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| Topic |
Details |
| 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.
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| Topic 2 |
- 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.
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| Topic 3 |
- 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.
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| 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.
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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q38-Q43):
NEW QUESTION # 38
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?
- A. The token will be the only one considered in the next generation step.
- B. The token is unrelated to the current token and will not be used.
- C. The token is less likely to follow the current token.
- D. The token is more likely to follow the current token.
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In "Show Likelihoods," a higher number (probability score) indicates a token's greater likelihood of following the current token, reflecting the model's prediction confidence-Option B is correct. Option A (less likely) is the opposite. Option C (unrelated) misinterprets-likelihood ties tokens contextually. Option D (only one) assumes greedy decoding, not the feature's purpose. This helps users understand model preferences.
OCI 2025 Generative AI documentation likely explains "Show Likelihoods" under token generation insights.
NEW QUESTION # 39
How does a presence penalty function in language model generation when using OCI Generative AI service?
- A. It penalizes a token each time it appears after the first occurrence.
- B. It only penalizes tokens that have never appeared in the text before.
- C. It applies a penalty only if the token has appeared more than twice.
- D. It penalizes all tokens equally, regardless of how often they have appeared.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty in LLMs (including OCI's service) reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use. This discourages repetition, making Option D correct. Option A is false, as penalties depend on prior appearance, not uniform application. Option B is the opposite-penalizing unused tokens isn't the goal. Option C is incorrect, as the penalty isn't threshold-based (e.g., more than twice) but applied per reoccurrence. This enhances output diversity.
OCI 2025 Generative AI documentation likely details presence penalty under generation parameters.
NEW QUESTION # 40
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?
- A. Hierarchical relationships; important for structuring database queries
- B. Temporal relationships; necessary for predicting future linguistic trends
- C. Semantic relationships; crucial for understanding context and generating precise language
- D. Linear relationships; they simplify the modeling process
Answer: C
Explanation:
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.
NEW QUESTION # 41
What is the purpose of embeddings in natural language processing?
- A. To create numerical representations of text that capture the meaning and relationships between words or phrases
- B. To compress text data into smaller files for storage
- C. To translate text into a different language
- D. To increase the complexity and size of text data
Answer: A
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 # 42
Which is a key characteristic of the annotation process used in T-Few fine-tuning?
- A. T-Few fine-tuning relies on unsupervised learning techniques for annotation.
- B. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.
- C. T-Few fine-tuning requires manual annotation of input-output pairs.
- D. T-Few fine-tuning involves updating the weights of all layers in the model.
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few, a Parameter-Efficient Fine-Tuning (PEFT) method, uses annotated (labeled) data to selectively update a small fraction of model weights, optimizing efficiency-Option A is correct. Option B is false-manual annotation isn't required; the data just needs labels. Option C (all layers) describes Vanilla fine-tuning, not T-Few. Option D (unsupervised) is incorrect-T-Few typically uses supervised, annotated data. Annotation supports targeted updates.
OCI 2025 Generative AI documentation likely details T-Few's data requirements under fine-tuning processes.
NEW QUESTION # 43
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