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1Z0-1127-25 Valid Test Sample - Reliable 1Z0-1127-25 Dumps Files

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Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic
Details

Topic 1

Using OCI Generative AI RAG Agents Service: This domain measures the skills of Conversational AI Developers and AI Application Architects in creating and managing RAG agents using OCI Generative AI services. It includes building knowledge bases, deploying agents as chatbots, and invoking deployed RAG agents for interactive use cases. The focus is on leveraging generative AI to create intelligent conversational systems.

Topic 2

Using OCI Generative AI Service: This section evaluates the expertise of Cloud AI Specialists and Solution Architects in utilizing Oracle Cloud Infrastructure (OCI) Generative AI services. It includes understanding pre-trained foundational models for chat and embedding, creating dedicated AI clusters for fine-tuning and inference, and deploying model endpoints for real-time inference. The section also explores OCI's security architecture for generative AI and emphasizes responsible AI practices.

Topic 3

Implement RAG Using OCI Generative AI Service: This section tests the knowledge of Knowledge Engineers and Database Specialists in implementing Retrieval-Augmented Generation (RAG) workflows using OCI Generative AI services. It covers integrating LangChain with Oracle Database 23ai, document processing techniques like chunking and embedding, storing indexed chunks in Oracle Database 23ai, performing similarity searches, and generating responses using OCI Generative AI.

Topic 4

Fundamentals of Large Language Models (LLMs): This section of the exam measures the skills of AI Engineers and Data Scientists in understanding the core principles of large language models. It covers LLM architectures, including transformer-based models, and explains how to design and use prompts effectively. The section also focuses on fine-tuning LLMs for specific tasks and introduces concepts related to code models, multi-modal capabilities, and language agents.

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q85-Q90):

NEW QUESTION # 85
Which role does a "model endpoint" serve in the inference workflow of the OCI Generative AI service?

A. Hosts the training data for fine-tuning custom models
B. Serves as a designated point for user requests and model responses
C. Evaluates the performance metrics of the custom models
D. Updates the weights of the base model during the fine-tuning process

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A "model endpoint" in OCI's inference workflow is an API or interface where users send requests and receive responses from a deployed model-Option B is correct. Option A (weight updates) occurs during fine-tuning, not inference. Option C (metrics) is for evaluation, not endpoints. Option D (training data) relates to storage, not inference. Endpoints enable real-time interaction.
OCI 2025 Generative AI documentation likely describes endpoints under inference deployment.

NEW QUESTION # 86
What do embeddings in Large Language Models (LLMs) represent?

A. The semantic content of data in high-dimensional vectors
B. The color and size of the font in textual data
C. The grammatical structure of sentences in the data
D. The frequency of each word or pixel in the data

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Embeddings in LLMs are high-dimensional vectors that encode the semantic meaning of words, phrases, or sentences, capturing relationships like similarity or context (e.g., "cat" and "kitten" being close in vector space). This allows the model to process and understand text numerically, making Option C correct. Option A is irrelevant, as embeddings don't deal with visual attributes. Option B is incorrect, as frequency is a statistical measure, not the purpose of embeddings. Option D is partially related but too narrow-embeddings capture semantics beyond just grammar.
OCI 2025 Generative AI documentation likely discusses embeddings under data representation or vectorization topics.

NEW QUESTION # 87
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

A. Increasing the temperature removes the impact of the most likely word.
B. Temperature has no effect on probability distribution; it only changes the speed of decoding.
C. Decreasing the temperature broadens the distribution, making less likely words more probable.
D. Increasing the temperature flattens the distribution, allowing for more varied word choices.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Temperature adjusts the softmax distribution in decoding. Increasing it (e.g., to 2.0) flattens the curve, giving lower-probability words a better chance, thus increasing diversity-Option C is correct. Option A exaggerates-top words still have impact, just less dominance. Option B is backwards-decreasing temperature sharpens, not broadens. Option D is false-temperature directly alters distribution, not speed. This controls output creativity.
OCI 2025 Generative AI documentation likely reiterates temperature effects under decoding parameters.

NEW QUESTION # 88
What is the characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

A. It selectively updates only a fraction of weights to reduce computational load and avoid overfitting.
B. It increases the training time as compared to Vanilla fine-tuning.
C. It selectively updates only a fraction of weights to reduce the number of parameters.
D. It updates all the weights of the model uniformly.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning (a Parameter-Efficient Fine-Tuning method) updates a small subset of the model's weights, reducing computational cost and mitigating overfitting compared to Vanilla fine-tuning, which updates all weights. This makes Option C correct. Option A describes Vanilla fine-tuning, not T-Few. Option B is incomplete, as it omits the overfitting benefit. Option D is false, as T-Few typically reduces training time due to fewer updates. T-Few balances efficiency and performance.
OCI 2025 Generative AI documentation likely describes T-Few under fine-tuningoptions.

NEW QUESTION # 89
How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language processing?

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

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Dot Product computes the raw similarity between two vectors, factoring in both magnitude and direction, while Cosine Distance (or similarity) normalizes for magnitude, focusing solely on directional alignment (angle), making Option C correct. Option A is vague-both measure similarity, not distinct content vs. topicality. Option B is false-both address semantics, not syntax. Option D is incorrect-neither measures word overlap or style directly; they operate on embeddings. Cosine is preferred for normalized semantic comparison.
OCI 2025 Generative AI documentation likely explains these metrics under vector similarity in embeddings.

NEW QUESTION # 90
......

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