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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q20-Q25):
NEW QUESTION # 20
When should you use the T-Few fine-tuning method for training a model?
- A. For datasets with hundreds of thousands to millions of samples
- B. For complicated semantic understanding improvement
- C. For models that require their own hosting dedicated AI cluster
- 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 # 21
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. 25 unit hours
- D. 20 unit hours
Answer: D
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 # 22
Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?
- A. They increase the cost due to the need for real-time updates.
- B. They are more expensive but provide higher quality data.
- C. They offer real-time updated knowledge bases and are cheaper than fine-tuned LLMs.
- D. They require frequent manual updates, which increase operational costs.
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Vector databases enable real-time knowledge retrieval for LLMs (e.g., in RAG), avoiding the high computational and data costs of fine-tuning an LLM for every update. They store embeddings efficiently, making them a cost-effective alternative to retraining, thus Option B is correct. Option A is false-updates are automated, not manual. Option C misrepresents-real-time capability reduces, not increases, costs compared to fine-tuning. Option D is incorrect-vector databases aren't inherently more expensive; they optimize cost and performance. This makes them economical for dynamic applications.
OCI 2025 Generative AI documentation likely highlights vector database cost benefits under RAG or data management sections.
NEW QUESTION # 23
In the simplified workflow for managing and querying vector data, what is the role of indexing?
- A. To compress vector data for minimized storage usage
- B. To convert vectors into a non-indexed format for easier retrieval
- C. To categorize vectors based on their originating data type (text, images, audio)
- D. To map vectors to a data structure for faster searching, enabling efficient retrieval
Answer: D
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 # 24
Which is a key advantage of using T-Few over Vanilla fine-tuning in the OCI Generative AI service?
- A. Enhanced generalization to unseen data
- B. Reduced model complexity
- C. Increased model interpretability
- D. Faster training time and lower cost
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few, a Parameter-Efficient Fine-Tuning method, updates fewer parameters than Vanilla fine-tuning, leading to faster training and lower computational costs-Option D is correct. Option A (complexity) isn't directly affected-structure remains. Option B (generalization) may occur but isn't the primary advantage. Option C (interpretability) isn't a focus. Efficiency is T-Few's hallmark.
OCI 2025 Generative AI documentation likely compares T-Few and Vanilla under fine-tuning benefits.
NEW QUESTION # 25
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