Oracle Oracle Cloud Infrastructure 2025 Generative AI Professional 1z0-1127-25 Exam Dumps: Updated Questions & Answers (October 2025)

Comprehensive and Detailed In-Depth Explanation:

In Dedicated AI Clusters (e.g., in OCI), GPUs are allocated exclusively to a customer for their generative AI tasks, ensuring isolation for security, performance, and privacy. This makes Option B correct. Option A describes shared resources, not dedicated clusters. Option C is false, as GPUs are for computation, not storage. Option D is incorrect, as public Internet connections would compromise security and efficiency.

OCI 2025 Generative AI documentation likely details GPU isolation under DedicatedAI Clusters.

Comprehensive and Detailed In-Depth Explanation:

The “stop sequence” parameter defines a string (e.g., “.” or “\n”) that, when generated, halts text generation, allowing control over output length or structure—Option A is correct. Option B (penalty) describes frequency/presence penalties. Option C (max tokens) is a separate parameter. Option D (randomness) relates to temperature. Stop sequences ensure precise termination.

OCI 2025 Generative AI documentation likely details stop sequences under generation parameters.

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.

Comprehensive and Detailed In-Depth Explanation:

Loss is a metric that quantifies the difference between a model’s predictions and the actual target values, indicating how incorrect (or “wrong”) the predictions are. Lower loss means better performance, making Option B correct. Option A is false—loss isn’t about prediction count. Option C is incorrect—loss decreases as the model improves, not increases. Option D is wrong—loss measures overall error, not just correct predictions. Loss guides training optimization.

OCI 2025 Generative AI documentation likely defines loss under model training and evaluation metrics.

Comprehensive and Detailed In-Depth Explanation:

Fine-tuning is suitable when an LLM underperforms on a specific task and prompt engineering alone isn’t feasible due to large, task-specific data that can’t be efficiently included in prompts. This adjusts the model’s weights, making Option B correct. Option A suggests no customization is needed. Option C favors RAG for latest data, not fine-tuning. Option D is vague—fine-tuning requires data and goals, not just optimization without direction. Fine-tuning excels with substantial task-specific data.

OCI 2025 Generative AI documentation likely outlines fine-tuning use cases under customization strategies.

Comprehensive and Detailed In-Depth Explanation:

In OCI, unit hours typically equal actual hours of cluster activity unless specified otherwise (e.g., per GPU scaling). For 10 hours of activity, it’s 10 hours × 1 unit/hour = 10 unit hours, but options suggest a multiplier (common in cloud pricing). Assuming a standard 2-unit/hour rate (e.g., for GPU clusters), it’s 10 × 2 = 20 unit hours—Option C fits best. Options A, B, and D imply inconsistent rates (2.5, 4, 3).

OCI 2025 Generative AI documentation likely specifies unit hour rates under DedicatedAI Cluster pricing.

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.

Comprehensive and Detailed In-Depth Explanation:

Few-shot prompting involves providing a few examples in the prompt to guide the LLM’s behavior, leveraging its in-context learning ability without requiring retraining or additional computational resources. This makes Option C correct. Option A is false, as few-shot prompting doesn’t expand the dataset. Option B overstates the case, as inference still requires resources. Option D is incorrect, as latency isn’t significantly affected by few-shot prompting.

OCI 2025 Generative AI documentation likely highlights few-shot prompting in sections on efficient customization.

Comprehensive and Detailed In-Depth Explanation:

Prompt engineering involves crafting and refining input prompts to guide an LLM to produce desired outputs without altering its internal structure or parameters. It’s an iterative process that leverages the model’s pre-trained knowledge, making Option A correct. Option B is unrelated, as adding layers pertains to model architecture design, not prompting. Option C refers to hyperparameter tuning (e.g., temperature), not prompt engineering. Option D describes pretraining or fine-tuning, not prompt engineering.

OCI 2025 Generative AI documentation likely covers prompt engineering in sections on model interaction or inference.

Comprehensive and Detailed In-Depth Explanation:

A presence penalty reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use, to discourage repetition. This makes Option D correct. Option A (equal penalties) ignores prior appearance. Option B is the opposite—penalizing unused tokens isn’t the intent. Option C (more than twice) adds an arbitrary threshold not typically used. Presence penalty enhances output variety.

OCI 2025 Generative AI documentation likely details presence penalty under generation control parameters.