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[General] Free PDF Oracle - Unparalleled 1Z0-1127-25 - Oracle Cloud Infrastructure 2025 Ge

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【General】 Free PDF Oracle - Unparalleled 1Z0-1127-25 - Oracle Cloud Infrastructure 2025 Ge

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q46-Q51):NEW QUESTION # 46
What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?
  • A. Limiting the model to only k possible outcomes or answers for a given task
  • B. The process of training the model on k different tasks simultaneously to improve its versatility
  • C. Explicitly providing k examples of the intended task in the prompt to guide the model's output
  • D. Providing the exact k words in the prompt to guide the model's response
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
"k-shot prompting" (e.g., few-shot) involves providing k examples of a task in the prompt to guide the LLM's output via in-context learning, without additional training. This makes Option B correct. Option A (k words) misinterprets-examples, not word count, matter. Option C (training) confuses prompting with fine-tuning. Option D (k outcomes) is unrelated-k refers to examples, not limits. k-shot leverages pre-trained knowledge efficiently.
OCI 2025 Generative AI documentation likely covers k-shot prompting under prompt engineering techniques.

NEW QUESTION # 47
Why is normalization of vectors important before indexing in a hybrid search system?
  • A. It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.
  • B. It significantly reduces the size of the database.
  • C. It ensures that all vectors represent keywords only.
  • D. It converts all sparse vectors to dense vectors.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Normalization scales vectors to unit length, ensuring comparisons (e.g., cosine similarity) reflect directional similarity, not magnitude differences, critical for hybrid search accuracy. This makes Option C correct. Option A is false-vectors represent semantics, not just keywords. Option B (size reduction) isn't the goal. Option D (sparse to dense) is unrelated-normalization adjusts length. Normalized vectors ensure fair similarity metrics.
OCI 2025 Generative AI documentation likely explains normalization under vector preprocessing.

NEW QUESTION # 48
What do prompt templates use for templating in language model applications?
  • A. Python's str.format syntax
  • B. Python's list comprehension syntax
  • C. Python's lambda functions
  • D. Python's class and object structures
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt templates in LLM applications (e.g., LangChain) typically use Python's str.format() syntax to insert variables into predefined string patterns (e.g., "Hello, {name}!"). This makes Option B correct. Option A (list comprehension) is for list operations, not templating. Option C (lambda functions) defines functions, not templates. Option D (classes/objects) is overkill-templates are simpler constructs. str.format() ensures flexibility and readability.
OCI 2025 Generative AI documentation likely mentions str.format() under prompt template design.

NEW QUESTION # 49
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 an unencrypted form in Object Storage
  • C. Stored in Key Management service
  • 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 # 50
What happens if a period (.) is used as a stop sequence in text generation?
  • A. The model stops generating text after it reaches the end of the first sentence, even if the token limit is much higher.
  • B. The model generates additional sentences to complete the paragraph.
  • C. The model stops generating text after it reaches the end of the current paragraph.
  • D. The model ignores periods and continues generating text until it reaches the token limit.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
A stop sequence in text generation (e.g., a period) instructs the model to halt generation once it encounters that token, regardless of the token limit. If set to a period, the model stops after the first sentence ends, making Option D correct. Option A is false, as stop sequences are enforced. Option B contradicts the stop sequence's purpose. Option C is incorrect, as it stops at the sentence level, not paragraph.
OCI 2025 Generative AI documentation likely explains stop sequences under text generation parameters.

NEW QUESTION # 51
......
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