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Oracle 1Z0-1127-25 Exam Syllabus Topics:
Topic
Details
Topic 1
- 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 2
- 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 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.
Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q12-Q17):
NEW QUESTION # 12
What is the purpose of Retrievers in LangChain?
- A. To break down complex tasks into smaller steps
- B. To train Large Language Models
- C. To combine multiple components into a single pipeline
- D. To retrieve relevant information from knowledge bases
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Retrievers in LangChain fetch relevant information (e.g., documents, embeddings) from external knowledge bases (like vector stores) to provide context for LLM responses, especially in RAG setups. This makes Option B correct. Option A (training) is unrelated-Retrievers operate at inference. Option C (task breakdown) pertains to prompting techniques, not retrieval. Option D (pipeline combination) describes chains, not Retrievers specifically. Retrievers enhance context awareness.
OCI 2025 Generative AI documentation likely defines Retrievers under LangChain components.
NEW QUESTION # 13
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?
- A. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
- B. The level of incorrectness in the model's predictions, with lower values indicating better performance
- C. The improvement in accuracy achieved by the model during training on the user-uploaded dataset
- D. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Loss measures the discrepancy between a model's predictions and true values, with lower values indicating better fit-Option D is correct. Option A (accuracy difference) isn't loss-it's a derived metric. Option B (error percentage) is closer to error rate, not loss. Option C (accuracy improvement) is a training outcome, not loss's definition. Loss is a fundamental training signal.
OCI 2025 Generative AI documentation likely defines loss under fine-tuning metrics.
NEW QUESTION # 14
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?
- A. Decreasing the temperature broadens the distribution, making less likely words more probable.
- B. Temperature has no effect on probability distribution; it only changes the speed of decoding.
- C. Increasing the temperature removes the impact of the most likely word.
- 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 # 15
What is the purpose of embeddings in natural language processing?
- A. To translate text into a different language
- B. To compress text data into smaller files for storage
- C. To create numerical representations of text that capture the meaning and relationships between words or phrases
- D. To increase the complexity and size of text data
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Embeddings in NLP are dense, numerical vectors that represent words, phrases, or sentences in a way that captures their semantic meaning and relationships (e.g., "king" and "queen" being close in vector space). This enables models to process text mathematically, making Option C correct. Option A is false, as embeddings simplify processing, not increase complexity. Option B relates to translation, not embeddings' primary purpose. Option D is incorrect, as embeddings aren't primarily for compression but for representation.
OCI 2025 Generative AI documentation likely covers embeddings under data preprocessing or vector databases.
NEW QUESTION # 16
How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?
- A. Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance.
- B. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity.
- C. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity.
- D. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In RAG, "Groundedness" assesses whether the response is factually correct and supported by retrieved data, while "Answer Relevance" evaluates how well the response addresses the user's query. Option A captures this distinction accurately. Option B is off-groundedness isn't just contextual alignment, and relevance isn't about syntax. Option C swaps the definitions. Option D misaligns-groundedness isn't solely data integrity, and relevance isn't lexical diversity. This distinction ensures RAG outputs are both true and pertinent.
OCI 2025 Generative AI documentation likely defines these under RAG evaluation metrics.
NEW QUESTION # 17
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