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

NEW QUESTION # 24
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

A. Controls the randomness of the model's output, affecting its creativity
B. Assigns a penalty to tokens that have already appeared in the preceding text
C. Specifies a string that tells the model to stop generating more content
D. Determines the maximum number of tokens the model can generate per response

Answer: A

NEW QUESTION # 25
In LangChain, which retriever search type is used to balance between relevancy and diversity?

A. mmr
B. top k
C. similarity_score_threshold
D. similarity

Answer: D

NEW QUESTION # 26
What does accuracy measure in the context of fine-tuning results for a generative model?

A. The proportion of incorrect predictions made by the model during an evaluation
B. The depth of the neural network layers used in the model
C. The number of predictions a model makes, regardless of whether they are correct or incorrect
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 # 27
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?

A. It com rob the randomness of the model* output, affecting its creativity.
B. It assigns a penalty to frequently occurring tokens to reduce repetitive text.
C. It specifies a string that tells the model to stop generating more content
D. It determines the maximum number of tokens the model can generate per response.

Answer: C

NEW QUESTION # 28
How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model's response?

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

Answer: A

Explanation:
The Retrieval-Augmented Generation (RAG) technique enhances the response generation process of language models by incorporating relevant external documents. RAG Token and RAG Sequence are two variations of this technique.
RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally. This means that during the response generation process, the model continuously retrieves and incorporates information from external documents as it generates each token (or part) of the response. This allows for more dynamic and contextually relevant answers, as the model can adjust its retrieval based on the evolving context of the response.
In contrast, RAG Sequence typically retrieves documents once at the beginning of the response generation and uses those documents to generate the entire response. This approach is less dynamic compared to RAG Token, as it does not adjust the retrieval process during the generation of the response.
Reference
Research articles on Retrieval-Augmented Generation (RAG) techniques
Documentation on advanced language model inference methods

NEW QUESTION # 29
......

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