What issue might arise from using small datasets with the Vanilla fine-tuning method in the OCI Generative AI service?

Data Leakage

Underfitting

Overfitting

Model Drift

25 / 30

What is the purpose of embeddings in natural language processing?

To create numerical representations of text that capture the meaning and relationships between words or phrases

To compress text data into smaller files for storage

To translate text into a different language

To increase the complexity and size of text data

26 / 30

Given the following prompts used with a Large Language Model, classify each as employing the Chain-of-Thought, Least-to-Most, or Step-Back prompting technique:

Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50

To understand the impact of greenhouse gases on climate change, let’s start by defining what greenhouse gases are. Next, we’ll explore how they trap heat in the Earth’s atmosphere."A. 1: Step-Back, 2: Chain-of-Thought, 3: Least-to-MostB. 1: Least-to-Most, 2: Chain-of-Thought, 3: Step-BackC. 1: Chain-of-Thought, 2: Step-Back, 3: Least-to-MostD. 1: Chain-of-Thought, 2: Least-to-Most, 3: Step-Back

Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question

27 / 30

Which statement accurately reflects the differences between these approaches in terms of the
number of parameters modified and the type of data used?

Fine-tuning modifies all parameters using labeled, task-specific data, whereas Parameter Efficient Fine-Tuning updates a few, new parameters also with labeled, task-specific data

Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model

Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data

Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data

28 / 30

What is the role of temperature in the decoding process of a Large Language Model (LLM)?

To increase the accuracy of the most likely word in the vocabulary

To adjust the sharpness of probability distribution over vocabulary when selecting the next word

To decide to which part of speech the next word should belong

To determine the number of words to generate in a single decoding step

29 / 30

What does the term "hallucination" refer to in the context of Large Language Models (LLMs)?

The model's ability to generate imaginative and creative content

The phenomenon where the model generates factually incorrect information or unrelated content as if it were true

The process by which the model visualizes and describes images in detail

A technique used to enhance the model's performance on specific tasks

30 / 30

What is prompt engineering in the context of Large Language Models (LLMs)?

Adjusting the hyperparameters of the model

Adding more layers to the neural network

Iteratively refining the ask to elicit a desired response

Training the model on a large dataset

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Free 1Z0-1127-25 Exam Questions – 2025 Updated

Prepare Better for the 1Z0-1127-25 Exam with Our Free and Reliable 1Z0-1127-25 Exam Questions – Updated for 2025.

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