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What does the Ranker do in a text generation system?
A. It generates the final text based on the user's query.
B. It interacts with the user to understand the query better.
C. It sources information from databases to use in text generation.
D. It evaluates and prioritizes the information retrieved by the Retriever.
答案：D
解題說明：
Comprehensive and Detailed In-Depth Explanation=
In systems like RAG, the Ranker evaluates and sorts the information retrieved by the Retriever (e.g., documents or snippets) based on relevance to the query, ensuring the most pertinent data is passed to the Generator. This makes Option C correct. Option A is the Generator's role. Option B describes the Retriever. Option D is unrelated, as the Ranker doesn't interact with users but processes retrieved data. The Ranker enhances output quality by prioritizing relevant content.
OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline components.
問題 #19
What is LCEL in the context of LangChain Chains?
A. A programming language used to write documentation for LangChain
B. An older Python library for building Large Language Models
C. A legacy method for creating chains in LangChain
D. A declarative way to compose chains together using LangChain Expression Language
答案：D
解題說明：
Comprehensive and Detailed In-Depth Explanation=
LCEL (LangChain Expression Language) is a declarative syntax in LangChain for composing chains-sequences of operations involving LLMs, tools, and memory. It simplifies chain creation with a readable, modular approach, making Option C correct. Option A is false, as LCEL isn't fordocumentation. Option B is incorrect, as LCEL is current, not legacy. Option D is wrong, as LCEL is part of LangChain, not a standalone LLM library. LCEL enhances flexibility in application design.
OCI 2025 Generative AI documentation likely mentions LCEL under LangChain integration or chain composition.
問題 #20
What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?
A. Limiting the model to only k possible outcomes or answers for a given task
B. Providing the exact k words in the prompt to guide the model's response
C. Explicitly providing k examples of the intended task in the prompt to guide the model's output
D. The process of training the model on k different tasks simultaneously to improve its versatility
答案：C
解題說明：
Comprehensive and Detailed In-Depth Explanation=
"k-shot prompting" (e.g., few-shot) involves providing k examples of a task in the prompt to guide the LLM's output via in-context learning, without additional training. This makes Option B correct. Option A (k words) misinterprets-examples, not word count, matter. Option C (training) confuses prompting with fine-tuning. Option D (k outcomes) is unrelated-k refers to examples, not limits. k-shot leverages pre-trained knowledge efficiently.
OCI 2025 Generative AI documentation likely covers k-shot prompting under prompt engineering techniques.
問題 #21
Given the following code:
PromptTemplate(input_variables=["human_input", "city"], template=template) Which statement is true about PromptTemplate in relation to input_variables?
A. PromptTemplate requires a minimum of two variables to function properly.
B. PromptTemplate supports any number of variables, including the possibility of having none.
C. PromptTemplate is unable to use any variables.
D. PromptTemplate can support only a single variable at a time.
答案：B
解題說明：
Comprehensive and Detailed In-Depth Explanation=
In LangChain, PromptTemplate supports any number of input_variables (zero, one, or more), allowing flexible prompt design-Option C is correct. The example shows two, but it's not a requirement. Option A (minimum two) is false-no such limit exists. Option B (single variable) is too restrictive. Option D (no variables) contradicts its purpose-variables are optional but supported. This adaptability aids prompt engineering.
OCI 2025 Generative AI documentation likely covers PromptTemplate under LangChain prompt design.
問題 #22
What do prompt templates use for templating in language model applications?
A. Python's lambda functions
B. Python's str.format syntax
C. Python's list comprehension syntax
D. Python's class and object structures
答案：B
解題說明：
Comprehensive and Detailed In-Depth Explanation=
Prompt templates in LLM applications (e.g., LangChain) typically use Python's str.format() syntax to insert variables into predefined string patterns (e.g., "Hello, {name}!"). This makes Option B correct. Option A (list comprehension) is for list operations, not templating. Option C (lambda functions) defines functions, not templates. Option D (classes/objects) is overkill-templates are simpler constructs. str.format() ensures flexibility and readability.
OCI 2025 Generative AI documentation likely mentions str.format() under prompt template design.