1z0-1127-24 VCE Exam Simulator, 1z0-1127-24 Passing Score

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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 (Q62-Q67):

NEW QUESTION # 62
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. 40 unit hours
B. 30 unit hours
C. 15 unit hours
D. 10 unit hours

Answer: D

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 # 63
Which is the main characteristic of greedy decoding in the context of language model word prediction?

A. It selects words bated on a flattened distribution over the vocabulary.
B. It requires a large temperature setting to ensure diverse word selection.
C. It picks the most likely word email at each step of decoding.
D. It chooses words randomly from the set of less probable candidates.

Answer: C

Explanation:
Greedy decoding in the context of language model word prediction refers to a decoding strategy where, at each step, the model selects the word with the highest probability (the most likely word). This approach is simple and straightforward but can sometimes lead to less diverse or creative outputs because it always opts for the most likely option without considering alternative sequences that might result in better overall sentences.
Reference
Research papers on decoding strategies in language models
Technical documentation on language model inference methods

NEW QUESTION # 64
Given a block of code:
qa = Conversational Retrieval Chain, from 11m (11m, retriever-retv, memory-memory) when does a chain typically interact with memory during execution?

A. Continuously throughout the entire chain execution process
B. Only after the output has been generated
C. After user input but before chain execution, and again after core logic but before output
D. Before user input and after chain execution

Answer: B

NEW QUESTION # 65
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models(LLMS) fundamentally alter their responses?

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

Answer: A

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 # 66
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

NEW QUESTION # 67
......

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1z0-1127-24 VCE Exam Simulator, 1z0-1127-24 Passing Score

Oracle 1z0-1127-24 Exam Syllabus Topics:

Oracle 1z0-1127-24 Exam Dumps - 100% Pass Guarantee With Latest Demo [2025]

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q62-Q67):

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