LEAD2PASSEXAM ORACLE 1Z0-1127-24 EXAM QUESTIONS IN PDF FORMAT

Lead2PassExam Oracle 1z0-1127-24 Exam Questions in PDF Format

Lead2PassExam Oracle 1z0-1127-24 Exam Questions in PDF Format

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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 (Q13-Q18):

NEW QUESTION # 13
What does accuracy measure in the context of fine-tuning results for a generative model?

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

Answer: A

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 # 14
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

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

Answer: A

Explanation:
In the "Show Likelihoods" feature of language model token generation, a higher number assigned to a token indicates that the token is more likely to follow the current token. This likelihood is based on the model's probability distribution, where tokens with higher probabilities are considered more likely to be the next in the sequence. This feature helps in understanding the model's decision-making process and the relative probabilities of different tokens.
Reference
Technical documentation on language model token generation
Research articles on probability distributions in generative models


NEW QUESTION # 15
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. Unlike RAG Sequence, RAG Token generates the entire response at once without considering individual parts.
  • D. RAG Token does not use document retrieval but generates responses based on pre-existing knowledge only.

Answer: B


NEW QUESTION # 16
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

  • A. When you want to optimize the model without any instructions
  • B. When the LLM does not perform well on a task and the data for prompt engineering is too large
  • C. When the LLM requires access to the latest data for generating outputs
  • D. When the LLM already understands the topics necessary for text generation

Answer: B

Explanation:
Fine-tuning is a technique used to customize an existing Large Language Model (LLM) by training it on domain-specific or task-specific data. Fine-tuning is necessary when:
The LLM's General Knowledge is Insufficient - If the model struggles with a specialized domain (e.g., medical, legal, finance), fine-tuning helps by exposing it to relevant domain-specific data.
Prompt Engineering is Ineffective Due to Large Data Requirements - When a task requires significant custom instructions or examples, fine-tuning is a better approach than prompt engineering, which may have length and complexity limitations.
Improved Accuracy is Required - Fine-tuning helps tailor the model to perform specific tasks more accurately, as it learns from additional training data.
Adapting to a Changing Knowledge Base - Fine-tuning can help update the model with recent trends or company-specific data that were not available during its initial training.
???? Oracle Generative AI Reference:
Oracle supports LLM fine-tuning within its AI ecosystem, allowing enterprises to optimize pre-trained AI models for industry-specific applications.


NEW QUESTION # 17
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

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

Answer: B

Explanation:
The "temperature" parameter in generative AI models controls the randomness of the model's output. It affects the creativity and diversity of the generated text:
Low temperature: Leads to more deterministic and focused outputs, where the model tends to choose the most probable tokens, resulting in less randomness and creativity.
High temperature: Increases randomness by making the probability distribution over the next tokens flatter. This allows for more diverse and creative outputs, as the model is more likely to choose less probable tokens.
Adjusting the temperature parameter enables fine-tuning the balance between creativity and coherence in the model's responses.
Reference
Research articles on the role of temperature in generative models
Technical guides for tuning generative AI models in OCI


NEW QUESTION # 18
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