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Oracle 1Z0-1127-25 Exam Questions
- Oracle Cloud Certifications
- Oracle Cloud Infrastructure Certifications
- Topic 1: 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.
- 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: 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.
Free Oracle 1Z0-1127-25 Exam Actual Questions
Note: Premium Questions for 1Z0-1127-25 were last updated On Jun. 21, 2025 (see below)
Accuracy in vector databases contributes to the effectiveness of Large Language Models (LLMs) by preserving a specific type of relationship. What is the nature of these relationships, and why arethey crucial for language models?
Comprehensive and Detailed In-Depth Explanation=
Vector databases store embeddings that preserve semantic relationships (e.g., similarity between 'dog' and 'puppy') via their positions in high-dimensional space. This accuracy enables LLMs to retrieve contextually relevant data, improving understanding and generation, making Option B correct. Option A (linear) is too vague and unrelated. Option C (hierarchical) applies more to relational databases. Option D (temporal) isn't the focus---semantics drives LLM performance. Semantic accuracy is vital for meaningful outputs.
: OCI 2025 Generative AI documentation likely discusses vector database accuracy under embeddings and RAG.
What differentiates Semantic search from traditional keyword search?
Comprehensive and Detailed In-Depth Explanation=
Semantic search uses embeddings and NLP to understand the meaning, intent, and context behind a query, rather than just matching exact keywords (as in traditional search). This enables more relevant results, even if exact terms aren't present, making Option C correct. Options A and B describe traditional keyword search mechanics. Option D is unrelated, as metadata like date or author isn't the primary focus of semantic search. Semantic search leverages vector representations for deeper understanding.
: OCI 2025 Generative AI documentation likely contrasts semantic and keyword search under search or retrieval sections.
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?
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.
Why is it challenging to apply diffusion models to text generation?
Comprehensive and Detailed In-Depth Explanation=
Diffusion models, widely used for image generation, iteratively denoise data from noise to a structured output. Images are continuous (pixel values), while text is categorical (discrete tokens), making it challenging to apply diffusion directly to text, as the denoising process struggles with discrete spaces. This makes Option C correct. Option A is false---text generation can benefit from complex models. Option B is incorrect---text is categorical. Option D is wrong, as diffusion models aren't inherently image-only but are better suited to continuous data. Research adapts diffusion for text, but it's less straightforward.
: OCI 2025 Generative AI documentation likely discusses diffusion models under generative techniques, noting their image focus.
Given the following code:
PromptTemplate(input_variables=["human_input", "city"], template=template)
Which statement is true about PromptTemplate in relation to input_variables?
Comprehensive and Detailed In-Depth Explanation=
In LangChain, PromptTemplate supports any number of input_variables (zero, one, or more), allowing flexible prompt design---Option C is correct. The example shows two, but it's not a requirement. Option A (minimum two) is false---no such limit exists. Option B (single variable) is too restrictive. Option D (no variables) contradicts its purpose---variables are optional but supported. This adaptability aids prompt engineering.
: OCI 2025 Generative AI documentation likely covers PromptTemplate under LangChain prompt design.
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