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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q86-Q91):

NEW QUESTION # 86
What is the purpose of Retrievers in LangChain?

A. To retrieve relevant information from knowledge bases
B. To break down complex tasks into smaller steps
C. To combine multiple components into a single pipeline
D. To train Large Language Models

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Retrievers in LangChain fetch relevant information (e.g., documents, embeddings) from external knowledge bases (like vector stores) to provide context for LLM responses, especially in RAG setups. This makes Option B correct. Option A (training) is unrelated-Retrievers operate at inference. Option C (task breakdown) pertains to prompting techniques, not retrieval. Option D (pipeline combination) describes chains, not Retrievers specifically. Retrievers enhance context awareness.
OCI 2025 Generative AI documentation likely defines Retrievers under LangChain components.

NEW QUESTION # 87
Which statement describes the difference between "Top k" and "Top p" in selecting the next token in the OCI Generative AI Generation models?

A. "Top k" and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.
B. "Top k" considers the sum of probabilities of the top tokens, whereas "Top p" selects from the "Top k" tokens sorted by probability.
C. "Top k" and "Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.
D. "Top k" selects the next token based on its position in the list of probable tokens, whereas "Top p" selects based on the cumulative probability of the top tokens.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
"Top k" sampling selects from the k most probable tokens, based on their ranked position, while "Top p" (nucleus sampling) selects from tokens whose cumulative probability exceeds p, focusing on a dynamic probability mass-Option B is correct. Option A is false-they differ in selection, not penalties. Option C reverses definitions. Option D (frequency) is incorrect-both use probability, not frequency. This distinction affects diversity.
OCI 2025 Generative AI documentation likely contrasts Top k and Top p under sampling methods.

NEW QUESTION # 88
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

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

Answer: C

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 # 89
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours are required for fine-tuning if the cluster is active for 10 days?

A. 744 unit hours
B. 480 unit hours
C. 240 unit hours
D. 20 unit hours

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In OCI, a dedicated AI cluster's usage is typically measured in unit hours, where 1 unit hour = 1 hour of cluster activity. For 10 days, assuming 24 hours per day, the calculation is: 10 days × 24 hours/day = 240 hours. Thus, Option B (240 unit hours) is correct. Option A (480) might assume multiple clusters or higher rates, but the question specifies one cluster. Option C (744) approximates a month (31 days), not 10 days. Option D (20) is arbitrarily low.
OCI 2025 Generative AI documentation likely specifies unit hour calculations under Dedicated AI Cluster pricing.

NEW QUESTION # 90
Which is a distinguishing feature of "Parameter-Efficient Fine-Tuning (PEFT)" as opposed to classic "Fine-tuning" in Large Language Model training?

A. PEFT does not modify any parameters but uses soft prompting with unlabeled data.
B. PEFT involves only a few or new parameters and uses labeled, task-specific data.
C. PEFT modifies all parameters and is typically used when no training data exists.
D. PEFT modifies all parameters and uses unlabeled, task-agnostic data.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
PEFT (e.g., LoRA, T-Few) updates a small subset of parameters (often new ones) using labeled, task-specific data, unlike classic fine-tuning, which updates all parameters-Option A is correct. Option B reverses PEFT's efficiency. Option C (no modification) fits soft prompting, not all PEFT. Option D (all parameters) mimics classic fine-tuning. PEFT reduces resource demands.
OCI 2025 Generative AI documentation likely contrasts PEFT and fine-tuning under customization methods.

NEW QUESTION # 91
......

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q86-Q91):

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