GET SUCCESS IN ORACLE 1Z0-1127-25 EXAM WITH FLYING COLORS​

Get Success in Oracle 1Z0-1127-25 Exam with Flying Colors​

Get Success in Oracle 1Z0-1127-25 Exam with Flying Colors​

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q16-Q21):

NEW QUESTION # 16
What does the RAG Sequence model do in the context of generating a response?

  • A. For each input query, it retrieves a set of relevant documents and considers them together to generate a cohesive response.
  • B. It retrieves relevant documents only for the initial part of the query and ignores the rest.
  • C. It retrieves a single relevant document for the entire input query and generates a response based on that alone.
  • D. It modifies the input query before retrieving relevant documents to ensure a diverse response.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
The RAG (Retrieval-Augmented Generation) Sequence model retrieves a set of relevant documents for a query from an external knowledge base (e.g., via a vector database) and uses them collectively with the LLM to generate a cohesive, informed response. This leverages multiple sources for better context, making Option B correct. Option A describes a simpler approach (e.g., RAG Token), not Sequence. Option C is incorrect-RAG considers the full query. Option D is false-query modification isn't standard in RAG Sequence. This method enhances response quality with diverse inputs.
OCI 2025 Generative AI documentation likely details RAG Sequence under retrieval-augmented techniques.


NEW QUESTION # 17
How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?

  • A. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity.
  • B. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity.
  • C. Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance.
  • D. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In RAG, "Groundedness" assesses whether the response is factually correct and supported by retrieved data, while "Answer Relevance" evaluates how well the response addresses the user's query. Option A captures this distinction accurately. Option B is off-groundedness isn't just contextual alignment, and relevance isn't about syntax. Option C swaps the definitions. Option D misaligns-groundedness isn't solely data integrity, and relevance isn't lexical diversity. This distinction ensures RAG outputs are both true and pertinent.
OCI 2025 Generative AI documentation likely defines these under RAG evaluation metrics.


NEW QUESTION # 18
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of-Thought, Least-to-Most, or Step-Back prompting technique:

  • A. "To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, we'll explore how they trap heat in the Earth's atmosphere."A. 1: Step-Back, 2: Chain-of-Thought, 3: Least-to-MostB. 1: Least-to-Most, 2: Chain-of-Thought, 3: Step-BackC. 1: Chain-of-Thought, 2: Step-Back, 3: Least-to-MostD. 1: Chain-of-Thought, 2: Least-to-Most, 3: Step-Back
  • B. "Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50."
  • C. "Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question."

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt 1: Shows intermediate steps (3 × 4 = 12, then 12 ÷ 4 = 3 sets, $200 ÷ $50 = 4)-Chain-of-Thought.
Prompt 2: Steps back to a simpler problem before the full one-Step-Back.
Prompt 3: OCI 2025 Generative AI documentation likely defines these under prompting strategies.


NEW QUESTION # 19
Which statement is true about string prompt templates and their capability regarding variables?

  • A. They can only support a single variable at a time.
  • B. They are unable to use any variables.
  • C. They require a minimum of two variables to function properly.
  • D. They support any number of variables, including the possibility of having none.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
String prompt templates (e.g., in LangChain) are flexible frameworks that can include zero, one, or multiple variables (placeholders) to customize prompts dynamically. They can be static (no variables) or complex (many variables), making Option C correct. Option A is too restrictive. Option B is false-variables are a core feature. Option D is incorrect, as no minimum is required. This flexibility aids prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt templates under LangChain or prompt design.


NEW QUESTION # 20
What does a cosine distance of 0 indicate about the relationship between two embeddings?

  • A. They are completely dissimilar
  • B. They are similar in direction
  • C. They are unrelated
  • D. They have the same magnitude

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Cosine distance measures the angle between two vectors, where 0 means the vectors point in the same direction (cosine similarity = 1), indicating high similarity in embeddings' semantic content-Option C is correct. Option A (dissimilar) aligns with a distance of 1. Option B is vague-directional similarity matters. Option D (magnitude) isn't relevant-cosine ignores magnitude. This is key for semantic comparison.
OCI 2025 Generative AI documentation likely explains cosine distance under vector database metrics.


NEW QUESTION # 21
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