1Z0-1127-25 LangChain and AI Application Development — All Questions With Answers

Question 1mediummultiple choice

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A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Question 2mediummultiple choice

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An application uses LangChain's ConversationalRetrievalChain with memory. Users report that the chatbot occasionally repeats information from earlier in the conversation even when the new question is unrelated. What is the most likely cause?

Question 3easymulti select

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Which THREE of the following are core LangChain components?

Question 4mediummultiple choice

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A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Question 5hardmultiple choice

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An organization needs to implement a RAG application with Oracle AI Vector Search but has strict latency requirements. They have millions of vectors. Which index type is likely to provide the best search speed while maintaining reasonable recall?

Question 6easymulti select

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A developer wants to persist chat history for a LangChain application so that conversations survive application restarts. Which TWO approaches are appropriate?

Question 7mediummultiple choice

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A developer is building a RAG pipeline using LangChain and OCI Generative AI. They need to split a large PDF into overlapping chunks for embedding. Which text splitter and parameter settings are MOST appropriate?

Question 8mediummultiple choice

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A developer wants to compose a LangChain pipeline using the LCEL (LangChain Expression Language) to combine a prompt template, a model, and an output parser. Which operator is used for this composition?

Question 9hardmultiple choice

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A company is deploying a LangChain agent that uses a custom tool to query an external API. The agent must handle rate limits gracefully. Which approach should the developer implement?

Question 10hardmultiple choice

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An enterprise is building a LangChain application that must use Oracle AI Vector Search for retrieval. They need to store embeddings in an Oracle Database 23ai table with a VECTOR column. Which index type should they create to support efficient similarity search with exact nearest neighbor queries?

Question 11mediummultiple choice

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A developer is using LangChain to build a RAG pipeline with Oracle Database 23ai as the vector store. Which LangChain wrapper should they use to create embeddings and store them in the database?

Question 12mediummultiple choice

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A company is using Oracle AI Vector Search in Oracle Database 23ai for semantic search over product descriptions. They need to create an index that supports approximate nearest neighbor search with high recall and moderate indexing time. Which index type and parameters should they choose?

Question 13hardmultiple choice

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A LangChain application using ChatOCIGenAI is hitting rate limits from the OCI Generative AI service. The developer wants to implement retry logic with exponential backoff. Which approach is most appropriate in LangChain?

Question 14mediummultiple choice

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A developer is using LangChain's SequentialChain to process text: first, summarize a long document, then translate the summary to French. How should they configure the chain to pass the output of the first step as input to the second?

Question 15mediummulti select

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A developer is using LangChain to build a RAG pipeline that processes PDF documents. They need to split the text into chunks and store embeddings in Oracle Database 23ai. Which two components are essential? (Choose TWO.)

Question 16mediummultiple choice

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A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Question 17hardmultiple choice

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An organization using Oracle AI Vector Search for a RAG application wants to minimize latency for vector similarity searches on a dataset of 10 million vectors. Which index type and parameter combination is MOST likely to achieve the lowest latency while maintaining high recall?

Question 18mediummultiple choice

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A developer uses the ReAct agent in LangChain with a calculator tool and a search tool. The agent receives the question: 'What is the population of Paris multiplied by 3?' The agent first calls the search tool to find the population, then calls the calculator tool to multiply it by 3. Which component is responsible for deciding the sequence of tool calls?

Question 19hardmultiple choice

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An AI application uses LangChain's LCEL with the | operator to compose a chain: prompt | model | output_parser. During testing, the developer notices that the output_parser is not receiving the expected input format from the model. What is the most likely cause?

Question 20mediummultiple choice

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A team is using LangChain's LCEL to build a RAG pipeline. They want to add a step that logs the retrieved documents before passing them to the LLM. How can they achieve this while keeping the chain composable?

Question 21easymulti select

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A developer wants to load a CSV file containing customer feedback and split it into chunks for a RAG pipeline. Which TWO LangChain components should they use?

Question 22mediummultiple choice

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A developer is building a LangChain RAG pipeline that uses OCI Generative AI embeddings to index a large corpus of technical documents. During testing, they notice that semantically similar passages are not being retrieved because the embeddings for those passages appear unrelated. The embedding model is confirmed to be working correctly and the same text always produces the same vector. Which is the MOST likely cause of the retrieval problem?

Question 23mediummulti select

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A developer is building a LangChain application that uses OCI Generative AI for text generation. They need to ensure that the application can handle failures gracefully when the OCI Generative AI service is temporarily unavailable. Which TWO strategies should they implement? (Choose two.)

Question 24mediummultiple choice

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A developer is using LangChain to create a chain that summarizes customer support tickets stored in an Oracle Database. The chain uses a prompt template that includes the ticket text and a placeholder for the summary. The developer wants to ensure that the chain can handle tickets of varying lengths without exceeding the token limit of the OCI Generative AI model. Which LangChain feature should they use to dynamically manage the prompt length?

Question 25easymultiple choice

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In LangChain, which component is responsible for connecting a language model to a retriever and a prompt template to answer questions based on retrieved documents?

Question 26mediummultiple choice

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A developer is building a RAG pipeline using LangChain and Oracle AI Vector Search. After loading and splitting PDF documents, they generate embeddings and store them in Oracle Database using OracleVS. Which method should they call on the vector store object to create a retriever that uses similarity search with a configurable number of results?

Question 27hardmultiple choice

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A company uses LangChain with OCI Generative AI. They notice that their agent-based application occasionally exceeds the rate limits of the OCI Generative AI service, causing errors. Which strategy is MOST effective for handling rate limits in a production LangChain application?

Question 28easymultiple choice

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Which LangChain memory type stores the entire conversation history as a list of messages and is best for simple, short conversations?

Question 29mediummultiple choice

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A developer wants to index a large corpus of HTML web pages for a RAG pipeline using LangChain. They need to load the content from URLs, split the text into chunks, and generate embeddings. Which combination of LangChain components should they use?

Question 30mediummultiple choice

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In LangChain, what is the purpose of the LCEL (LangChain Expression Language) | operator?

Question 31hardmultiple choice

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A team is building a conversational chatbot using LangChain and OCI Generative AI. They want to maintain a summary of the conversation rather than storing the entire history, to keep within token limits. Which memory class should they use, and what additional step is required when initializing the memory?

Question 32easymultiple choice

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Which Oracle AI Vector Search index type is designed for approximate nearest neighbor search and uses a navigable small world graph?

Question 33mediummultiple choice

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A developer wants to use LangChain to create an agent that can perform calculations and look up information from a database. Which tools should be provided to the agent?

Question 34hardmultiple choice

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A company has a collection of PDF documents that are 500 pages each. They want to build a RAG system using LangChain and FAISS. They need to ensure that each chunk has enough context for accurate retrieval while keeping chunk size small enough for efficient embedding. They also want some overlap between chunks to avoid losing context at boundaries. Which text splitter configuration is most appropriate?

Question 35mediummulti select

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A developer is building a LangChain application that uses OCI Generative AI service. They want to implement streaming responses from the LLM to improve user experience. Which TWO actions are necessary to enable streaming?

Question 36hardmulti select

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A data scientist is designing a RAG pipeline using LangChain and Oracle AI Vector Search. They want to ensure that the retrieved documents are diverse and not overly similar to each other. Which TWO approaches can achieve this?

Question 37easymultiple choice

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Which LangChain abstraction is responsible for storing and retrieving conversation history to maintain context across multiple turns in a chatbot?

Question 38mediummultiple choice

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An AI developer is building a document Q&A application using LangChain and OCI Generative AI. They need to split large PDF documents into smaller chunks before embedding. Which text splitter should they use to ensure splits respect sentence boundaries while also controlling chunk size?

Question 39easymultiple choice

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Which of the following best describes the role of a Retriever in a LangChain RAG pipeline?

Question 40hardmultiple choice

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In Oracle AI Vector Search, which index type is designed for approximate nearest neighbor search and employs a hierarchical navigable small world graph, offering high recall and fast search speeds for high-dimensional data?

Question 41mediummultiple choice

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A developer is using LangChain's LCEL to build a RAG pipeline. They want to add streaming of the final answer to the user. Which LCEL feature enables streaming output from the model?

Question 42mediummultiple choice

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A team wants to deploy a LangChain agent that can perform mathematical calculations, look up current weather, and search the web. Which tools should they include in the agent's toolkit?

Question 43mediummultiple choice

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A developer notices that the ConversationalRetrievalChain in their LangChain application is not retaining context from previous turns in the conversation. Which component is most likely missing or misconfigured?

Question 44easymultiple choice

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Which LangChain document loader would be most appropriate to load content from a public website for inclusion in a knowledge base?

Question 45hardmultiple choice

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In a LangChain RAG pipeline using Oracle AI Vector Search, the developer wants to retrieve chunks that are both relevant and diverse to cover multiple aspects of a query. Which retrieval method should they configure on the retriever?

Question 46mediummultiple choice

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A developer is using ChatPromptTemplate with MessagesPlaceholder to handle conversation history. What is the purpose of MessagesPlaceholder in the prompt template?

Question 47mediummulti select

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A developer is deploying a LangChain agent that uses an LLM from OCI Generative AI. The agent interacts with external APIs and must handle rate limits gracefully. Which TWO practices should the developer implement?

Question 48mediummulti select

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In a LangChain RAG pipeline using OCI Generative AI, which THREE components are essential for ingesting documents into a vector store?

Question 49mediummultiple choice

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A team is using LangChain's ConversationalRetrievalChain with ConversationBufferMemory to build a chatbot. After a few turns, the chatbot starts repeating information from earlier messages. What is the MOST likely cause?

Question 50hardmultiple choice

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An organization uses Oracle AI Vector Search in Oracle Database 23ai to store embeddings for a LangChain RAG application. They need to perform similarity search with high recall and low latency for a large dataset (10M vectors). Which index configuration should they choose?

Question 51easymultiple choice

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Which LangChain abstraction is used to wrap OCI Generative AI's chat models (e.g., Cohere Command R) for use in a LangChain chain?

Question 52easymultiple choice

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In a LangChain RetrievalQA chain, which method on a vector store is used to create the retriever object?

Question 53mediummultiple choice

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A developer is using LangChain's ConversationBufferMemory to store chat history. They notice that after many turns, the prompt becomes too large and exceeds the model's context window. What is the BEST memory type to use for this scenario?

Question 54easymultiple choice

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Which LangChain document loader would you use to load data from a webpage given its URL?

Question 55mediummultiple choice

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A LangChain application uses an agent with a calculator tool and a search tool. The agent is supposed to answer a question that requires both arithmetic and web lookup, but it only uses the search tool and gives an approximate answer. Which agent type is MOST likely to correctly combine the tools?

Question 56mediummultiple choice

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When using LangChain's RetrievalQA chain with `chain_type="stuff"`, what happens if the retrieved documents exceed the model's context window?

Question 57mediummulti select

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A developer is building a LangChain RAG pipeline with OCI Generative AI. Which TWO components are needed to create embeddings from documents and store them for retrieval?

Question 58hardmulti select

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A LangChain ReAct agent is failing to correctly use a custom tool that requires a specific parameter format. The agent keeps calling the tool with incorrect parameters. Which THREE steps should the developer take to debug and fix the issue?

Question 59easymulti select

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A company wants to use LangChain to build a chatbot that remembers previous conversations across sessions. Which TWO components should they use together?

Question 60mediummultiple choice

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A team is implementing a conversational chatbot that needs to remember a user's previous messages within the same session. They are using LangChain with OCI Generative AI. Which memory type and persistence approach should they choose for session-only memory?

Question 61easymultiple choice

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In LangChain's Expression Language (LCEL), what does the pipe (|) operator do when connecting components?

Question 62mediummultiple choice

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A developer is using LangChain's RecursiveCharacterTextSplitter with chunk_size=1000 and chunk_overlap=200. Which statement best describes the resulting chunks?

Question 63mediummultiple choice

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A team is building an agent using LangChain that needs to perform calculations and search the web for current information. Which combination of tools and agent type should they use?

Question 64hardmultiple choice

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A developer observes that their LangChain RAG pipeline sometimes returns duplicate or near-duplicate documents in the retrieved set, reducing the diversity of information provided to the LLM. Which retrieval parameter should they adjust to improve diversity?

Question 65easymultiple choice

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In LangChain, which component is responsible for loading data from a specific file format, such as PDF or CSV, into a document object?

Question 66hardmultiple choice

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An application uses ConversationalRetrievalChain with a vector store retriever. Users report that the chatbot sometimes provides answers that are not grounded in the retrieved documents. Which step in the RAG pipeline is most likely the cause?

Question 67easymultiple choice

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Which LangChain memory type is best suited for a long-running conversation where token consumption must be minimized, and the gist of previous exchanges should be retained?

Question 68mediummulti select

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A team is designing a production-grade LangChain agent that uses multiple tools, including a custom SQL query tool and a web search tool. They need to ensure the agent handles errors gracefully and logs all actions. Which three practices should they implement? (Choose THREE.)

Question 69hardmulti select

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An organization is deploying a RAG application with Oracle AI Vector Search. They need to ensure that the vector index supports low-latency queries and can handle updates to the underlying documents (inserts, deletes, modifications) without significant performance degradation. Which two index features should they consider? (Choose TWO.)

Question 70easymultiple choice

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Which LangChain component is responsible for storing and retrieving message history across multiple turns in a conversation?

Question 71hardmultiple choice

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A developer uses RecursiveCharacterTextSplitter with chunk_size=500 and chunk_overlap=100. After splitting, a particular chunk ends with an incomplete sentence. What is the likely cause?

Question 72mediummultiple choice

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A developer wants to create a LangChain chain that takes a user's question, retrieves relevant documents from a vector store, and then generates an answer using an LLM. Which chain is MOST appropriate for this task?

Question 73easymultiple choice

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In LangChain, which class should be used to wrap Oracle Cloud Infrastructure's Generative AI service as a chat model?

Question 74mediummultiple choice

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A developer needs to include the conversation history in a prompt for a LangChain chatbot. They want to insert previous exchanges between the user and the AI into the prompt at a specific position. Which component should they use?

Question 75easymultiple choice

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Which LangChain document loader should be used to load text from a web page given its URL?

Question 76mediummultiple choice

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A developer wants to ensure that a LangChain RetrievalQA chain returns diverse results, avoiding near-duplicate passages in the retrieved context. Which retrieval parameter should be configured on the retriever?

Question 77hardmultiple choice

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An application uses Oracle AI Vector Search with a VECTOR data type column. The team wants to perform a similarity search to find the top 5 most similar vectors to a query vector. Which SQL clause is necessary to achieve this?

Question 78mediummulti select

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A developer is building a conversational AI application using LangChain and needs to persist chat history across sessions. Which TWO approaches can they use? (Choose TWO.)

Question 79mediummulti select

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A company is deploying a LangChain application on OCI and needs to implement error handling and rate limit management. Which THREE strategies should they consider? (Choose THREE.)

Question 80hardmulti select

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You need to build a RAG pipeline using LangChain and OCI Generative AI. The pipeline must load PDF documents, split them into chunks, embed them, store in a vector store, and retrieve relevant chunks at query time. Which THREE components are essential? (Choose THREE.)

Question 81mediummultiple choice

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A developer is building a LangChain-powered application that must maintain conversation history across multiple turns. They want to store the chat history in Oracle Database. Which memory type and persistence approach should they use?

Question 82easymultiple choice

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Which LangChain component is responsible for splitting long documents into smaller, overlapping chunks before embedding?

Question 83hardmultiple choice

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A developer is using Oracle AI Vector Search with LangChain to build a RAG system on top of Oracle Database 23ai. They have created a VECTOR column and built an HNSW index. To improve recall at the cost of some accuracy, which index parameter should they adjust?

Question 84easymultiple choice

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A developer wants to use LangChain to connect to OCI Generative AI Service for text generation. Which LangChain wrapper class should they use for the chat model?

Question 85hardmultiple choice

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A company is deploying a LangChain agent for internal data analysis. The agent uses a ReAct pattern with tools including a Python REPL and a SQL query tool. What is the PRIMARY security concern when exposing this agent to non-technical users?

Question 86mediummultiple choice

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A developer needs to build a chain that first summarizes a long document, then translates the summary into French. Which LangChain chain type allows executing these steps in sequence with the output of one step feeding into the next?

Question 87mediummulti select

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A developer is building a RAG application with LangChain and Oracle AI Vector Search. They need to choose a text splitter that respects semantic boundaries (e.g., paragraphs, sentences) and a vector store that supports transactional updates in Oracle Database 23ai. Which TWO options should they select?

Question 88mediummulti select

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A team is building a LangChain agent that needs to answer questions using both a company-internal knowledge base (stored in Oracle AI Vector Search) and live web search. Which THREE components should they include in the agent setup?

Question 89mediummulti select

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A developer is using LangChain's RetrievalQA chain with a vector store. They want to improve the diversity of retrieved documents to avoid redundant information. Which TWO parameters or methods should they adjust?

Question 90easymulti select

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A developer is building a RAG pipeline with LangChain. They have loaded PDF documents with PDFLoader. Which TWO steps must they perform before indexing the documents into a vector store?

Question 91hardmulti select

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A company is deploying a LangChain application using OCI Generative AI. They need to comply with a policy that requires all prompts sent to the LLM to be logged for audit, and they must also handle rate limits gracefully. Which TWO strategies should they implement?

Question 92mediummulti select

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A developer is using LangChain's ChatPromptTemplate to construct a prompt for a conversational agent. The prompt should include a system message, a placeholder for conversation history, and the latest user query. Which TWO components should they include in the template?

Question 93hardmulti select

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A developer is using LangChain with Oracle AI Vector Search (OracleVS) to store embeddings. They notice that similarity search queries are slow. Which THREE actions could improve query performance?

Question 94mediummultiple choice

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A developer is building a LangChain pipeline against OCI Generative AI. They need a component that accepts a natural-language question plus a set of retrieved Documents and returns a single string answer, so it can be dropped directly into an LCEL pipeline with a RunnablePassthrough for the question and a retriever for the documents. Which LangChain runnable should they instantiate for this role?

Question 95hardmulti select

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A team is building an LCEL RAG pipeline over OCI Generative AI. They want to stream partial tokens to the browser as the model generates the final answer, and they also want to keep the retrieved source Documents available for citation display. Which TWO design choices support these goals? (Choose two.)

Question 96easymultiple choice

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A developer is assembling an LCEL chain that must send a fixed system instruction, the user's question, and the retrieved context to an OCI Generative AI chat model in a single call. Which LangChain component is responsible for combining these pieces into the final prompt sent to the model?

Question 97mediummultiple choice

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A developer is building a LangChain agent that must decide, at each step, whether to call an OCI Generative AI chat model for reasoning or to invoke a custom tool that queries an Oracle Autonomous Database. The agent must handle tool errors gracefully and continue reasoning. Which LangChain component is responsible for orchestrating this iterative reason-act-observe loop?

Question 98mediummultiple choice

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A developer is building a LangChain agent that must maintain memory across multiple turns in a conversation. The agent uses a custom tool to query an Oracle Database 23ai table. After several interactions, the agent starts repeating questions it already asked. Which component in the LangChain agent architecture is most likely responsible for retaining conversational context?

Question 99easymultiple choice

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A developer is using LangChain to build a retrieval-augmented generation (RAG) application with OCI Generative AI. They need to split a large PDF document into smaller chunks before embedding. Which LangChain component is specifically designed to split text into chunks based on character count?

Question 100hardmultiple choice

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A team's LangChain RAG application uses a vector store retriever configured with search_type="mmr". Users complain that answers omit important details that appear in the top-scoring document. Which change best addresses this while preserving result diversity?

Question 101hardmultiple choice

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A team is deploying a LangChain agent that uses OCI Generative AI as its reasoning model and a set of tools including a database query tool and a web search tool. In production, the agent occasionally calls the database tool with malformed SQL that returns errors, then loops by retrying the same malformed call. Which change would BEST reduce these loops while preserving the agent's ability to recover from transient failures?

Question 102hardmultiple choice

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A developer is using LangChain with OCI Generative AI to create a conversational agent. The agent uses a custom tool that executes SQL queries against an Oracle Autonomous Database. During testing, the agent sometimes generates SQL that is syntactically correct but semantically wrong, leading to incorrect results. Which LangChain feature should the developer implement to improve the agent's ability to generate accurate SQL queries?

Question 103mediummulti select

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A developer is instrumenting a LangChain application that calls OCI Generative AI models so that prompts, completions, token usage, and latency are captured for troubleshooting and cost analysis. Which TWO LangChain facilities should they use? (Choose two.)

Question 104easymultiple choice

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A developer is assembling a LangChain pipeline that must load HTML pages from an internal knowledge base, split them into chunks, embed the chunks with OCI Generative AI, and store them in a vector database. They want the loading step to produce Document objects with page content and metadata. Which LangChain component category is responsible for this loading step?

Question 105mediummultiple choice

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A developer is building a LangChain agent using OCI Generative AI (Cohere Command R) as the reasoning LLM. The agent must decide between three tools: a SQL query tool that returns structured rows, a web search tool, and a calculator. After several test runs, the agent consistently calls the SQL tool even for simple arithmetic questions. The developer confirms the tool descriptions are generic. What is the MOST likely cause and the correct fix?

Question 106hardmultiple choice

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A team uses LangChain's RecursiveCharacterTextSplitter with chunk_size=800 and chunk_overlap=200 to index a large policy manual into Oracle Database 23ai via OracleVS. Retrieval quality is poor for questions whose answers span section boundaries. The team wants to keep chunk sizes similar but improve boundary coverage. Which change BEST addresses the problem?

Question 107easymultiple choice

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A developer is assembling a LangChain pipeline that must retrieve documents from an OracleVS vector store and then answer questions with an OCI Generative AI chat model. They want the fewest moving parts and no custom prompt engineering for the final answer step. Which LangChain component should they use to combine retrieval and generation in one object?

Question 108mediummulti select

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A developer is instrumenting a LangChain application that calls OCI Generative AI models. They need to trace each chain step, inspect the exact prompt sent to the model, and measure per-step latency in a hosted dashboard for debugging in staging. (Choose two.)

Question 109hardmultiple choice

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A developer builds a LangChain chain that uses an OCI Generative AI chat model to answer questions over a product catalog stored in Oracle Database 23ai via OracleVS. Users report that answers occasionally cite product details that do not exist in the catalog. The retriever returns relevant documents, and the prompt instructs the model to answer only from context. Which action BEST reduces these unsupported answers?

Question 1mediummultiple choice

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A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Question 2mediummultiple choice

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An application uses LangChain's ConversationalRetrievalChain with memory. Users report that the chatbot occasionally repeats information from earlier in the conversation even when the new question is unrelated. What is the most likely cause?

Question 3easymulti select

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Which THREE of the following are core LangChain components?

Question 4mediummultiple choice

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A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Question 5hardmultiple choice

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An organization needs to implement a RAG application with Oracle AI Vector Search but has strict latency requirements. They have millions of vectors. Which index type is likely to provide the best search speed while maintaining reasonable recall?

Question 6easymulti select

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A developer wants to persist chat history for a LangChain application so that conversations survive application restarts. Which TWO approaches are appropriate?

Question 7mediummultiple choice

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A developer is building a RAG pipeline using LangChain and OCI Generative AI. They need to split a large PDF into overlapping chunks for embedding. Which text splitter and parameter settings are MOST appropriate?

Question 8mediummultiple choice

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A developer wants to compose a LangChain pipeline using the LCEL (LangChain Expression Language) to combine a prompt template, a model, and an output parser. Which operator is used for this composition?

Question 9hardmultiple choice

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A company is deploying a LangChain agent that uses a custom tool to query an external API. The agent must handle rate limits gracefully. Which approach should the developer implement?

Question 10hardmultiple choice

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An enterprise is building a LangChain application that must use Oracle AI Vector Search for retrieval. They need to store embeddings in an Oracle Database 23ai table with a VECTOR column. Which index type should they create to support efficient similarity search with exact nearest neighbor queries?

Question 11mediummultiple choice

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A developer is using LangChain to build a RAG pipeline with Oracle Database 23ai as the vector store. Which LangChain wrapper should they use to create embeddings and store them in the database?

Question 12mediummultiple choice

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A company is using Oracle AI Vector Search in Oracle Database 23ai for semantic search over product descriptions. They need to create an index that supports approximate nearest neighbor search with high recall and moderate indexing time. Which index type and parameters should they choose?

Question 13hardmultiple choice

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A LangChain application using ChatOCIGenAI is hitting rate limits from the OCI Generative AI service. The developer wants to implement retry logic with exponential backoff. Which approach is most appropriate in LangChain?

Question 14mediummultiple choice

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A developer is using LangChain's SequentialChain to process text: first, summarize a long document, then translate the summary to French. How should they configure the chain to pass the output of the first step as input to the second?

Question 15mediummulti select

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A developer is using LangChain to build a RAG pipeline that processes PDF documents. They need to split the text into chunks and store embeddings in Oracle Database 23ai. Which two components are essential? (Choose TWO.)

Question 16mediummultiple choice

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A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Question 17hardmultiple choice

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An organization using Oracle AI Vector Search for a RAG application wants to minimize latency for vector similarity searches on a dataset of 10 million vectors. Which index type and parameter combination is MOST likely to achieve the lowest latency while maintaining high recall?

Question 18mediummultiple choice

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A developer uses the ReAct agent in LangChain with a calculator tool and a search tool. The agent receives the question: 'What is the population of Paris multiplied by 3?' The agent first calls the search tool to find the population, then calls the calculator tool to multiply it by 3. Which component is responsible for deciding the sequence of tool calls?

Question 19hardmultiple choice

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An AI application uses LangChain's LCEL with the | operator to compose a chain: prompt | model | output_parser. During testing, the developer notices that the output_parser is not receiving the expected input format from the model. What is the most likely cause?

Question 20mediummultiple choice

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A team is using LangChain's LCEL to build a RAG pipeline. They want to add a step that logs the retrieved documents before passing them to the LLM. How can they achieve this while keeping the chain composable?

Question 21easymulti select

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A developer wants to load a CSV file containing customer feedback and split it into chunks for a RAG pipeline. Which TWO LangChain components should they use?

Question 22mediummultiple choice

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A developer is building a LangChain RAG pipeline that uses OCI Generative AI embeddings to index a large corpus of technical documents. During testing, they notice that semantically similar passages are not being retrieved because the embeddings for those passages appear unrelated. The embedding model is confirmed to be working correctly and the same text always produces the same vector. Which is the MOST likely cause of the retrieval problem?

Question 23mediummulti select

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A developer is building a LangChain application that uses OCI Generative AI for text generation. They need to ensure that the application can handle failures gracefully when the OCI Generative AI service is temporarily unavailable. Which TWO strategies should they implement? (Choose two.)

Question 24mediummultiple choice

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A developer is using LangChain to create a chain that summarizes customer support tickets stored in an Oracle Database. The chain uses a prompt template that includes the ticket text and a placeholder for the summary. The developer wants to ensure that the chain can handle tickets of varying lengths without exceeding the token limit of the OCI Generative AI model. Which LangChain feature should they use to dynamically manage the prompt length?

Question 25easymultiple choice

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In LangChain, which component is responsible for connecting a language model to a retriever and a prompt template to answer questions based on retrieved documents?

Question 26mediummultiple choice

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A developer is building a RAG pipeline using LangChain and Oracle AI Vector Search. After loading and splitting PDF documents, they generate embeddings and store them in Oracle Database using OracleVS. Which method should they call on the vector store object to create a retriever that uses similarity search with a configurable number of results?

Question 27hardmultiple choice

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A company uses LangChain with OCI Generative AI. They notice that their agent-based application occasionally exceeds the rate limits of the OCI Generative AI service, causing errors. Which strategy is MOST effective for handling rate limits in a production LangChain application?

Question 28easymultiple choice

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Which LangChain memory type stores the entire conversation history as a list of messages and is best for simple, short conversations?

Question 29mediummultiple choice

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A developer wants to index a large corpus of HTML web pages for a RAG pipeline using LangChain. They need to load the content from URLs, split the text into chunks, and generate embeddings. Which combination of LangChain components should they use?

Question 30mediummultiple choice

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In LangChain, what is the purpose of the LCEL (LangChain Expression Language) | operator?

Question 31hardmultiple choice

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A team is building a conversational chatbot using LangChain and OCI Generative AI. They want to maintain a summary of the conversation rather than storing the entire history, to keep within token limits. Which memory class should they use, and what additional step is required when initializing the memory?

Question 32easymultiple choice

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Which Oracle AI Vector Search index type is designed for approximate nearest neighbor search and uses a navigable small world graph?

Question 33mediummultiple choice

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A developer wants to use LangChain to create an agent that can perform calculations and look up information from a database. Which tools should be provided to the agent?

Question 34hardmultiple choice

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A company has a collection of PDF documents that are 500 pages each. They want to build a RAG system using LangChain and FAISS. They need to ensure that each chunk has enough context for accurate retrieval while keeping chunk size small enough for efficient embedding. They also want some overlap between chunks to avoid losing context at boundaries. Which text splitter configuration is most appropriate?

Question 35mediummulti select

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A developer is building a LangChain application that uses OCI Generative AI service. They want to implement streaming responses from the LLM to improve user experience. Which TWO actions are necessary to enable streaming?

Question 36hardmulti select

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A data scientist is designing a RAG pipeline using LangChain and Oracle AI Vector Search. They want to ensure that the retrieved documents are diverse and not overly similar to each other. Which TWO approaches can achieve this?

Question 37easymultiple choice

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Which LangChain abstraction is responsible for storing and retrieving conversation history to maintain context across multiple turns in a chatbot?

Question 38mediummultiple choice

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An AI developer is building a document Q&A application using LangChain and OCI Generative AI. They need to split large PDF documents into smaller chunks before embedding. Which text splitter should they use to ensure splits respect sentence boundaries while also controlling chunk size?

Question 39easymultiple choice

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Which of the following best describes the role of a Retriever in a LangChain RAG pipeline?

Question 40hardmultiple choice

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In Oracle AI Vector Search, which index type is designed for approximate nearest neighbor search and employs a hierarchical navigable small world graph, offering high recall and fast search speeds for high-dimensional data?

Question 41mediummultiple choice

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A developer is using LangChain's LCEL to build a RAG pipeline. They want to add streaming of the final answer to the user. Which LCEL feature enables streaming output from the model?

Question 42mediummultiple choice

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A team wants to deploy a LangChain agent that can perform mathematical calculations, look up current weather, and search the web. Which tools should they include in the agent's toolkit?

Question 43mediummultiple choice

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A developer notices that the ConversationalRetrievalChain in their LangChain application is not retaining context from previous turns in the conversation. Which component is most likely missing or misconfigured?

Question 44easymultiple choice

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Which LangChain document loader would be most appropriate to load content from a public website for inclusion in a knowledge base?

Question 45hardmultiple choice

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In a LangChain RAG pipeline using Oracle AI Vector Search, the developer wants to retrieve chunks that are both relevant and diverse to cover multiple aspects of a query. Which retrieval method should they configure on the retriever?

Question 46mediummultiple choice

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A developer is using ChatPromptTemplate with MessagesPlaceholder to handle conversation history. What is the purpose of MessagesPlaceholder in the prompt template?

Question 47mediummulti select

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A developer is deploying a LangChain agent that uses an LLM from OCI Generative AI. The agent interacts with external APIs and must handle rate limits gracefully. Which TWO practices should the developer implement?

Question 48mediummulti select

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In a LangChain RAG pipeline using OCI Generative AI, which THREE components are essential for ingesting documents into a vector store?

Question 49mediummultiple choice

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A team is using LangChain's ConversationalRetrievalChain with ConversationBufferMemory to build a chatbot. After a few turns, the chatbot starts repeating information from earlier messages. What is the MOST likely cause?

Question 50hardmultiple choice

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An organization uses Oracle AI Vector Search in Oracle Database 23ai to store embeddings for a LangChain RAG application. They need to perform similarity search with high recall and low latency for a large dataset (10M vectors). Which index configuration should they choose?

Question 51easymultiple choice

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Which LangChain abstraction is used to wrap OCI Generative AI's chat models (e.g., Cohere Command R) for use in a LangChain chain?

Question 52easymultiple choice

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In a LangChain RetrievalQA chain, which method on a vector store is used to create the retriever object?

Question 53mediummultiple choice

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A developer is using LangChain's ConversationBufferMemory to store chat history. They notice that after many turns, the prompt becomes too large and exceeds the model's context window. What is the BEST memory type to use for this scenario?

Question 54easymultiple choice

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Which LangChain document loader would you use to load data from a webpage given its URL?

Question 55mediummultiple choice

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A LangChain application uses an agent with a calculator tool and a search tool. The agent is supposed to answer a question that requires both arithmetic and web lookup, but it only uses the search tool and gives an approximate answer. Which agent type is MOST likely to correctly combine the tools?

Question 56mediummultiple choice

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When using LangChain's RetrievalQA chain with `chain_type="stuff"`, what happens if the retrieved documents exceed the model's context window?

Question 57mediummulti select

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A developer is building a LangChain RAG pipeline with OCI Generative AI. Which TWO components are needed to create embeddings from documents and store them for retrieval?

Question 58hardmulti select

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A LangChain ReAct agent is failing to correctly use a custom tool that requires a specific parameter format. The agent keeps calling the tool with incorrect parameters. Which THREE steps should the developer take to debug and fix the issue?

Question 59easymulti select

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A company wants to use LangChain to build a chatbot that remembers previous conversations across sessions. Which TWO components should they use together?

Question 60mediummultiple choice

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A team is implementing a conversational chatbot that needs to remember a user's previous messages within the same session. They are using LangChain with OCI Generative AI. Which memory type and persistence approach should they choose for session-only memory?

Question 61easymultiple choice

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In LangChain's Expression Language (LCEL), what does the pipe (|) operator do when connecting components?

Question 62mediummultiple choice

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A developer is using LangChain's RecursiveCharacterTextSplitter with chunk_size=1000 and chunk_overlap=200. Which statement best describes the resulting chunks?

Question 63mediummultiple choice

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A team is building an agent using LangChain that needs to perform calculations and search the web for current information. Which combination of tools and agent type should they use?

Question 64hardmultiple choice

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A developer observes that their LangChain RAG pipeline sometimes returns duplicate or near-duplicate documents in the retrieved set, reducing the diversity of information provided to the LLM. Which retrieval parameter should they adjust to improve diversity?

Question 65easymultiple choice

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In LangChain, which component is responsible for loading data from a specific file format, such as PDF or CSV, into a document object?

Question 66hardmultiple choice

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An application uses ConversationalRetrievalChain with a vector store retriever. Users report that the chatbot sometimes provides answers that are not grounded in the retrieved documents. Which step in the RAG pipeline is most likely the cause?

Question 67easymultiple choice

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Which LangChain memory type is best suited for a long-running conversation where token consumption must be minimized, and the gist of previous exchanges should be retained?

Question 68mediummulti select

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A team is designing a production-grade LangChain agent that uses multiple tools, including a custom SQL query tool and a web search tool. They need to ensure the agent handles errors gracefully and logs all actions. Which three practices should they implement? (Choose THREE.)

Question 69hardmulti select

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An organization is deploying a RAG application with Oracle AI Vector Search. They need to ensure that the vector index supports low-latency queries and can handle updates to the underlying documents (inserts, deletes, modifications) without significant performance degradation. Which two index features should they consider? (Choose TWO.)

Question 70easymultiple choice

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Which LangChain component is responsible for storing and retrieving message history across multiple turns in a conversation?

Question 71hardmultiple choice

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A developer uses RecursiveCharacterTextSplitter with chunk_size=500 and chunk_overlap=100. After splitting, a particular chunk ends with an incomplete sentence. What is the likely cause?

Question 72mediummultiple choice

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A developer wants to create a LangChain chain that takes a user's question, retrieves relevant documents from a vector store, and then generates an answer using an LLM. Which chain is MOST appropriate for this task?

Question 73easymultiple choice

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In LangChain, which class should be used to wrap Oracle Cloud Infrastructure's Generative AI service as a chat model?

Question 74mediummultiple choice

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A developer needs to include the conversation history in a prompt for a LangChain chatbot. They want to insert previous exchanges between the user and the AI into the prompt at a specific position. Which component should they use?

Question 75easymultiple choice

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Which LangChain document loader should be used to load text from a web page given its URL?

Question 76mediummultiple choice

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A developer wants to ensure that a LangChain RetrievalQA chain returns diverse results, avoiding near-duplicate passages in the retrieved context. Which retrieval parameter should be configured on the retriever?

Question 77hardmultiple choice

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An application uses Oracle AI Vector Search with a VECTOR data type column. The team wants to perform a similarity search to find the top 5 most similar vectors to a query vector. Which SQL clause is necessary to achieve this?

Question 78mediummulti select

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A developer is building a conversational AI application using LangChain and needs to persist chat history across sessions. Which TWO approaches can they use? (Choose TWO.)

Question 79mediummulti select

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A company is deploying a LangChain application on OCI and needs to implement error handling and rate limit management. Which THREE strategies should they consider? (Choose THREE.)

Question 80hardmulti select

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You need to build a RAG pipeline using LangChain and OCI Generative AI. The pipeline must load PDF documents, split them into chunks, embed them, store in a vector store, and retrieve relevant chunks at query time. Which THREE components are essential? (Choose THREE.)

Question 81mediummultiple choice

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A developer is building a LangChain-powered application that must maintain conversation history across multiple turns. They want to store the chat history in Oracle Database. Which memory type and persistence approach should they use?

Question 82easymultiple choice

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Which LangChain component is responsible for splitting long documents into smaller, overlapping chunks before embedding?

Question 83hardmultiple choice

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A developer is using Oracle AI Vector Search with LangChain to build a RAG system on top of Oracle Database 23ai. They have created a VECTOR column and built an HNSW index. To improve recall at the cost of some accuracy, which index parameter should they adjust?

Question 84easymultiple choice

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A developer wants to use LangChain to connect to OCI Generative AI Service for text generation. Which LangChain wrapper class should they use for the chat model?

Question 85hardmultiple choice

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A company is deploying a LangChain agent for internal data analysis. The agent uses a ReAct pattern with tools including a Python REPL and a SQL query tool. What is the PRIMARY security concern when exposing this agent to non-technical users?

Question 86mediummultiple choice

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A developer needs to build a chain that first summarizes a long document, then translates the summary into French. Which LangChain chain type allows executing these steps in sequence with the output of one step feeding into the next?

Question 87mediummulti select

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A developer is building a RAG application with LangChain and Oracle AI Vector Search. They need to choose a text splitter that respects semantic boundaries (e.g., paragraphs, sentences) and a vector store that supports transactional updates in Oracle Database 23ai. Which TWO options should they select?

Question 88mediummulti select

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A team is building a LangChain agent that needs to answer questions using both a company-internal knowledge base (stored in Oracle AI Vector Search) and live web search. Which THREE components should they include in the agent setup?

Question 89mediummulti select

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A developer is using LangChain's RetrievalQA chain with a vector store. They want to improve the diversity of retrieved documents to avoid redundant information. Which TWO parameters or methods should they adjust?

Question 90easymulti select

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A developer is building a RAG pipeline with LangChain. They have loaded PDF documents with PDFLoader. Which TWO steps must they perform before indexing the documents into a vector store?

Question 91hardmulti select

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A company is deploying a LangChain application using OCI Generative AI. They need to comply with a policy that requires all prompts sent to the LLM to be logged for audit, and they must also handle rate limits gracefully. Which TWO strategies should they implement?

Question 92mediummulti select

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A developer is using LangChain's ChatPromptTemplate to construct a prompt for a conversational agent. The prompt should include a system message, a placeholder for conversation history, and the latest user query. Which TWO components should they include in the template?

Question 93hardmulti select

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A developer is using LangChain with Oracle AI Vector Search (OracleVS) to store embeddings. They notice that similarity search queries are slow. Which THREE actions could improve query performance?

Question 94mediummultiple choice

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A developer is building a LangChain pipeline against OCI Generative AI. They need a component that accepts a natural-language question plus a set of retrieved Documents and returns a single string answer, so it can be dropped directly into an LCEL pipeline with a RunnablePassthrough for the question and a retriever for the documents. Which LangChain runnable should they instantiate for this role?

Question 95hardmulti select

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A team is building an LCEL RAG pipeline over OCI Generative AI. They want to stream partial tokens to the browser as the model generates the final answer, and they also want to keep the retrieved source Documents available for citation display. Which TWO design choices support these goals? (Choose two.)

Question 96easymultiple choice

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A developer is assembling an LCEL chain that must send a fixed system instruction, the user's question, and the retrieved context to an OCI Generative AI chat model in a single call. Which LangChain component is responsible for combining these pieces into the final prompt sent to the model?

Question 97mediummultiple choice

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A developer is building a LangChain agent that must decide, at each step, whether to call an OCI Generative AI chat model for reasoning or to invoke a custom tool that queries an Oracle Autonomous Database. The agent must handle tool errors gracefully and continue reasoning. Which LangChain component is responsible for orchestrating this iterative reason-act-observe loop?

Question 98mediummultiple choice

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A developer is building a LangChain agent that must maintain memory across multiple turns in a conversation. The agent uses a custom tool to query an Oracle Database 23ai table. After several interactions, the agent starts repeating questions it already asked. Which component in the LangChain agent architecture is most likely responsible for retaining conversational context?

Question 99easymultiple choice

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A developer is using LangChain to build a retrieval-augmented generation (RAG) application with OCI Generative AI. They need to split a large PDF document into smaller chunks before embedding. Which LangChain component is specifically designed to split text into chunks based on character count?

Question 100hardmultiple choice

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A team's LangChain RAG application uses a vector store retriever configured with search_type="mmr". Users complain that answers omit important details that appear in the top-scoring document. Which change best addresses this while preserving result diversity?

Question 101hardmultiple choice

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A team is deploying a LangChain agent that uses OCI Generative AI as its reasoning model and a set of tools including a database query tool and a web search tool. In production, the agent occasionally calls the database tool with malformed SQL that returns errors, then loops by retrying the same malformed call. Which change would BEST reduce these loops while preserving the agent's ability to recover from transient failures?

Question 102hardmultiple choice

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A developer is using LangChain with OCI Generative AI to create a conversational agent. The agent uses a custom tool that executes SQL queries against an Oracle Autonomous Database. During testing, the agent sometimes generates SQL that is syntactically correct but semantically wrong, leading to incorrect results. Which LangChain feature should the developer implement to improve the agent's ability to generate accurate SQL queries?

Question 103mediummulti select

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A developer is instrumenting a LangChain application that calls OCI Generative AI models so that prompts, completions, token usage, and latency are captured for troubleshooting and cost analysis. Which TWO LangChain facilities should they use? (Choose two.)

Question 104easymultiple choice

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A developer is assembling a LangChain pipeline that must load HTML pages from an internal knowledge base, split them into chunks, embed the chunks with OCI Generative AI, and store them in a vector database. They want the loading step to produce Document objects with page content and metadata. Which LangChain component category is responsible for this loading step?

Question 105mediummultiple choice

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A developer is building a LangChain agent using OCI Generative AI (Cohere Command R) as the reasoning LLM. The agent must decide between three tools: a SQL query tool that returns structured rows, a web search tool, and a calculator. After several test runs, the agent consistently calls the SQL tool even for simple arithmetic questions. The developer confirms the tool descriptions are generic. What is the MOST likely cause and the correct fix?

Question 106hardmultiple choice

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A team uses LangChain's RecursiveCharacterTextSplitter with chunk_size=800 and chunk_overlap=200 to index a large policy manual into Oracle Database 23ai via OracleVS. Retrieval quality is poor for questions whose answers span section boundaries. The team wants to keep chunk sizes similar but improve boundary coverage. Which change BEST addresses the problem?

Question 107easymultiple choice

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A developer is assembling a LangChain pipeline that must retrieve documents from an OracleVS vector store and then answer questions with an OCI Generative AI chat model. They want the fewest moving parts and no custom prompt engineering for the final answer step. Which LangChain component should they use to combine retrieval and generation in one object?

Question 108mediummulti select

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A developer is instrumenting a LangChain application that calls OCI Generative AI models. They need to trace each chain step, inspect the exact prompt sent to the model, and measure per-step latency in a hosted dashboard for debugging in staging. (Choose two.)

Question 109hardmultiple choice

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A developer builds a LangChain chain that uses an OCI Generative AI chat model to answer questions over a product catalog stored in Oracle Database 23ai via OracleVS. Users report that answers occasionally cite product details that do not exist in the catalog. The retriever returns relevant documents, and the prompt instructs the model to answer only from context. Which action BEST reduces these unsupported answers?