1Z0-1127-25 LangChain and AI Application Development Practice Test 7 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Implement a custom LangChain callback that logs the prompt before sending it to the model. Option B is correct because a custom LangChain callback handler (e.g., implementing on_llm_start or on_llm_end) is the standard mechanism to intercept and log the exact prompt payload before it is dispatched to the OCI Generative AI endpoint, satisfying the audit requirement. Option D is correct because OCI Generative AI returns HTTP 429 (TooManyRequests) when throttling limits are exceeded, and wrapping the invocation in a retry with exponential backoff (e.g., via LangChain's max_retries or a retry decorator) lets the application recover gracefully instead of failing. Option A is not correct because choosing a faster model addresses latency, not audit logging or rate-limit handling. Option C is not correct because batching more requests together increases the chance of hitting rate limits and does nothing for prompt logging. Option E is not correct because stuffing conversation history into the system message neither logs prompts for audit nor mitigates rate-limit errors.

Answer

Use a faster LLM to reduce response time

Answer

Increase the batch size of requests to reduce the number of API calls

Answer

Store the full conversation history in the prompt's system message

Question 2

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?

Accepted Answer

SystemMessage. Option B (SystemMessage) is correct because a system message sets the agent's behavior, role, and instructions, which the scenario explicitly requires as part of the prompt. Option C (MessagesPlaceholder with variable name 'history') is correct because MessagesPlaceholder is the LangChain component designed to inject a dynamic list of prior messages (the conversation history) into a ChatPromptTemplate at runtime. The remaining options are not required components here: AIMessage (A) represents a prior model response and would only appear inside the history list, not as a standalone template element, and HumanMessage (D) represents a user turn, which is typically supplied at invocation time or included within the history placeholder rather than being a distinct required template component.

Answer

AIMessage

Answer

HumanMessage

Question 3

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?

Accepted Answer

Use a smaller embedding model (e.g., 384 dimensions instead of 1536). Option C is correct because using a smaller embedding model (e.g., 384 dimensions instead of 1536) reduces the vector dimensionality, which directly shrinks the distance-computation cost and memory footprint of each similarity search, making queries faster. Option D is correct because creating an HNSW vector index on the VECTOR column enables Oracle AI Vector Search to perform approximate nearest-neighbor (ANN) search instead of a full exact scan, which is the primary way to accelerate similarity queries in OracleVS. Option A is not correct because reducing chunk_size only changes how documents are split into smaller pieces; it increases the number of stored vectors and does not inherently speed up similarity search. Option B is not correct because switching to IVF with a low number of centroids reduces index granularity and can degrade recall and performance rather than reliably improving query speed. Option E is not correct because increasing efSearch raises the HNSW search breadth, which improves recall at the cost of more distance computations and therefore makes queries slower, not faster.

Answer

Reduce the chunk_size parameter in the text splitter

Answer

Switch from HNSW to IVF with a low number of centroids

Answer

Increase the efSearch parameter in the HNSW index

Question 4

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?

Accepted Answer

create_stuff_documents_chain. The requirement is a runnable whose inputs are retrieved Documents plus a question and whose output is a plain string answer. The stuff-documents chain constructor produces exactly that: it stuffs the document contents into a prompt, calls the model, and returns text. The retrieval chain and history-aware retriever operate at different layers, and the passthrough utility only manipulates dictionary keys.

Answer

RunnablePassthrough.assign

Answer

create_history_aware_retriever

Answer

create_retrieval_chain

Question 5

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.)

Accepted Answer

Use create_retrieval_chain so the final result includes both the generated answer and the retrieved source documents.. Streaming requires calling the streaming interface on the runnable that wraps the chat model, so token fragments can be forwarded as generated. Citations require a composition whose output retains the retrieved Documents next to the answer. Combining a streaming answer runnable with a retrieval chain that returns sources satisfies both, while blocking invocation, retriever-level streaming, and parallel wrappers do not.

Answer

Call invoke() on the answer chain and poll the returned object for partial tokens while it is still executing.

Answer

Enable streaming on the vector store retriever so document chunks are emitted token by token during similarity search.

Answer

Wrap the entire LCEL chain in RunnableParallel so every branch shares one streaming connection to the model.

Question 6

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?

Accepted Answer

A ChatPromptTemplate with system, context, and human message placeholders.. Prompt construction for a chat model belongs to a chat prompt template, which declares message roles and placeholders for the system instruction, retrieved context, and user question. Splitters prepare documents before retrieval, output parsers handle the response afterward, and passthrough utilities only move data through the chain without building messages.

Answer

A RunnablePassthrough that concatenates all inbound dictionary values into one string.

Answer

An OutputParser that reformats the model response into the system instruction format.

Answer

A RecursiveCharacterTextSplitter configured with the system instruction as a separator.

Question 7

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?

Accepted Answer

The AgentExecutor, which runs the agent loop, invokes tools, and feeds observations back to the model until it produces a final answer.. The AgentExecutor is the runtime that implements the reason-act-observe cycle: it calls the agent, executes the chosen tool, appends the observation to the scratchpad, and repeats until a final answer is produced. Its configuration options, including iteration limits and parsing-error handling, directly address the requirement to recover from tool failures and keep reasoning.

Answer

The Memory module, which stores intermediate tool outputs and replays them to the model on each iteration.

Answer

The Retriever, which decides which tool to invoke by performing a similarity search over the agent's tool descriptions.

Answer

The OutputParser, which converts the model's text into a structured AgentAction or AgentFinish object that terminates the loop.

Question 8

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?

Accepted Answer

The agent's memory module. The memory module in LangChain is responsible for persisting and retrieving conversational context. When an agent repeats questions, it indicates that prior turns are not being stored or accessed. The prompt template, tools, and output parser are stateless components that do not retain dialogue history. Therefore, the memory module is the correct component to address the issue.

Answer

The agent's prompt template

Answer

The agent's output parser

Answer

The agent's tool definitions

Question 9

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?

Accepted Answer

TextSplitter. TextSplitter is the LangChain abstraction specifically built to break large texts into smaller chunks. It supports various splitting methods, including character-based splitting, which matches the requirement. DocumentLoader loads the PDF, Embeddings vectorize chunks, and VectorStore stores them—none of these split text. Thus, TextSplitter is the correct choice for chunking the document.

Answer

VectorStore

Answer

Embeddings

Answer

DocumentLoader

Question 10

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?

Accepted Answer

Increase the fetch_k parameter so MMR selects from a larger candidate pool before reranking for diversity.. Maximal Marginal Relevance retrieves an initial candidate set of size fetch_k, then greedily selects documents that balance query similarity with dissimilarity from already-selected results. When fetch_k is too small, relevant documents can be filtered out before the diversity pass. Increasing fetch_k gives MMR a richer pool so important top-scoring content survives selection while redundancy is still reduced.

Answer

Increase the chunk_overlap in the text splitter so retrieved chunks share more content with neighboring chunks.

Answer

Reduce the k parameter so that fewer documents are returned and each answer focuses on the single best match.

Answer

Switch search_type to "similarity_score_threshold" and set a low score threshold to include more documents.

Question 11

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?

Accepted Answer

Wrap the database tool so that on error it returns a structured error message describing the failure and the expected input format, and configure the agent with a modest max_iterations.. Agents loop when tool errors do not carry enough information for the model to change its plan. A tool wrapper that translates exceptions into structured, instructive messages lets the reasoning model adjust its next action, while a bounded max_iterations caps worst-case cost. This combination preserves recovery from transient failures and enables self-correction of malformed SQL without unbounded retries.

Answer

Increase the model temperature so the agent explores a wider variety of tool calls and eventually finds one that works.

Answer

Remove the database tool from the agent's tool list and require users to run SQL manually before asking questions.

Answer

Set max_iterations to 1 so the agent can only make a single tool call per user request.

Question 12

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?

Accepted Answer

Few-shot prompting with examples of correct SQL queries. Few-shot prompting with correct SQL examples teaches the model the specific schema and query patterns, reducing semantic errors. Increasing temperature adds randomness, syntax validation only catches syntactic issues, and a larger model alone doesn't address schema-specific accuracy. Therefore, few-shot prompting is the most effective LangChain feature to implement here.

Answer

Adding a separate validation tool that checks SQL syntax

Answer

Increasing the temperature setting of the language model

Answer

Switching to a larger language model with more parameters

Question 13

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.)

Accepted Answer

LangSmith tracing, by setting environment variables so runs are recorded and inspected in the LangSmith UI.. Callbacks provide programmatic event hooks that expose prompts, completions, errors, and timing for each LLM and tool invocation, while LangSmith tracing records runs with inputs, outputs, token usage, and durations in an inspectable UI. Together they deliver the observability needed for troubleshooting and cost analysis without altering application logic.

Answer

The ConversationBufferMemory class, by storing prior turns alongside token counts.

Answer

The VectorStore retriever, by logging the documents returned for each query.

Answer

Pydantic output parsers, by defining a schema that validates the model's response structure.

Question 14

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?

Accepted Answer

Document loaders. Document loaders are the LangChain abstraction for reading a source format and emitting Document objects with page content and metadata. They sit at the front of the ingestion pipeline, before splitting and embedding, which is exactly the responsibility described for loading HTML pages from the internal knowledge base.

Answer

Text splitters

Answer

Retrievers

Answer

Output parsers

Question 15

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?

Accepted Answer

The tool descriptions are too generic, so the LLM cannot distinguish them; the developer should rewrite each tool's description to state its exact purpose, inputs, and when to use it.. LangChain agents select tools by reasoning over each tool's name and description included in the prompt. When descriptions are generic, the LLM lacks the signal needed to map a question to the right tool, so it falls back to a familiar one. Rewriting descriptions to state purpose, inputs, and when to use each tool restores the discriminative information and corrects routing.

Answer

The agent's prompt is too vague; the developer should switch to a zero-shot ReAct agent type, which removes the need for tool descriptions.

Answer

The agent's memory is retaining the first tool call and repeating it; the developer should disable memory on the agent executor.

Answer

The OCI Generative AI chat model does not support function calling, so the developer must replace it with a model that does before any agent can work.