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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q29-Q34):

NEW QUESTION # 29
An AI development company is working on an AI-assisted chatbot for a customer, which happens to be an online retail company. The goal is to create an assistant that can best answer queries regarding the company policies as well as retain the chat history throughout a session. Considering the capabilities, which type of model would be the best?

A. A keyword search-based AI that responds based on specific keywords identified in customer queries.
B. An LLM enhanced with Retrieval-Augmented Generation (RAG) for dynamic information retrieval and response generation.
C. A pre-trained LLM model from Cohere or OpenAI.
D. An LLM dedicated to generating text responses without external data integration.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
For a chatbot needing to answer policy queries (requiring up-to-date, specific data) and retain chat history (context awareness), an LLM with RAG is ideal. RAG integrates external data (e.g., policy documents) via retrieval and supports memory for session-long context, making Option B correct. Option A (keyword search) lacks reasoning and context retention. Option C (standalone LLM) can't dynamically fetch policy data. Option D (pre-trained LLM) is too vague and lacks RAG's capabilities. RAG meets both requirements effectively.
OCI 2025 Generative AI documentation likely highlights RAG for dynamic, context-aware applications.

NEW QUESTION # 30
Which statement is true about string prompt templates and their capability regarding variables?

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

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
String prompt templates (e.g., in LangChain) are flexible frameworks that can include zero, one, or multiple variables (placeholders) to customize prompts dynamically. They can be static (no variables) or complex (many variables), making Option C correct. Option A is too restrictive. Option B is false-variables are a core feature. Option D is incorrect, as no minimum is required. This flexibility aids prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt templates under LangChain or prompt design.

NEW QUESTION # 31
Which is the main characteristic of greedy decoding in the context of language model word prediction?

A. It requires a large temperature setting to ensure diverse word selection.
B. It chooses words randomly from the set of less probable candidates.
C. It selects words based on a flattened distribution over the vocabulary.
D. It picks the most likely word at each step of decoding.

Answer: D

Explanation:
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.

NEW QUESTION # 32
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. How many predictions the model made correctly out of all the predictions in an evaluation
C. The depth of the neural network layers used in the model
D. The proportion of incorrect predictions made by the model during an evaluation

Answer: B

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 # 33
Why is normalization of vectors important before indexing in a hybrid search system?

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

Answer: A

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 # 34
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