ORACLE 1Z0-1127-24 RELIABLE PRACTICE QUESTIONS & ACCURATE 1Z0-1127-24 PREP MATERIAL

Oracle 1z0-1127-24 Reliable Practice Questions & Accurate 1z0-1127-24 Prep Material

Oracle 1z0-1127-24 Reliable Practice Questions & Accurate 1z0-1127-24 Prep Material

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Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Fundamentals of Large Language Models (LLMs): For AI developers and Cloud Architects, this topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.
Topic 2
  • Building an LLM Application with OCI Generative AI Service: For AI Engineers, this section covers Retrieval Augmented Generation (RAG) concepts, vector database concepts, and semantic search concepts. It also focuses on deploying an LLM, tracing and evaluating an LLM, and building an LLM application with RAG and LangChain.
Topic 3
  • Using OCI Generative AI Service: For AI Specialists, this section covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on the fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.

>> Oracle 1z0-1127-24 Reliable Practice Questions <<

Quiz 2025 Oracle High Pass-Rate 1z0-1127-24: Oracle Cloud Infrastructure 2024 Generative AI Professional Reliable Practice Questions

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q13-Q18):

NEW QUESTION # 13
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 # 14
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

  • A. Stored in Object Storage encrypted by default
  • B. Shared among multiple customers for efficiency
  • C. Stored in an unencrypted form in Object Storage
  • D. Stored in Key Management service

Answer: A

Explanation:
Fine-tuned customer models in the OCI Generative AI service are stored in Object Storage, and they are encrypted by default. This encryption ensures strong data privacy and security by protecting the model data from unauthorized access. Using encrypted storage is a key measure in safeguarding sensitive information and maintaining compliance with security standards.
Reference
OCI documentation on data storage and security practices
Technical details on encryption and data privacy in OCI services


NEW QUESTION # 15
Why is normalization of vectors important before indexing in a hybrid search system?

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

Answer: A

Explanation:
Normalization of vectors is crucial in a hybrid search system because it standardizes the lengths of vectors, ensuring they have a unit norm. This standardization is essential for meaningful comparison using similarity metrics such as Cosine Similarity. Without normalization, the magnitudes of vectors could skew the similarity scores, leading to inaccurate comparisons and search results. Normalizing vectors ensures that the similarity measure focuses purely on the direction of the vectors rather than their magnitude.
Reference
Research papers on vector normalization in information retrieval
Technical documentation on hybrid search systems


NEW QUESTION # 16
Which is the main characteristic of greedy decoding in the context of language model word prediction?

  • A. It chooses words randomly from the set of less probable candidates.
  • B. It picks the most likely word email at each step of decoding.
  • C. It selects words bated on a flattened distribution over the vocabulary.
  • D. It requires a large temperature setting to ensure diverse word selection.

Answer: B


NEW QUESTION # 17
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 and Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.
  • B. Top k and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.
  • C. Top K considers the sum of probabilities of the top tokens, whereas Top" selects from the Top k" tokens sorted by probability.
  • D. 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.

Answer: D

Explanation:
The difference between "Top k" and "Top p" in selecting the next token in generative models lies in their selection criteria:
Top k: This method selects the next token from the top k tokens based on their probability scores. It restricts the selection to a fixed number of the most probable tokens, irrespective of their cumulative probability.
Top p: Also known as nucleus sampling, this method selects tokens based on the cumulative probability until it exceeds a certain threshold p. It dynamically adjusts the number of tokens considered, ensuring that the sum of their probabilities meets or exceeds the specified p value. This allows for a more flexible and often more diverse selection compared to Top k.
Reference
Research articles on sampling techniques in language models
Technical documentation for generative AI models in OCI


NEW QUESTION # 18
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