REMARKABLE 1Z0-1127-24 GUIDE MATERIALS: ORACLE CLOUD INFRASTRUCTURE 2024 GENERATIVE AI PROFESSIONAL DELIVER YOU FIRST-RANK EXAM QUESTIONS - VALIDTORRENT

Remarkable 1z0-1127-24 Guide Materials: Oracle Cloud Infrastructure 2024 Generative AI Professional deliver you first-rank Exam Questions - ValidTorrent

Remarkable 1z0-1127-24 Guide Materials: Oracle Cloud Infrastructure 2024 Generative AI Professional deliver you first-rank Exam Questions - ValidTorrent

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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 Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q31-Q36):

NEW QUESTION # 31
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

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

Answer: B

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 # 32
What does accuracy measure in the context of fine-tuning results for a generative model?

  • A. The number of predictions a model makes, regardless of whether they are correct or incorrect
  • B. The proportion of incorrect predictions made by the model during an evaluation
  • C. The depth of the neural network layers used in the model
  • D. How many predictions the model made correctly out of all the predictions in an evaluation

Answer: D

Explanation:
Accuracy in machine learning measures the proportion of correct predictions made by a model relative to the total predictions during an evaluation.
How Accuracy is Calculated:

A higher accuracy indicates better model performance.
Used primarily in classification tasks, but it can also assess LLM fine-tuning results.
Why Other Options Are Incorrect:
(A) is incorrect because the number of neural network layers does not define accuracy.
(B) is incorrect because accuracy considers correctness, not just total predictions.
(D) is incorrect because accuracy measures correct predictions, not just incorrect ones.
???? Oracle Generative AI Reference:
Oracle AI assesses model fine-tuning performance using accuracy, loss, and perplexity to improve LLM capabilities.


NEW QUESTION # 33
When should you use the T-Few fine-tuning method for training a model?

  • A. For models that require their own hosting dedicated Al duster
  • B. For data sets with hundreds of thousands to millions of samples
  • C. For data sets with a few thousand samples or less
  • D. For complicated semantical undemanding improvement

Answer: C

Explanation:
The T-Few fine-tuning method is particularly suitable for data sets with a few thousand samples or less. This method is designed to be efficient and effective with limited data, making it ideal for scenarios where collecting large amounts of training data is impractical. T-Few fine-tuning allows for meaningful adjustments to the model even with smaller data sets, providing good performance improvements without requiring extensive data.
Reference
Articles on fine-tuning techniques for small data sets
Technical documentation on T-Few fine-tuning in machine learning models


NEW QUESTION # 34
ow do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

  • A. Dot Product measures the magnitude and direction vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.
  • B. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.
  • C. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.
  • D. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.

Answer: A


NEW QUESTION # 35
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?

  • A. By optimizing GPIJ memory utilization for each model's unique para
  • B. By allocating separate GPUS for each model instance
  • C. By loading the entire model into G PU memory for efficient processing
  • D. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs

Answer: D

Explanation:
The architecture of dedicated AI clusters contributes to minimizing GPU memory overhead for fine-tuned model inference by sharing base model weights across multiple fine-tuned models on the same group of GPUs. This approach allows different fine-tuned models to leverage the shared base model weights, reducing the memory requirements and enabling efficient use of GPU resources. By not duplicating the base model weights for each fine-tuned model, the system can handle more models simultaneously with lower memory overhead.
Reference
Technical documentation on AI cluster architectures
Research articles on optimizing GPU memory utilization in model inference


NEW QUESTION # 36
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