VALID TEST 1Z0-1127-24 TESTKING | ORACLE 1Z0-1127-24 LATEST DUMPS FILES: ORACLE CLOUD INFRASTRUCTURE 2024 GENERATIVE AI PROFESSIONAL PASS CERTIFY

Valid Test 1z0-1127-24 Testking | Oracle 1z0-1127-24 Latest Dumps Files: Oracle Cloud Infrastructure 2024 Generative AI Professional Pass Certify

Valid Test 1z0-1127-24 Testking | Oracle 1z0-1127-24 Latest Dumps Files: Oracle Cloud Infrastructure 2024 Generative AI Professional Pass Certify

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q30-Q35):

NEW QUESTION # 30
Which statement best describes the role of encoder and decoder models in natural language processing?

  • A. Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.
  • B. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.
  • C. Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.
  • D. Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words.

Answer: D


NEW QUESTION # 31
Which statement describes the difference between Top V and Top p" in selecting the next token in the OCI Generative AI Generation models?

  • A. 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 token.
  • B. Top K considers the sum of probabilities of the top tokens, whereas Top" selects from the Top k" tokens sorted by probability.
  • C. Top k and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.
  • 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


NEW QUESTION # 32
Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?

  • A. They are more expensive but provide higher quality data.
  • B. They increase the cost due to the need for real- time updates.
  • 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:
Using vector databases with Large Language Models (LLMs) offers cost-related benefits, particularly by providing real-time updated knowledge bases. This approach can be more cost-effective than fine-tuning LLMs frequently, as vector databases allow for the dynamic retrieval of information without the need for constant retraining. This reduces operational costs while maintaining access to up-to-date data.
Reference
Articles on the cost efficiency of vector databases
Research on integrating vector databases with LLMs for real-time updates


NEW QUESTION # 33
An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?

  • A. A Large Language Model based agent that focuses on generating textual responses
  • B. A language model that operates on a token-by-token output basis
  • C. A Retrieval Augmented Generation (RAG) model that uses text as input and output
  • D. A diffusion model that specializes in producing complex outputs.

Answer: D

Explanation:
An AI development company aiming to create an assistant capable of analyzing images and generating descriptive text, as well as converting text descriptions into accurate visual representations, would likely focus on integrating a diffusion model. Diffusion models are advanced generative models that specialize in producing complex outputs, including high-quality images from textual descriptions and vice versa.
Reference
Research papers on diffusion models and their applications
Technical documentation on generative models for image and text synthesis


NEW QUESTION # 34
What does a cosine distance of 0 indicate about the relationship between two embeddings?

  • A. They are completely dissimilar
  • B. They are unrelated
  • C. They have the same magnitude
  • D. They are similar in direction

Answer: D

Explanation:
Cosine distance (or cosine similarity) is a metric used to measure the angular similarity between two vectors in high-dimensional space.
Cosine Distance Calculation:
Cosine similarity formula:

The value ranges from -1 to 1:
1 → Vectors are identical.
0 → Vectors are orthogonal (unrelated).
-1 → Vectors are completely opposite.
Why a Cosine Distance of 0 Means Similar Direction:
A cosine similarity of 1 means vectors point in the same direction.
A cosine distance of 0 means maximum similarity (no angular difference).
Why Other Options Are Incorrect:
(A) is incorrect because a cosine distance of 0 implies similarity, not dissimilarity.
(B) is incorrect because unrelated vectors have a cosine similarity close to 0, not exactly 0.
(C) is incorrect because cosine similarity does not measure vector magnitude, only direction.
???? Oracle Generative AI Reference:
Oracle's vector search and embedding-based AI models rely on cosine similarity for semantic search, recommendation systems, and NLP tasks.


NEW QUESTION # 35
......

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