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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q11-Q16):

NEW QUESTION # 11
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

A. The token is less likely to follow the current token.
B. The token is more likely to follow the current token.
C. The token is unrelated to the current token and will not be used.
D. The token will be the only one considered in the next generation step.

Answer: B

NEW QUESTION # 12
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

A. Model Drift
B. Underfitting
C. Data Leakage
D. Overfilling

Answer: D

NEW QUESTION # 13
How does a presence penalty function in language model generation?

A. It penalizes a token each time it appears after the first occurrence.
B. It applies a penalty only if the token has appeared more than twice.
C. It penalizes only tokens that have never appeared in the text before.
D. It penalizes all tokens equally, regardless of how often they have appeared.

Answer: A

Explanation:
A presence penalty is a mechanism used in language model generation to discourage repetition of words or phrases in generated text. This is crucial for improving diversity in AI-generated responses.
How It Works:
The presence penalty increases the loss associated with words that have already appeared in the output.
The model is less likely to generate the same word multiple times, leading to more diverse responses.
Unlike frequency penalties, which increase with repeated occurrences, presence penalties apply as soon as a word appears.
Key Use Cases:
Avoiding redundant phrases in AI-generated text.
Enhancing creative writing applications where repetitive wording is undesirable.
Making chatbot conversations more engaging and natural.
🔹 Oracle Generative AI Reference:
Oracle's generative AI models implement presence and frequency penalties as part of their fine-tuning and model inference processes to balance text coherence and diversity.

NEW QUESTION # 14
Accuracy in vector databases contributes to the effectiveness of Large Language Models (LLMs) by preserving a specific type of relationship.
What is the nature of these relationships, and why are they crucial for language models?

A. Semantic relationships; crucial for understanding context and generating precise language
B. Hierarchical relationships; important for structuring database queries
C. Linear relationships; they simplify the modeling process
D. Temporal relationships; necessary for predicting future linguistic trends

Answer: A

Explanation:
Vector databases store word, sentence, or document embeddings that preserve semantic meaning. These embeddings capture relationships between concepts in a multi-dimensional space, improving LLM performance.
Why Semantic Relationships Are Crucial:
Enhance NLP Models: Ensure that words with similar meanings are closely placed in vector space.
Improve Search and Retrieval: Allow LLMs to retrieve conceptually relevant documents even if exact keywords do not match.
Enable Context-Aware Responses: Helps LLMs generate cohesive and meaningful text.
Why Other Options Are Incorrect:
(A) Hierarchical relationships help in database indexing, but they do not drive semantic understanding.
(B) Linear relationships are too simplistic for complex semantic modeling.
(D) Temporal relationships matter for time-based predictions, not semantic retrieval.
🔹 Oracle Generative AI Reference:
Oracle AI integrates vector databases to enhance LLM retrieval accuracy and semantic search capabilities.

NEW QUESTION # 15
What does the Loss metric indicate about a model's predictions?

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

Answer: D

Explanation:
In machine learning and AI models, the loss metric quantifies the error between the model's predictions and the actual values.
Definition of Loss:
Loss represents how far off the model's predictions are from the expected output.
The objective of training an AI model is to minimize loss, improving its predictive accuracy.
Loss functions are critical in gradient descent optimization, which updates model parameters.
Types of Loss Functions:
Mean Squared Error (MSE) - Used for regression problems.
Cross-Entropy Loss - Used in classification problems (e.g., NLP tasks).
Hinge Loss - Used in Support Vector Machines (SVMs).
Negative Log-Likelihood (NLL) - Common in probabilistic models.
Clarifying Other Options:
(B) is incorrect because loss does not count the number of predictions.
(C) is incorrect because loss focuses on both right and wrong predictions.
(D) is incorrect because loss should decrease as a model improves, not increase.
🔹 Oracle Generative AI Reference:
Oracle AI platforms implement loss optimization techniques in their training pipelines for LLMs, classification models, and deep learning architectures.

NEW QUESTION # 16
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