PASS GUARANTEED QUIZ NEWEST ORACLE - 1Z0-1127-25 LATEST EXAM LABS

Pass Guaranteed Quiz Newest Oracle - 1Z0-1127-25 Latest Exam Labs

Pass Guaranteed Quiz Newest Oracle - 1Z0-1127-25 Latest Exam Labs

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q21-Q26):

NEW QUESTION # 21
Accuracy in vector databases contributes to the effectiveness of Large Language Models (LLMs) by preserving a specific type of relationship. What is the nature of these relationships, and why arethey crucial for language models?

  • A. Semantic relationships; crucial for understanding context and generating precise language
  • B. Linear relationships; they simplify the modeling process
  • C. Hierarchical relationships; important for structuring database queries
  • D. Temporal relationships; necessary for predicting future linguistic trends

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Vector databases store embeddings that preserve semantic relationships (e.g., similarity between "dog" and "puppy") via their positions in high-dimensional space. This accuracy enables LLMs to retrieve contextually relevant data, improving understanding and generation, making Option B correct. Option A (linear) is too vague and unrelated. Option C (hierarchical) applies more to relational databases. Option D (temporal) isn't the focus-semantics drives LLM performance. Semantic accuracy is vital for meaningful outputs.
OCI 2025 Generative AI documentation likely discusses vector database accuracy under embeddings and RAG.


NEW QUESTION # 22
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

  • A. When the LLM requires access to the latest data for generating outputs
  • B. When you want to optimize the model without any instructions
  • C. When the LLM does not perform well on a task and the data for prompt engineering is too large
  • D. When the LLM already understands the topics necessary for text generation

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning is suitable when an LLM underperforms on a specific task and prompt engineering alone isn't feasible due to large, task-specific data that can't be efficiently included in prompts. This adjusts the model's weights, making Option B correct. Option A suggests no customization is needed. Option C favors RAG for latest data, not fine-tuning. Option D is vague-fine-tuning requires data and goals, not just optimization without direction. Fine-tuning excels with substantial task-specific data.
OCI 2025 Generative AI documentation likely outlines fine-tuning use cases under customization strategies.


NEW QUESTION # 23
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?

  • A. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
  • B. The improvement in accuracy achieved by the model during training on the user-uploaded dataset
  • C. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
  • D. The level of incorrectness in the model's predictions, with lower values indicating better performance

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Loss measures the discrepancy between a model's predictions and true values, with lower values indicating better fit-Option D is correct. Option A (accuracy difference) isn't loss-it's a derived metric. Option B (error percentage) is closer to error rate, not loss. Option C (accuracy improvement) is a training outcome, not loss's definition. Loss is a fundamental training signal.
OCI 2025 Generative AI documentation likely defines loss under fine-tuning metrics.


NEW QUESTION # 24
Why is normalization of vectors important before indexing in a hybrid search system?

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

Answer: B

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 # 25
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

  • A. Decreasing the temperature broadens the distribution, making less likely words more probable.
  • B. Increasing the temperature flattens the distribution, allowing for more varied word choices.
  • C. Increasing the temperature removes the impact of the most likely word.
  • D. Temperature has no effect on probability distribution; it only changes the speed of decoding.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Temperature adjusts the softmax distribution in decoding. Increasing it (e.g., to 2.0) flattens the curve, giving lower-probability words a better chance, thus increasing diversity-Option C is correct. Option A exaggerates-top words still have impact, just less dominance. Option B is backwards-decreasing temperature sharpens, not broadens. Option D is false-temperature directly alters distribution, not speed. This controls output creativity.
OCI 2025 Generative AI documentation likely reiterates temperature effects under decoding parameters.


NEW QUESTION # 26
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