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Valid 1z0-1127-24 Exam Dumps | 1z0-1127-24 Valid Exam Pdf

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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.

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

NEW QUESTION # 28
Given the following code: chain = prompt |11m

A. LCEL is a legacy method for creating chains in LangChain
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 declarative and preferred way to compose chains together.

Answer: B

NEW QUESTION # 29
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

A. Determines the maximum number of tokens the model can generate per response
B. Assigns a penalty to tokens that have already appeared in the preceding text
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 # 30
Which statement is true about string prompt templates and their capability regarding variables?

A. They can only support a single variable at a time.
B. They require a minimum of two variables to function properly.
C. They are unable to use any variables.
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
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?

A. By allocating separate GPUS for each model instance
B. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs
C. By loading the entire model into G PU memory for efficient processing
D. By optimizing GPIJ memory utilization for each model's unique para

Answer: B

NEW QUESTION # 32
What does a dedicated RDMA cluster network do during model fine-tuning and inference?

A. It increases G PU memory requirements for model deployment.
B. It leads to higher latency in model inference.
C. It limits the number of fine-tuned model deployable on the same GPU cluster.
D. It enables the deployment of multiple fine-tuned models.

Answer: D

Explanation:
A dedicated RDMA (Remote Direct Memory Access) cluster network is crucial during model fine-tuning and inference because it facilitates high-speed, low-latency communication between GPUs. This capability is essential for scaling up the deployment of multiple fine-tuned models across a GPU cluster.
RDMA allows data to be transferred directly between the memory of different computers without involving the CPU, leading to significantly reduced latency and higher throughput. This efficiency is particularly important in the context of fine-tuning and deploying large language models, where the speed and efficiency of data transfer can impact overall performance and scalability.
By enabling fast and efficient communication, a dedicated RDMA cluster network supports the deployment of multiple fine-tuned models on the same GPU cluster, enhancing both flexibility and scalability in handling various AI workloads.
Reference
Oracle Cloud Infrastructure (OCI) documentation on RDMA cluster networks Technical resources on the benefits of RDMA in high-performance computing environments

NEW QUESTION # 33
......

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