Oracle 1z0-1127-25 - Oracle Cloud Infrastructure 2025 Generative AI Professional

Comprehensive and Detailed In-Depth Explanation:

Greedy decoding selects the word with the highest probability at each step, aiming for locally optimal choices without considering future tokens. This makes Option C correct. Option A (random selection) describes sampling, not greedy decoding. Option B (position-based) isn’t how greedy decoding works—it’s probability-driven. Option D (weighted random) aligns with top-k or top-p sampling, not greedy. Greedy decoding is fast but can lack diversity.

OCI 2025 Generative AI documentation likely explains greedy decoding under decoding strategies.

Comprehensive and Detailed In-Depth Explanation:

In OCI, a dedicated AI cluster’s usage is typically measured in unit hours, where 1 unit hour = 1 hour of cluster activity. For 10 days, assuming 24 hours per day, the calculation is: 10 days × 24 hours/day = 240 hours. Thus, Option B (240 unit hours) is correct. Option A (480) might assume multiple clusters or higher rates, but the question specifies one cluster. Option C (744) approximates a month (31 days), not 10 days. Option D (20) is arbitrarily low.

OCI 2025 Generative AI documentation likely specifies unit hour calculations under Dedicated AI Cluster pricing.

Comprehensive and Detailed In-Depth Explanation:

T-Few, a Parameter-Efficient Fine-Tuning method, updates fewer parameters than Vanilla fine-tuning, leading to faster training and lower computational costs—Option D is correct. Option A (complexity) isn’t directly affected—structure remains. Option B (generalization) may occur but isn’t the primary advantage. Option C (interpretability) isn’t a focus. Efficiency is T-Few’s hallmark.

OCI 2025 Generative AI documentation likely compares T-Few and Vanilla under fine-tuning benefits.

Comprehensive and Detailed In-Depth Explanation:

Accuracy in fine-tuning measures the proportion of correct predictions (e.g., matching expected outputs) out of all predictions made during evaluation, reflecting model performance—Option C is correct. Option A (total predictions) ignores correctness. Option B (incorrect proportion) is the inverse—error rate. Option D (layer depth) is unrelated to accuracy. Accuracy is a standard metric for generative tasks.

OCI 2025 Generative AI documentation likely defines accuracy under fine-tuning evaluation metrics.

Comprehensive and Detailed In-Depth Explanation:

The “temperature” parameter adjusts the randomness of an LLM’s output by scaling the softmax distribution—low values (e.g., 0.7) make it more deterministic, high values (e.g., 1.5) increase creativity—Option A is correct. Option B (stop string) is the stop sequence. Option C (penalty) relates to presence/frequency penalties. Option D (max tokens) is a separate parameter. Temperature shapes output style.

OCI 2025 Generative AI documentation likely defines temperature under generation parameters.

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.

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.

Comprehensive and Detailed In-Depth Explanation:

Embeddings in LLMs are high-dimensional vectors that encode the semantic meaning of words, phrases, or sentences, capturing relationships like similarity or context (e.g., "cat" and "kitten" being close in vector space). This allows the model to process and understand text numerically, making Option C correct. Option A is irrelevant, as embeddings don’t deal with visual attributes. Option B is incorrect, as frequency is a statistical measure, not the purpose of embeddings. Option D is partially related but too narrow—embeddings capture semantics beyond just grammar.

OCI 2025 Generative AI documentation likely discusses embeddings under data representation or vectorization topics.

Comprehensive and Detailed In-Depth Explanation:

Temperature controls the randomness of an LLM’s output by adjusting the softmax probability distribution over the vocabulary. Increasing temperature (e.g., to 1.5) flattens the distribution, reducing the dominance of high-probability words and allowing more diverse, less predictable choices, making Option C correct. Option A is misleading—higher temperature doesn’t remove the top word’s impact entirely but reduces its relative likelihood. Option B is incorrect, as decreasing temperature sharpens the distribution, favoring likely words, not broadening it. Option D is false, as temperature directly affects the distribution, not just decoding speed. This mechanism is key for balancing creativity and coherence.

OCI 2025 Generative AI documentation likely explains temperature under decoding or output control parameters.