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Oracle 1z0-1127-24 Reliable Test Cost, Training 1z0-1127-24 Online

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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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.

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

NEW QUESTION # 28
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:Least-to-most, 2 Chain-of-Thought, 3:Step-Back
B. 1:Step-Back, 2:Chain-of-Thought, 3:Least-to-most
C. 1:Chain-of-throught, 2: Least-to-most, 3:Step-Back
D. 1:Chain-of-Thought ,2:Step-Back, 3:Least-to most

Answer: B

NEW QUESTION # 29
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. Which statement is true about LangChain Expression language (ICED?
D. LCEL is a legacy method for creating chains in LangChain

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 # 30
ow do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

A. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.
B. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.
C. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.
D. Dot Product measures the magnitude and direction vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.

Answer: D

NEW QUESTION # 31
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours arc required for fine-tuning if the cluster is active for 10 hours?

A. 10 unit hours
B. 40 unit hours
C. 15 unit hours
D. 30 unit hours

Answer: A

Explanation:
When you create a fine-tuning dedicated AI cluster and it is active for 10 hours, the number of unit hours required for fine-tuning is equal to the duration for which the cluster is active. Therefore, if the cluster is active for 10 hours, it requires 10 unit hours. This calculation assumes that the unit hour measurement directly corresponds to the active time of the cluster.
Reference
OCI documentation on unit hours and fine-tuning processes
Usage guidelines for dedicated AI clusters in OCI

NEW QUESTION # 32
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models(LLMS) fundamentally alter their responses?

A. It transforms their architecture from a neural network to a traditional database system.
B. It limits their ability to understand and generate natural language.
C. It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.
D. It enables them to bypass the need for pretraining on large text corpora.

Answer: C

Explanation:
The integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alters their responses by shifting the basis from pretrained internal knowledge to real-time data retrieval. This means that instead of relying solely on the knowledge encoded in the model during training, the LLM can retrieve and incorporate up-to-date and relevant information from an external database in real time. This enhances the model's ability to generate accurate and contextually relevant responses.
Reference
Research papers on Retrieval-Augmented Generation (RAG) techniques
Technical documentation on integrating vector databases with LLMs

NEW QUESTION # 33
......

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