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Oracle 1z0-1127-24 Exam Syllabus Topics:
Topic
Details
Topic 1

* Building an LLM Application with OCI Generative AI Service: The topic discusses 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 2

* Fundamentals of Large Language Models (LLMs): This topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.
Topic 3

* Using OCI Generative AI Service: It covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.



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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q36-Q41):

NEW QUESTION # 36
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

* A. The token will be the only one considered in the next generation step.
* B. The token is more likely to follow the current token.
* C. The token is less likely to follow the current token.
* D. The token is unrelated to the current token and will not be used.
Answer: B

NEW QUESTION # 37
How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model's response?

* A. RAG Token does not use document retrieval but generates responses based on pre-existing knowledge only.
* B. Unlike RAG Sequence, RAG Token generates the entire response at once without considering individual parts.
* C. RAG Token retrieves documents oar/at the beginning of the response generation and uses those for the entire content
* D. RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally.
Answer: C

NEW QUESTION # 38
ow do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

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

NEW QUESTION # 39
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 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 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: B

NEW QUESTION # 40
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

* A. T- Few fine-tuning involves updating the weights of all layers in the model.
* B. T-Few fine-tuning requires manual annotation of input-output pain.
* C. T-Few fine-tuning relies on unsupervised learning techniques for annotation.
* D. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.
Answer: C

NEW QUESTION # 41
......

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