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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q30-Q35):

NEW QUESTION # 30
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

A. Improved retrievals for Retrieval Augmented Generation (RAG) systems
B. Capacity to translate text in over 100 languages
C. Emphasis on syntactic clustering of word embeddings
D. Support for tokenizing longer sentences

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Cohere Embed v3, as an advanced embedding model, is designed with improved performance for retrieval tasks, enhancing RAG systems by generating more accurate, contextually rich embeddings. This makes Option B correct. Option A (tokenization) isn't a primary focus-embedding quality is. Option C (syntactic clustering) is too narrow-semantics drives improvement. Option D (translation) isn't an embedding model's role. v3 boosts RAG effectiveness.
OCI 2025 Generative AI documentation likely highlights Embed v3 under supported models or RAG enhancements.

NEW QUESTION # 31
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

A. "Top p" selects tokens from the "Top k" tokens sorted by probability.
B. "Top p" assigns penalties to frequently occurring tokens.
C. "Top p" determines the maximum number of tokens per response.
D. "Top p" limits token selection based on the sum of their probabilities.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
"Top p" (nucleus sampling) selects tokens whose cumulative probability exceeds a threshold (p), limiting the pool to the smallest set meeting this sum, enhancing diversity-Option C is correct. Option A confuses it with "Top k." Option B (penalties) is unrelated. Option D (max tokens) is a different parameter. Top p balances randomness and coherence.
OCI 2025 Generative AI documentation likely explains "Top p" under sampling methods.
Here is the next batch of 10 questions (81-90) from your list, formatted as requested with detailed explanations. The answers are based on widely accepted principles in generative AI and Large Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical errors have been corrected for clarity.

NEW QUESTION # 32
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 requires manual annotation of input-output pairs.
C. T-Few fine-tuning involves updating the weights of all layers in the model.
D. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.

Answer: D

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 # 33
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 current paragraph.
B. The model generates additional sentences to complete the paragraph.
C. The model ignores periods and continues generating text until it reaches the token limit.
D. The model stops generating text after it reaches the end of the first sentence, even if the token limit is much higher.

Answer: D

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 # 34
Which is the main characteristic of greedy decoding in the context of language model word prediction?

A. It selects words based on a flattened distribution over the vocabulary.
B. It chooses words randomly from the set of less probable candidates.
C. It picks the most likely word at each step of decoding.
D. It requires a large temperature setting to ensure diverse word selection.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Greedy decoding selects the word with the highest probability at each step, optimizing locally without lookahead, making Option D correct. Option A (random low-probability) contradicts greedy's deterministic nature. Option B (high temperature) flattens distributions for diversity, not greediness. Option C (flattened distribution) aligns with sampling, not greedy decoding. Greedy is simple but can lack global coherence.
OCI 2025 Generative AI documentation likely describes greedy decoding under decoding strategies.

NEW QUESTION # 35
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Exam 1Z0-1127-25 Questions Fee, 1Z0-1127-25 Valid Exam Objectives

1Z0-1127-25 Valid Exam Objectives, Exams 1Z0-1127-25 Torrent

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q30-Q35):

Oracle - 1Z0-1127-25 - Oracle Cloud Infrastructure 2025 Generative AI Professional –Valid Exam Questions Fee

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