Oracle 1Z0-1127-25學習指南：Oracle Cloud Infrastructure 2025 Generative AI Professional和資格考試的領導者選擇參加Oracle 1Z0-1127-25 認證考試是一個明智的選擇，因為有了Oracle 1Z0-1127-25認證證書後，你的工資和職位都會有所提升，生活水準就會相應的提供。但是通過Oracle 1Z0-1127-25 認證考試不是很容易的，需要花很多時間和精力掌握好相關專業知識。KaoGuTi是一個制訂Oracle 1Z0-1127-25 認證考試培訓方案的專業IT培訓網站。你可以先在我們的網站上免費下載部分部分關於Oracle 1Z0-1127-25 認證考試的練習題和答案作為免費嘗試，以便你可以檢驗我們的可靠性。一般，試用KaoGuTi的產品後，你會對我們的產品很有信心的。 最新的 Oracle Cloud Infrastructure 1Z0-1127-25 免費考試真題 (Q62-Q67):問題 #62
What is the purpose of memory in the LangChain framework?
A. To act as a static database for storing permanent records
B. To retrieve user input and provide real-time output only
C. To store various types of data and provide algorithms for summarizing past interactions
D. To perform complex calculations unrelated to user interaction
答案：C
解題說明：
Comprehensive and Detailed In-Depth Explanation=
In LangChain, memory stores contextual data (e.g., chat history) and provides mechanisms to summarize or recall past interactions, enabling coherent, context-aware conversations. This makes Option B correct. Option A is too limited, as memory does more than just input/output handling. Option C is unrelated, as memory focuses on interaction context, not abstract calculations. Option D is inaccurate, as memory is dynamic, not a static database. Memory is crucial for stateful applications.
OCI 2025 Generative AI documentation likely discusses memory under LangChain's context management features.
問題 #63
What is the function of "Prompts" in the chatbot system?
A. They store the chatbot's linguistic knowledge.
B. They are used to initiate and guide the chatbot's responses.
C. They handle the chatbot's memory and recall abilities.
D. They are responsible for the underlying mechanics of the chatbot.
答案：B
解題說明：
Comprehensive and Detailed In-Depth Explanation=
Prompts in a chatbot system are inputs provided to the LLM to initiate and steer its responses, often including instructions, context, or examples. They shape the chatbot's behavior without altering its core mechanics, making Option B correct. Option A is false, as knowledge is stored in the model's parameters. Option C relates to the model's architecture, not prompts. Option D pertains to memory systems, not prompts directly. Prompts are key for effective interaction.
OCI 2025 Generative AI documentation likely covers prompts under chatbot design or inference sections.
問題 #64
Which is NOT a built-in memory type in LangChain?
A. ConversationImageMemory
B. ConversationBufferMemory
C. ConversationTokenBufferMemory
D. ConversationSummaryMemory
答案：A
解題說明：
Comprehensive and Detailed In-Depth Explanation=
LangChain includes built-in memory types like ConversationBufferMemory (stores full history), ConversationSummaryMemory (summarizes history), and ConversationTokenBufferMemory (limits by token count)-Options B, C, and D are valid. ConversationImageMemory (A) isn't a standard type-image handling typically requires custom or multimodal extensions, not a built-in memory class-making A correct as NOT included.
OCI 2025 Generative AI documentation likely lists memory types under LangChain memory management.
問題 #65
How are prompt templates typically designed for language models?
A. To work only with numerical data instead of textual content
B. As complex algorithms that require manual compilation
C. As predefined recipes that guide the generation of language model prompts
D. To be used without any modification or customization
答案：C
解題說明：
Comprehensive and Detailed In-Depth Explanation=
Prompt templates are predefined, reusable structures (e.g., with placeholders for variables) that guide LLM prompt creation, streamlining consistent input formatting. This makes Option B correct. Option A is false, as templates aren't complex algorithms but simple frameworks. Option C is incorrect, as templates are customizable. Option D is wrong, as they handle text, not just numbers.Templates enhance efficiency in prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt templates under prompt engineering or LangChain tools.
Here is the next batch of 10 questions (21-30) from your list, formatted as requested with detailed explanations. The answers are based on widely accepted principles in generative AI and Large Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical errors have been corrected for clarity.
問題 #66
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours are required for fine-tuning if the cluster is active for 10 days?
A. 480 unit hours
B. 20 unit hours
C. 744 unit hours
D. 240 unit hours
答案：D
解題說明：
Comprehensive and Detailed In-Depth Explanation=
In OCI, a dedicated AI cluster's usage is typically measured in unit hours, where 1 unit hour = 1 hour of cluster activity. For 10 days, assuming 24 hours per day, the calculation is: 10 days × 24 hours/day = 240 hours. Thus, Option B (240 unit hours) is correct. Option A (480) might assume multiple clusters or higher rates, but the question specifies one cluster. Option C (744) approximates a month (31 days), not 10 days. Option D (20) is arbitrarily low.
OCI 2025 Generative AI documentation likely specifies unit hour calculations under Dedicated AI Cluster pricing.