[Q14-Q31] Dumps for Free Oracle 1z0-1127-24 Practice Exam Questions [Feb 24, 2026]

Topic	Details
Topic 1	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 2	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 3	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.

Q14. In LangChain, which retriever search type is used to balance between relevancy and diversity?

top k

mmr

similarity_score_threshold

similarity

Q15. How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model’s response?

Unlike RAG Sequence, RAG Token generates the entire response at once without considering individual parts.

RAG Token does not use document retrieval but generates responses based on pre-existing knowledge only.

RAG Token retrieves documents oar/at the beginning of the response generation and uses those for the entire content

RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally.

Q16. Why is normalization of vectors important before indexing in a hybrid search system?

It converts all sparse vectors to dense vectors.

It significantly reduces the size of the database.

It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.

It ensures that all vectors represent keywords only.

Q18. Which Oracle Accelerated Data Science (ADS) class can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment?

RetrievalQA

Text Leader

Chain Deployment

GenerativeAI

Q21. How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

By incorporating additional layers to the base model

By allowing updates across all layers of the model

By excluding transformer layers from the fine-tuning process entirely

By restricting updates to only a specific croup of transformer Layers

Q22. What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

Overfilling

Underfitting

Data Leakage

Model Drift

Q23. 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?

10 unit hours

30 unit hours

15 unit hours

40 unit hours

Q24. Which statement describes the difference between Top V and Top p” in selecting the next token in the OCI Generative AI Generation models?

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.

Top K considers the sum of probabilities of the top tokens, whereas Top” selects from the Top k” tokens sorted by probability.

Top k and Top p” both select from the same set of tokens but use different methods to prioritize them based on frequency.

Top k and “Top p” are identical in their approach to token selection but differ in their application of penalties to tokens.

Q25. Which is NOT a built-in memory type in LangChain?

Conversation Token Buffer Memory

Conversation ImgeMemory

Conversation Buffer Memory

Conversation Summary Memory

Q29. Given the following code: chain = prompt |11m

Which statement is true about LangChain Expression language (ICED?

LCEL is a programming language used to write documentation for LangChain.

LCEL is a legacy method for creating chains in LangChain