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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q18-Q23):

NEW QUESTION # 18
What does a cosine distance of 0 indicate about the relationship between two embeddings?

A. They are unrelated
B. They are completely dissimilar
C. They are similar in direction
D. They have the same magnitude

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Cosine distance measures the angle between two vectors, where 0 means the vectors point in the same direction (cosine similarity = 1), indicating high similarity in embeddings' semantic content-Option C is correct. Option A (dissimilar) aligns with a distance of 1. Option B is vague-directional similarity matters. Option D (magnitude) isn't relevant-cosine ignores magnitude. This is key for semantic comparison.
OCI 2025 Generative AI documentation likely explains cosine distance under vector database metrics.

NEW QUESTION # 19
What happens if a period (.) is used as a stop sequence in text generation?

A. The model generates additional sentences to complete the paragraph.
B. The model stops generating text after it reaches the end of the current 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 # 20
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

A. Controls the randomness of the model's output, affecting its creativity
B. Determines the maximum number of tokens the model can generate per response
C. Assigns a penalty to tokens that have already appeared in the preceding text
D. Specifies a string that tells the model to stop generating more content

Answer: A

Explanation:
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.

NEW QUESTION # 21
What is prompt engineering in the context of Large Language Models (LLMs)?

A. Adjusting the hyperparameters of the model
B. Adding more layers to the neural network
C. Iteratively refining the ask to elicit a desired response
D. Training the model on a large dataset

Answer: C

Explanation:
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.

NEW QUESTION # 22
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 arethey crucial for language models?

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

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Vector databases store embeddings that preserve semantic relationships (e.g., similarity between "dog" and "puppy") via their positions in high-dimensional space. This accuracy enables LLMs to retrieve contextually relevant data, improving understanding and generation, making Option B correct. Option A (linear) is too vague and unrelated. Option C (hierarchical) applies more to relational databases. Option D (temporal) isn't the focus-semantics drives LLM performance. Semantic accuracy is vital for meaningful outputs.
OCI 2025 Generative AI documentation likely discusses vector database accuracy under embeddings and RAG.

NEW QUESTION # 23
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