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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q59-Q64):

NEW QUESTION # 59
Why is normalization of vectors important before indexing in a hybrid search system?

A. It ensures that all vectors represent keywords only.
B. It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.
C. It converts all sparse vectors to dense vectors.
D. It significantly reduces the size of the database.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Normalization scales vectors to unit length, ensuring comparisons (e.g., cosine similarity) reflect directional similarity, not magnitude differences, critical for hybrid search accuracy. This makes Option C correct. Option A is false-vectors represent semantics, not just keywords. Option B (size reduction) isn't the goal. Option D (sparse to dense) is unrelated-normalization adjusts length. Normalized vectors ensure fair similarity metrics.
OCI 2025 Generative AI documentation likely explains normalization under vector preprocessing.

NEW QUESTION # 60
What does a cosine distance of 0 indicate about the relationship between two embeddings?

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

Answer: B

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 # 61
Given the following code:
PromptTemplate(input_variables=["human_input", "city"], template=template) Which statement is true about PromptTemplate in relation to input_variables?

A. PromptTemplate supports any number of variables, including the possibility of having none.
B. PromptTemplate requires a minimum of two variables to function properly.
C. PromptTemplate can support only a single variable at a time.
D. PromptTemplate is unable to use any variables.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, PromptTemplate supports any number of input_variables (zero, one, or more), allowing flexible prompt design-Option C is correct. The example shows two, but it's not a requirement. Option A (minimum two) is false-no such limit exists. Option B (single variable) is too restrictive. Option D (no variables) contradicts its purpose-variables are optional but supported. This adaptability aids prompt engineering.
OCI 2025 Generative AI documentation likely covers PromptTemplate under LangChain prompt design.

NEW QUESTION # 62
How does a presence penalty function in language model generation?

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

Answer: B

Explanation:
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.

NEW QUESTION # 63
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of-Thought, Least-to-Most, or Step-Back prompting technique:

A. "Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question."
B. "Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50."
C. "To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, we'll explore how they trap heat in the Earth's atmosphere."A. 1: Step-Back, 2: Chain-of-Thought, 3: Least-to-MostB. 1: Least-to-Most, 2: Chain-of-Thought, 3: Step-BackC. 1: Chain-of-Thought, 2: Step-Back, 3: Least-to-MostD. 1: Chain-of-Thought, 2: Least-to-Most, 3: Step-Back

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt 1: Shows intermediate steps (3 × 4 = 12, then 12 ÷ 4 = 3 sets, $200 ÷ $50 = 4)-Chain-of-Thought.
Prompt 2: Steps back to a simpler problem before the full one-Step-Back.
Prompt 3: OCI 2025 Generative AI documentation likely defines these under prompting strategies.

NEW QUESTION # 64
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