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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q28-Q33):

NEW QUESTION # 28
What does "k-shot prompting* refer to when using Large Language Models for task-specific applications?

A. The process of training the model on k different tasks simultaneously to improve its versatility
B. Providing the exact k words in the prompt to guide the model's response
C. Limiting the model to only k possible outcomes or answers for a given task
D. Explicitly providing k examples of the intended task in the prompt to guide the models output

Answer: D

Explanation:
K-shot prompting refers to providing the language model with k examples of the task at hand within the prompt. These examples help guide the model's understanding and output by demonstrating the desired format and approach. This technique is used to improve the model's performance on specific tasks by showing it how to handle similar situations.
Reference
Research papers on few-shot learning and prompting techniques
Technical documentation on using examples in prompts for large language models

NEW QUESTION # 29
How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model's response?

A. RAG Token retrieves documents oar/at the beginning of the response generation and uses those for the entire content
B. RAG Token does not use document retrieval but generates responses based on pre-existing knowledge only.
C. Unlike RAG Sequence, RAG Token generates the entire response at once without considering individual parts.
D. RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally.

Answer: A

NEW QUESTION # 30
Which statement is true about string prompt templates and their capability regarding variables?

A. They require a minimum of two variables to function properly.
B. They are unable to use any variables.
C. They can only support a single variable at a time.
D. They support any number of variables, including the possibility of having none.

Answer: D

Explanation:
A string prompt template is a mechanism used to structure prompts dynamically by inserting variables. These templates are commonly used in LLM-powered applications like chatbots, text generation, and automation tools.
How Prompt Templates Handle Variables:
They support an unlimited number of variables or can work without any variables.
Variables are typically denoted by placeholders such as {variable_name} or {{variable_name}} in frameworks like LangChain or Oracle AI.
Users can dynamically populate these placeholders to generate different prompts without rewriting the entire template.
Example of a Prompt Template:
Without variables: "What is the capital of France?"
With one variable: "What is the capital of {country}?"
With multiple variables: "What is the capital of {country}, and what language is spoken there?" Why Other Options Are Incorrect:
(B) is false because templates can work with one or no variables.
(C) is false because templates rely on variables for dynamic input.
(D) is false because templates can handle multiple placeholders.
🔹 Oracle Generative AI Reference:
Oracle integrates prompt engineering capabilities into its AI platforms, allowing developers to create scalable, reusable prompts for various AI applications.

NEW QUESTION # 31
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 analyze the reasoning process of language
D. To monitor the performance of language models

Answer: B

Explanation:
The primary purpose of LangSmith Tracing is to debug issues in language model outputs. LangSmith Tracing allows developers to trace and analyze the sequence of operations and decisions made by the model during the generation process. This helps identify and resolve problems, ensuring the model's outputs are accurate and reliable.
Reference
LangSmith documentation on tracing and debugging
Tutorials on using tracing tools for language model development

NEW QUESTION # 32
Accuracy in vector databases contributes to the effectiveness of Large Language Models (LLMs) by preserving a specific type of relationship.
What is the nature of these relationships, and why are they crucial for language models?

A. Hierarchical relationships; important for structuring database queries
B. Semantic relationships; crucial for understanding context and generating precise language
C. Temporal relationships; necessary for predicting future linguistic trends
D. Linear relationships; they simplify the modeling process

Answer: B

Explanation:
Vector databases store word, sentence, or document embeddings that preserve semantic meaning. These embeddings capture relationships between concepts in a multi-dimensional space, improving LLM performance.
Why Semantic Relationships Are Crucial:
Enhance NLP Models: Ensure that words with similar meanings are closely placed in vector space.
Improve Search and Retrieval: Allow LLMs to retrieve conceptually relevant documents even if exact keywords do not match.
Enable Context-Aware Responses: Helps LLMs generate cohesive and meaningful text.
Why Other Options Are Incorrect:
(A) Hierarchical relationships help in database indexing, but they do not drive semantic understanding.
(B) Linear relationships are too simplistic for complex semantic modeling.
(D) Temporal relationships matter for time-based predictions, not semantic retrieval.
🔹 Oracle Generative AI Reference:
Oracle AI integrates vector databases to enhance LLM retrieval accuracy and semantic search capabilities.

NEW QUESTION # 33
......

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Jim Fisher Jim Fisher

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