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Premium 1z0-1127-24 Files | 1z0-1127-24 Learning Materials

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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 (Q37-Q42):

NEW QUESTION # 37
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours arc required for fine-tuning if the cluster is active for 10 hours?

  • A. 10 unit hours
  • B. 40 unit hours
  • C. 15 unit hours
  • D. 30 unit hours

Answer: A


NEW QUESTION # 38
How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model's response?

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

Answer: D


NEW QUESTION # 39
In LangChain, which retriever search type is used to balance between relevancy and diversity?

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

Answer: B

Explanation:
In LangChain, the "mmr" (Maximal Marginal Relevance) search type is used to balance between relevancy and diversity when retrieving documents. This technique aims to select documents that are not only relevant to the query but also diverse from each other. This helps in avoiding redundancy and ensures that the retrieved set of documents covers a broader aspect of the topic.
Maximal Marginal Relevance (MMR) works by iteratively selecting documents that have high relevance to the query but low similarity to the documents already selected. This ensures that each new document adds new information and perspectives, rather than repeating what is already included.
Reference
LangChain documentation on retrievers and search types
Research papers and articles on Maximal Marginal Relevance (MMR)


NEW QUESTION # 40
Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic Tine- tuning" in Large Language Model training?

  • A. PEFT parameters and b typically used when no training data exists.
  • B. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
  • C. PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies
  • D. PEFT involves only a few or new parameters and uses labeled, task-specific data.

Answer: D


NEW QUESTION # 41
Why is normalization of vectors important before indexing in a hybrid search system?

  • A. It significantly reduces the size of the database.
  • B. It ensures that all vectors represent keywords only.
  • C. It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.
  • D. It converts all sparse vectors to dense vectors.

Answer: C

Explanation:
Normalization of vectors is crucial in a hybrid search system because it standardizes the lengths of vectors, ensuring they have a unit norm. This standardization is essential for meaningful comparison using similarity metrics such as Cosine Similarity. Without normalization, the magnitudes of vectors could skew the similarity scores, leading to inaccurate comparisons and search results. Normalizing vectors ensures that the similarity measure focuses purely on the direction of the vectors rather than their magnitude.
Reference
Research papers on vector normalization in information retrieval
Technical documentation on hybrid search systems


NEW QUESTION # 42
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