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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q30-Q35):

NEW QUESTION # 30
In which scenario is soft prompting appropriate compared to other training styles?

A. When there is a significant amount of labeled, task-specific data available
B. When the model needs to be adapted to perform well in a domain on which it was not originally trained
C. When the model requires continued pretraining on unlabeled data
D. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Soft prompting adds trainable parameters (soft prompts) to adapt an LLM without retraining its core weights, ideal for low-resource customization without task-specific data. This makes Option C correct. Option A suits fine-tuning. Option B may require more than soft prompting (e.g., domain fine-tuning). Option D describes pretraining, not soft prompting. Soft prompting is efficient for specific adaptations.
OCI 2025 Generative AI documentation likely discusses soft prompting under PEFT methods.

NEW QUESTION # 31
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

A. By restricting updates to only a specific group of transformer layers
B. By allowing updates across all layers of the model
C. By excluding transformer layers from the fine-tuning process entirely
D. By incorporating additional layers to the base model

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning enhances efficiency by updating only a small subset of transformer layers or parameters (e.g., via adapters), reducing computational load-Option D is correct. Option A (adding layers) increases complexity, not efficiency. Option B (all layers) describes Vanilla fine-tuning. Option C (excluding layers) is false-T-Few updates, not excludes. This selective approach optimizes resource use.
OCI 2025 Generative AI documentation likely details T-Few under PEFT methods.

NEW QUESTION # 32
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?

A. 30 unit hours
B. 40 unit hours
C. 20 unit hours
D. 25 unit hours

Answer: C

Explanation:
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.

NEW QUESTION # 33
In the simplified workflow for managing and querying vector data, what is the role of indexing?

A. To map vectors to a data structure for faster searching, enabling efficient retrieval
B. To compress vector data for minimized storage usage
C. To categorize vectors based on their originating data type (text, images, audio)
D. To convert vectors into a non-indexed format for easier retrieval

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Indexing in vector databases maps high-dimensional vectors to a data structure (e.g., HNSW,Annoy) to enable fast, efficient similarity searches, critical for real-time retrieval in LLMs. This makes Option B correct. Option A is backwards-indexing organizes, not de-indexes. Option C (compression) is a side benefit, not the primary role. Option D (categorization) isn't indexing's purpose-it's about search efficiency. Indexing powers scalable vector queries.
OCI 2025 Generative AI documentation likely explains indexing under vector database operations.

NEW QUESTION # 34
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?

A. It determines the maximum number of tokens the model can generate per response.
B. It specifies a string that tells the model to stop generating more content.
C. It assigns a penalty to frequently occurring tokens to reduce repetitive text.
D. It controls the randomness of the model's output, affecting its creativity.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
The "stop sequence" parameter defines a string (e.g., "." or " ") that, when generated, halts text generation, allowing control over output length or structure-Option A is correct. Option B (penalty) describes frequency/presence penalties. Option C (max tokens) is a separate parameter. Option D (randomness) relates to temperature. Stop sequences ensure precise termination.
OCI 2025 Generative AI documentation likely details stop sequences under generation parameters.

NEW QUESTION # 35
......

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Hugh Fox Hugh Fox

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