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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic
Details

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

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.

Topic 3

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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q24-Q29):

NEW QUESTION # 24
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?
The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

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

Answer: B

NEW QUESTION # 25
Which is NOT a typical use case for LangSmith Evaluators?

A. Detecting bias or toxicity
B. Measuring coherence of generated text
C. Evaluating factual accuracy of outputs
D. Aliening code readability

Answer: D

Explanation:
LangSmith Evaluators are not typically used for aligning code readability. Instead, they are used for tasks such as measuring the coherence of generated text, evaluating the factual accuracy of outputs, and detecting bias or toxicity. Evaluators help ensure the quality and reliability of the outputs generated by language models.
Reference
LangSmith documentation on evaluators
Research articles on evaluation metrics for language models

NEW QUESTION # 26
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 a few thousand samples or less
C. For complicated semantical undemanding improvement
D. For data sets with hundreds of thousands to millions of samples

Answer: D

NEW QUESTION # 27
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

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

Answer: D

NEW QUESTION # 28
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 considers the sum of probabilities of the top tokens, whereas Top" selects from the Top k" tokens sorted by probability.
B. 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.
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: A

NEW QUESTION # 29
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