Improve Your Chances of Success with Oracle's Realistic 1Z0-1127-25 Exam Questions and Accurate Answers

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q45-Q50):

NEW QUESTION # 45
What does the Loss metric indicate about a model's predictions?

A. Loss describes the accuracy of the right predictions rather than the incorrect ones.
B. Loss is a measure that indicates how wrong the model's predictions are.
C. Loss indicates how good a prediction is, and it should increase as the model improves.
D. Loss measures the total number of predictions made by a model.

Answer: B

Explanation:
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.

NEW QUESTION # 46
Which is a key characteristic of Large Language Models (LLMs) without Retrieval Augmented Generation (RAG)?

A. They use vector databases exclusively to produce answers.
B. They cannot generate responses without fine-tuning.
C. They rely on internal knowledge learned during pretraining on a large text corpus.
D. They always use an external database for generating responses.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LLMs without Retrieval Augmented Generation (RAG) depend solely on the knowledge encoded in their parameters during pretraining on a large, general text corpus. They generate responses basedon this internal knowledge without accessing external data at inference time, making Option B correct. Option A is false, as external databases are a feature of RAG, not standalone LLMs. Option C is incorrect, as LLMs can generate responses without fine-tuning via prompting or in-context learning. Option D is wrong, as vector databases are used in RAG or similar systems, not in basic LLMs. This reliance on pretraining distinguishes non-RAG LLMs from those augmented with real-time retrieval.
OCI 2025 Generative AI documentation likely contrasts RAG and non-RAG LLMs under model architecture or response generation sections.

NEW QUESTION # 47
Which is a distinctive feature of GPUs in Dedicated AI Clusters used for generative AI tasks?

A. GPUs are used exclusively for storing large datasets, not for computation.
B. Each customer's GPUs are connected via a public Internet network for ease of access.
C. GPUs are shared with other customers to maximize resource utilization.
D. The GPUs allocated for a customer's generative AI tasks are isolated from other GPUs.

Answer: D

Explanation:
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.

NEW QUESTION # 48
What does accuracy measure in the context of fine-tuning results for a generative model?

A. The number of predictions a model makes, regardless of whether they are correct or incorrect
B. The depth of the neural network layers used in the model
C. How many predictions the model made correctly out of all the predictions in an evaluation
D. The proportion of incorrect predictions made by the model during an evaluation

Answer: C

Explanation:
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.

NEW QUESTION # 49
What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?

A. The process of training the model on k different tasks simultaneously to improve its versatility
B. Providing the exact k words in the prompt to guide the model's response
C. Limiting the model to only k possible outcomes or answers for a given task
D. Explicitly providing k examples of the intended task in the prompt to guide the model's output

Answer: D

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 # 50
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Improve Your Chances of Success with Oracle's Realistic 1Z0-1127-25 Exam Questions and Accurate Answers

Oracle 1Z0-1127-25 Most Reliable Questions, New 1Z0-1127-25 Exam Simulator

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q45-Q50):

Experience The Real Oracle 1Z0-1127-25 Exam With Web-Based Practice Exam Software

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