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2025 High-quality 1z0-1127-24 Reliable Study Notes | 100% Free Latest Oracle Cloud Infrastructure 2024 Generative AI Professional Study Notes

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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic
Details

Topic 1

Building an LLM Application with OCI Generative AI Service: For AI Engineers, this section covers Retrieval Augmented Generation (RAG) concepts, vector database concepts, and semantic search concepts. It also focuses on deploying an LLM, tracing and evaluating an LLM, and building an LLM application with RAG and LangChain.

Topic 2

Fundamentals of Large Language Models (LLMs): For AI developers and Cloud Architects, this topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.

Topic 3

Using OCI Generative AI Service: For AI Specialists, this section covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on the fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q40-Q45):

NEW QUESTION # 40
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.
L 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.
2. 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.
3. To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, well explore how they trap heat in the Earths atmosphere.

A. 1:Chain-of-throught, 2: Least-to-most, 3:Step-Back
B. 1:Least-to-most, 2 Chain-of-Thought, 3:Step-Back
C. 1:Step-Back, 2:Chain-of-Thought, 3:Least-to-most
D. 1:Chain-of-Thought ,2:Step-Back, 3:Least-to most

Answer: A

Explanation:
Chain-of-Thought: The first prompt calculates the total number of wheels and then uses that information to determine how many sets of wheels can be bought. This sequential reasoning process aligns with the Chain-of-Thought technique.
Least-to-most: The second prompt solves a complex problem by first identifying the needed formula and then solving a simpler version before tackling the full question. This incremental approach matches the Least-to-most technique.
Step-Back: The third prompt starts by defining greenhouse gases and then explores their impact on climate change, taking a step back to establish foundational knowledge before addressing the main question.
Reference
Research articles on prompting techniques for language models
Documentation on effective use of prompting strategies

NEW QUESTION # 41
Which is NOT a typical use case for LangSmith Evaluators?

A. Measuring coherence of generated text
B. Aliening code readability
C. Evaluating factual accuracy of outputs
D. Detecting bias or toxicity

Answer: B

Explanation:
LangSmith Evaluators are not typically used for aligning code readability. Instead, they are used for tasks such as measuring the coherence of generated text, evaluating the factual accuracy of outputs, and detecting bias or toxicity. Evaluators help ensure the quality and reliability of the outputs generated by language models.
Reference
LangSmith documentation on evaluators
Research articles on evaluation metrics for language models

NEW QUESTION # 42
Given the following code: chain = prompt |11m

A. LCEL is a declarative and preferred way to compose chains together.
B. LCEL is a programming language used to write documentation for LangChain.
C. LCEL is a legacy method for creating chains in LangChain
D. Which statement is true about LangChain Expression language (ICED?

Answer: A

Explanation:
LangChain Expression Language (LCEL) is a declarative language used to compose chains together in LangChain. It allows users to define the flow and interaction of different components in a clear and concise manner. By using LCEL, developers can easily specify how prompts, models, and other elements should interact, making the process of creating and managing chains more straightforward and efficient. This method is preferred due to its readability and ease of use, compared to more imperative or programmatic approaches.
Reference
LangChain documentation on LCEL
Examples and tutorials on using LangChain Expression Language

NEW QUESTION # 43
Which Oracle Accelerated Data Science (ADS) class can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment?

A. GenerativeAI
B. RetrievalQA
C. Text Leader
D. Chain Deployment

Answer: A

NEW QUESTION # 44
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

A. Top p assigns penalties to frequently occurring tokens.
B. Top p determines the maximum number of tokens per response.
C. Top p limits token selection based on the sum of their probabilities.
D. Top p selects tokens from the "Top k' tokens sorted by probability.

Answer: C

Explanation:
The "Top p" parameter, also known as nucleus sampling, in generative AI models limits token selection based on the sum of their probabilities. It ensures that the cumulative probability of the selected tokens meets or exceeds a specified threshold p. This approach dynamically includes as many tokens as necessary to reach the desired probability sum, allowing for more diverse and contextually appropriate outputs compared to a fixed top-k selection.
Reference
Research papers on nucleus sampling and token selection methods
OCI Generative AI model documentation

NEW QUESTION # 45
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