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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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.

Topic 3

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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q29-Q34):

NEW QUESTION # 29
What is the primary purpose of LangSmith Tracing?

A. To monitor the performance of language models
B. To analyze the reasoning process of language
C. To generate test cases for language models
D. To debug issues in language model outputs

Answer: B

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

Answer: D

NEW QUESTION # 31
Which is NOT a typical use case for LangSmith Evaluators?

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

Answer: D

NEW QUESTION # 32
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

A. Retriever
B. Encoder-decoder
C. Generator
D. Ranker

Answer: D

Explanation:
In Retrieval-Augmented Generation (RAG), the component responsible for evaluating and prioritizing the information retrieved by the retrieval system is the Ranker. After the Retriever fetches relevant documents or passages, the Ranker assesses these retrieved items based on their relevance to the query. It then prioritizes them, typically scoring and ordering the documents so that the most pertinent information is considered first in the generation process. This ensures that the generated response is based on the most relevant and useful content available.
Reference
Research papers on RAG (Retrieval-Augmented Generation)
Technical documentation on the architecture of RAG models

NEW QUESTION # 33
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

A. Assigns a penalty to tokens that have already appeared in the preceding text
B. Determines the maximum number of tokens the model can generate per response
C. Controls the randomness of the model's output, affecting its creativity
D. Specifies a string that tells the model to stop generating more content

Answer: C

Explanation:
The "temperature" parameter in generative AI models controls the randomness of the model's output. It affects the creativity and diversity of the generated text:
Low temperature: Leads to more deterministic and focused outputs, where the model tends to choose the most probable tokens, resulting in less randomness and creativity.
High temperature: Increases randomness by making the probability distribution over the next tokens flatter. This allows for more diverse and creative outputs, as the model is more likely to choose less probable tokens.
Adjusting the temperature parameter enables fine-tuning the balance between creativity and coherence in the model's responses.
Reference
Research articles on the role of temperature in generative models
Technical guides for tuning generative AI models in OCI

NEW QUESTION # 34
......

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Ava Davis Ava Davis

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