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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q72-Q77):

NEW QUESTION # 72
What is the purpose of embeddings in natural language processing?

A. To compress text data into smaller files for storage
B. To translate text into a different language
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 # 73
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

A. Summarization models
B. Embedding models
C. Generation models
D. Translation models

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
OCI Generative AI typically offers pretrained models for summarization (A), generation (B), and embeddings (D), aligning with common generative tasks. Translation models (C) are less emphasized in generative AI services, often handled by specialized NLP platforms, making C the NOT category. While possible, translation isn't a core OCI generative focus based on standard offerings.
OCI 2025 Generative AI documentation likely lists model categories under pretrained options.

NEW QUESTION # 74
What is the characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

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

Answer: C

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 # 75
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

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

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In OCI, fine-tuned models are stored in Object Storage, encrypted by default, ensuring privacy and security per cloud best practices-Option B is correct. Option A (shared) violates privacy. Option C (unencrypted) contradicts security standards. Option D (Key Management) stores keys, not models. Encryption protects customer data.
OCI 2025 Generative AI documentation likely details storage security under fine-tuning workflows.

NEW QUESTION # 76
What is the function of "Prompts" in the chatbot system?

A. They are used to initiate and guide the chatbot's responses.
B. They store the chatbot's linguistic knowledge.
C. They handle the chatbot's memory and recall abilities.
D. They are responsible for the underlying mechanics of the chatbot.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompts in a chatbot system are inputs provided to the LLM to initiate and steer its responses, often including instructions, context, or examples. They shape the chatbot's behavior without altering its core mechanics, making Option B correct. Option A is false, as knowledge is stored in the model's parameters. Option C relates to the model's architecture, not prompts. Option D pertains to memory systems, not prompts directly. Prompts are key for effective interaction.
OCI 2025 Generative AI documentation likely covers prompts under chatbot design or inference sections.

NEW QUESTION # 77
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