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Q1.

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'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.'

B'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.'

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

See the explanation below.

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.

: OCI 2025 Generative AI documentation likely defines these under prompting strategies.

Q2.

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

APEFT involves only a few or new parameters and uses labeled, task-specific data.

BPEFT modifies all parameters and is typically used when no training data exists.

CPEFT does not modify any parameters but uses soft prompting with unlabeled data.

DPEFT modifies all parameters and uses unlabeled, task-agnostic data.

Answer: A

See the explanation below.

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.

Q3.

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 hours?

A25 unit hours

B40 unit hours

C20 unit hours

D30 unit hours

Answer: C

See the explanation below.

Comprehensive and Detailed In-Depth Explanation=

In OCI, unit hours typically equal actual hours of cluster activity unless specified otherwise (e.g., per GPU scaling). For 10 hours of activity, it's 10 hours 1 unit/hour = 10 unit hours, but options suggest a multiplier (common in cloud pricing). Assuming a standard 2-unit/hour rate (e.g., for GPU clusters), it's 10 2 = 20 unit hours---Option C fits best. Options A, B, and D imply inconsistent rates (2.5, 4, 3).

: OCI 2025 Generative AI documentation likely specifies unit hour rates under DedicatedAI Cluster pricing.

Q4.

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' 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.

C'Top k' considers the sum of probabilities of the top tokens, whereas 'Top p' selects from the 'Top k' tokens sorted by probability.

D'Top k' and 'Top p' both select from the same set of tokens but use different methods to prioritize them based on frequency.

Answer: B

See the explanation below.

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.

Q5.

Which role does a "model endpoint" serve in the inference workflow of the OCI Generative AI service?

AUpdates the weights of the base model during the fine-tuning process

BServes as a designated point for user requests and model responses

CEvaluates the performance metrics of the custom models

DHosts the training data for fine-tuning custom models

Answer: B

See the explanation below.

Comprehensive and Detailed In-Depth Explanation=

A ''model endpoint'' in OCI's inference workflow is an API or interface where users send requests and receive responses from a deployed model---Option B is correct. Option A (weight updates) occurs during fine-tuning, not inference. Option C (metrics) is for evaluation, not endpoints. Option D (training data) relates to storage, not inference. Endpoints enable real-time interaction.

: OCI 2025 Generative AI documentation likely describes endpoints under inference deployment.

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