Xhs1991 1z0-1127-24関連受験参考書/すぐにダウンロード

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1z0-1127-24日本語版 & 1z0-1127-24資格受験料

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Oracle 1z0-1127-24 認定試験の出題範囲：

トピック	出題範囲
トピック 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.
トピック 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.
トピック 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 認定 1z0-1127-24 試験問題 (Q14-Q19):

質問 # 14
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. 30 unit hours
D. 15 unit hours

正解：A

質問 # 15
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

A. Stored in an unencrypted form in Object Storage
B. Stored in Key Management service
C. Stored in Object Storage encrypted by default
D. Shared among multiple customers for efficiency

正解：C

質問 # 16
What does the Loss metric indicate about a model's predictions?

A. Loss is a measure that indicates how wrong the model's predictions are.
B. Loss indicates how good a prediction is, and it should increase as the model improves.
C. Loss describes the accuracy of the right predictions rather than the incorrect ones.
D. Loss measures the total number of predictions made by a model.

正解：A

解説：
In machine learning and AI models, the loss metric quantifies the error between the model's predictions and the actual values.
Definition of Loss:
Loss represents how far off the model's predictions are from the expected output.
The objective of training an AI model is to minimize loss, improving its predictive accuracy.
Loss functions are critical in gradient descent optimization, which updates model parameters.
Types of Loss Functions:
Mean Squared Error (MSE) - Used for regression problems.
Cross-Entropy Loss - Used in classification problems (e.g., NLP tasks).
Hinge Loss - Used in Support Vector Machines (SVMs).
Negative Log-Likelihood (NLL) - Common in probabilistic models.
Clarifying Other Options:
(B) is incorrect because loss does not count the number of predictions.
(C) is incorrect because loss focuses on both right and wrong predictions.
(D) is incorrect because loss should decrease as a model improves, not increase.
🔹 Oracle Generative AI Reference:
Oracle AI platforms implement loss optimization techniques in their training pipelines for LLMs, classification models, and deep learning architectures.

質問 # 17
An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?

A. A Large Language Model based agent that focuses on generating textual responses
B. A language model that operates on a token-by-token output basis
C. A diffusion model that specializes in producing complex outputs.
D. A Retrieval Augmented Generation (RAG) model that uses text as input and output

正解：D

質問 # 18
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

A. Support for tokenizing longer sentences
B. Capacity to translate text in over u languages
C. Emphasis on syntactic clustering of word embedding's
D. Improved retrievals for Retrieval Augmented Generation (RAG) systems

正解：D

解説：
The Cohere Embed v3 model distinguishes itself from its predecessor in the OCI Generative AI service primarily through improved retrievals for Retrieval Augmented Generation (RAG) systems. This enhancement means that the new version of the model is better at retrieving relevant documents or passages that can be used to augment the generation of responses. The improvements likely include better embedding quality, which allows the model to find more relevant and contextually appropriate information during the retrieval phase.
Reference
Cohere model documentation and release notes
Technical discussions on improvements in RAG systems

質問 # 19
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1z0-1127-24日本語版: https://www.xhs1991.com/1z0-1127-24.html

BONUS！！！ Xhs1991 1z0-1127-24ダンプの一部を無料でダウンロード：https://drive.google.com/open?id=1tcRQOZTtbPlnqs9xp0q7AvL1sJYtzPnu