MOCK ORACLE 1Z0-1127-25 EXAM | 1Z0-1127-25 TESTKING LEARNING MATERIALS

Mock Oracle 1Z0-1127-25 Exam | 1Z0-1127-25 Testking Learning Materials

Mock Oracle 1Z0-1127-25 Exam | 1Z0-1127-25 Testking Learning Materials

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2025 Mock 1Z0-1127-25 Exam | Valid 1Z0-1127-25 Testking Learning Materials: Oracle Cloud Infrastructure 2025 Generative AI Professional

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q34-Q39):

NEW QUESTION # 34
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

  • A. T-Few fine-tuning involves updating the weights of all layers in the model.
  • B. T-Few fine-tuning requires manual annotation of input-output pairs.
  • C. T-Few fine-tuning relies on unsupervised learning techniques for annotation.
  • D. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few, a Parameter-Efficient Fine-Tuning (PEFT) method, uses annotated (labeled) data to selectively update a small fraction of model weights, optimizing efficiency-Option A is correct. Option B is false-manual annotation isn't required; the data just needs labels. Option C (all layers) describes Vanilla fine-tuning, not T-Few. Option D (unsupervised) is incorrect-T-Few typically uses supervised, annotated data. Annotation supports targeted updates.
OCI 2025 Generative AI documentation likely details T-Few's data requirements under fine-tuning processes.


NEW QUESTION # 35
When should you use the T-Few fine-tuning method for training a model?

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

Answer: C

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 # 36
Which is a key advantage of using T-Few over Vanilla fine-tuning in the OCI Generative AI service?

  • A. Faster training time and lower cost
  • B. Increased model interpretability
  • C. Reduced model complexity
  • D. Enhanced generalization to unseen data

Answer: A

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 # 37
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

  • A. Emphasis on syntactic clustering of word embeddings
  • B. Capacity to translate text in over 100 languages
  • C. Improved retrievals for Retrieval Augmented Generation (RAG) systems
  • D. Support for tokenizing longer sentences

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Cohere Embed v3, as an advanced embedding model, is designed with improved performance for retrieval tasks, enhancing RAG systems by generating more accurate, contextually rich embeddings. This makes Option B correct. Option A (tokenization) isn't a primary focus-embedding quality is. Option C (syntactic clustering) is too narrow-semantics drives improvement. Option D (translation) isn't an embedding model's role. v3 boosts RAG effectiveness.
OCI 2025 Generative AI documentation likely highlights Embed v3 under supported models or RAG enhancements.


NEW QUESTION # 38
Why is normalization of vectors important before indexing in a hybrid search system?

  • A. It ensures that all vectors represent keywords only.
  • B. It significantly reduces the size of the database.
  • C. It converts all sparse vectors to dense vectors.
  • D. It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Normalization scales vectors to unit length, ensuring comparisons (e.g., cosine similarity) reflect directional similarity, not magnitude differences, critical for hybrid search accuracy. This makes Option C correct. Option A is false-vectors represent semantics, not just keywords. Option B (size reduction) isn't the goal. Option D (sparse to dense) is unrelated-normalization adjusts length. Normalized vectors ensure fair similarity metrics.
OCI 2025 Generative AI documentation likely explains normalization under vector preprocessing.


NEW QUESTION # 39
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