RELEVANT 1Z0-1127-24 ANSWERS | 1Z0-1127-24 GUARANTEED QUESTIONS ANSWERS

Relevant 1z0-1127-24 Answers | 1z0-1127-24 Guaranteed Questions Answers

Relevant 1z0-1127-24 Answers | 1z0-1127-24 Guaranteed Questions Answers

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Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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 (Q34-Q39):

NEW QUESTION # 34
Given the following code:
Prompt Template
(input_variable[''rhuman_input",'city''], template-template)
Which statement is true about Promt Template in relation to input_variables?

  • A. PromptTemplate supports Any number of variable*, including the possibility of having none.
  • B. PromptTemplate can support only a single variable M a time.
  • C. PromptTemplate is unable to use any variables.
  • D. PromptTemplate requires a minimum of two variables to function property.

Answer: A

Explanation:
The PromptTemplate in relation to input_variables is designed to be flexible and can support any number of variables, including the possibility of having none. This means that users can define a template with multiple variables or none at all, depending on their specific needs. The PromptTemplate facilitates dynamic prompt creation by inserting variable values into predefined template slots.
Reference
LangChain documentation on PromptTemplate
Examples and tutorials on using PromptTemplate in generative AI applications


NEW QUESTION # 35
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 offer real-time updated knowledge bases and are cheaper than fine-tuned LLMs.
  • C. They increase the cost due to the need for real- time updates.
  • D. They require frequent manual updates, which increase operational costs.

Answer: B

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 # 36
Given a block of code:
qa = Conversational Retrieval Chain, from 11m (11m, retriever-retv, memory-memory) when does a chain typically interact with memory during execution?

  • A. After user input but before chain execution, and again after core logic but before output
  • B. Continuously throughout the entire chain execution process
  • C. Before user input and after chain execution
  • D. Only after the output has been generated

Answer: D


NEW QUESTION # 37
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

  • A. Decreasing the temperature broadens the distribution, making less likely words more probable.
  • B. Temperature has no effect on probability distribution; it only changes the speed of decoding.
  • C. Increasing the temperature flattens the distribution, allowing for more varied word choices.
  • D. Increasing the temperature removes the impact of the most likely word.

Answer: C

Explanation:
Temperature is a parameter in LLM decoding algorithms that controls randomness in text generation.
Effects of Temperature on Text Generation:
Higher Temperature (>1.0):
Flattens the probability distribution, making lower-probability words more likely.
Increases randomness, resulting in more creative and diverse outputs.
Lower Temperature (<1.0):
Sharpening effect, making high-probability words more dominant.
Produces more predictable and deterministic responses.
Why Other Options Are Incorrect:
(B) is incorrect because temperature does not remove the impact of likely words; it reduces or increases randomness.
(C) is incorrect because temperature affects probability, not speed.
(D) is incorrect because decreasing the temperature narrows the distribution, making text more deterministic.
???? Oracle Generative AI Reference:
Oracle AI models allow dynamic temperature control to balance coherence and creativity in text generation.


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

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

Answer: B


NEW QUESTION # 39
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