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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q31-Q36):

NEW QUESTION # 31
When should you use the T-Few fine-tuning method for training a model?

A. For models that require their own hosting dedicated AI cluster
B. For datasets with hundreds of thousands to millions of samples
C. For complicated semantic understanding improvement
D. For datasets with a few thousand samples or less

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few is ideal for smaller datasets (e.g., a few thousand samples) where full fine-tuning risks overfitting and is computationally wasteful-Option C is correct. Option A (semantic understanding) is too vague-dataset size matters more. Option B (dedicated cluster) isn't a condition for T-Few. Option D (large datasets) favors Vanilla fine-tuning. T-Few excels in low-data scenarios.
OCI 2025 Generative AI documentation likely specifies T-Few use cases under fine-tuning guidelines.

NEW QUESTION # 32
What is the purpose of Retrieval Augmented Generation (RAG) in text generation?

A. To retrieve text from an external source and present it without any modifications
B. To generate text using extra information obtained from an external data source
C. To store text in an external database without using it for generation
D. To generate text based only on the model's internal knowledge without external data

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
RAG enhances text generation by combining an LLM's internal knowledge with external data retrieved from sources (e.g., vector databases), improving accuracy and relevance. This makes Option B correct. Option A describes standalone LLMs, not RAG. Option C misrepresents RAG's purpose-data is used, not just stored. Option D is incorrect-RAG generates new text, not just retrieves. RAG is ideal for dynamic, informed responses.
OCI 2025 Generative AI documentation likely explains RAG under advanced generation techniques.

NEW QUESTION # 33
Which is the main characteristic of greedy decoding in the context of language model word prediction?

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

Answer: A

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 # 34
When does a chain typically interact with memory in a run within the LangChain framework?

A. Continuously throughout the entire chain execution process.
B. Before user input and after chain execution.
C. Only after the output has been generated.
D. After user input but before chain execution, and again after core logic but before output.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, a chain interacts with memory after receiving user input (to load prior context) but before execution (to inform the process), and again after the core logic (to update memory with new context) but before the final output. This ensures context continuity, making Option C correct. Option A is too late, missing pre-execution context. Option B is misordered. Option D overstates interaction, as it's not continuous but at specific points. Memory integration is key for stateful chains.
OCI 2025 Generative AI documentation likely details memory interaction under LangChain workflows.

NEW QUESTION # 35
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 # 36
......

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Unparalleled 1Z0-1127-25 Test Labs - Oracle Cloud Infrastructure 2025 Generative AI Professional Test Labs

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q31-Q36):

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