1Z0-1127-25 Practice Test 44 — 15 Questions

Question 1

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

Accepted Answer

To pipe the output of one component into the next component. The | operator in LCEL is used to compose chains by passing the output of one component as input to the next.

Answer

To run two chains in parallel

Answer

To concatenate two prompt templates

Answer

To compare outputs of two different models

Question 2

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?

Accepted Answer

ReAct agent (e.g., zero-shot-react-description). The ReAct agent (zero-shot-react-description) is designed to reason step-by-step and decide which tool to use based on the task. It can combine the calculator and search tools by first performing a web lookup to get necessary data, then using the calculator tool for arithmetic, producing an exact answer. This agent type explicitly supports multi-step reasoning and tool selection, unlike simpler agents that may default to a single tool.

Answer

Zero-shot agent without ReAct (e.g., zero-shot-react-description)

Answer

Structured tool chat agent

Answer

Conversational agent with memory

Question 3

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?

Accepted Answer

WebBaseLoader, RecursiveCharacterTextSplitter, OCIGenAIEmbeddings. WebBaseLoader loads HTML content, RecursiveCharacterTextSplitter splits it, and OCIGenAIEmbeddings generates embeddings.

Answer

PDFLoader, RecursiveCharacterTextSplitter, OCIGenAIEmbeddings

Answer

CSVLoader, RecursiveCharacterTextSplitter, OCIGenAIEmbeddings

Answer

WebBaseLoader, TokenTextSplitter, OCIGenAI

Question 4

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?

Accepted Answer

ReAct agent with a calculator tool and a search tool. A ReAct agent reasons step-by-step and uses tools like a calculator for math and a search tool for web queries. The ReAct framework is designed for such tool-use tasks. The other options either omit necessary tools or use an incorrect agent type.

Answer

Zero-shot agent with only a calculator tool

Answer

Conversational agent with a search tool and memory

Answer

ReAct agent with a search tool only

Question 5

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

Accepted Answer

ConversationBufferMemory. ConversationBufferMemory stores the full history, making it suitable for short dialogues but not for long ones due to token limits.

Answer

ConversationTokenBufferMemory

Answer

ConversationSummaryMemory

Answer

ConversationBufferWindowMemory

Question 6

A company requires a generative AI service to automatically summarize customer support transcripts. Which OCI Generative AI model is most suitable for this task?

Accepted Answer

Cohere Command. Cohere Command is a large language model specifically designed for text generation tasks such as summarization, making it the most suitable choice for automatically summarizing customer support transcripts. Unlike embedding models or base Llama variants, Command is optimized for instruction-following and generating coherent, concise summaries from conversational data.

Answer

Llama 3 70B

Answer

Cohere Embed

Answer

Fine-tuned Llama 2

Question 7

Which OCI service provides pre-trained models for custom text classification without requiring fine-tuning?

Accepted Answer

OCI AI Language. B is correct because OCI AI Language provides pre-trained models that can perform custom text classification out-of-the-box without requiring fine-tuning. It offers built-in models for common NLP tasks like sentiment analysis, entity extraction, and text classification, allowing users to classify text into custom categories defined by their own labels without additional training.

Answer

OCI Generative AI

Answer

OCI Data Science

Answer

OCI Vision

Question 8

A team wants to evaluate an LLM's performance on a text classification task. Which metric is most appropriate for a balanced dataset?

Accepted Answer

Accuracy. Accuracy is the most appropriate metric for evaluating an LLM on a text classification task with a balanced dataset because it directly measures the proportion of correctly predicted labels out of total predictions. For balanced classes, accuracy provides a reliable and intuitive performance indicator without the distortion caused by class imbalance.

Answer

BLEU score

Answer

Perplexity

Answer

ROUGE score

Question 9

In OCI Generative AI, when using the Cohere Command model, which parameter is used to discourage the model from repeating the same phrases?

Accepted Answer

frequency_penalty. Frequency penalty reduces the likelihood of tokens that have already appeared, directly targeting repetition of phrases.

Answer

presence_penalty

Answer

temperature

Answer

top-k

Question 10

A developer wants to deploy a RAG application using OCI Generative AI for both embedding and text generation while minimizing costs. Which strategy is most effective?

Accepted Answer

Cache frequent queries and their embeddings. Caching embeddings for frequent queries eliminates repeated embedding API calls, directly reducing cost.

Answer

Use a larger generation model

Answer

Reduce chunk size to decrease embedding calls

Answer

Use a larger embedding model for better accuracy

Question 11

A developer is using the OCI Generative AI Chat API to create a customer support bot. They want the bot to maintain a consistent personality and follow specific guidelines. Which TWO settings should they use?

Accepted Answer

System prompt. A system prompt sets the bot's behavior and guidelines, and preamble override allows customizing the model's initial instructions. Temperature and max tokens do not define personality or guidelines.

Answer

Temperature

Answer

Frequency penalty

Answer

Max tokens

Question 12

An OCI customer is deploying a chatbot using a pre-trained LLM. They are concerned about the model generating biased or harmful content. Which TWO strategies should they implement as part of their responsible AI approach? (Choose two.)

Accepted Answer

Set up a human-in-the-loop review for sensitive queries. Content filtering and human review are direct mitigations. Training from scratch is impractical; modifying sampling does not address bias; increasing context window is irrelevant.

Answer

Train the model from scratch on a curated dataset

Answer

Increase the context window to include more examples

Answer

Use top-k sampling with k=1

Question 13

A data scientist needs to fine-tune a large language model on a custom dataset of 10,000 prompt-completion pairs. They want to minimize cost while still updating the model effectively. Which fine-tuning technique is used by OCI Generative AI service?

Accepted Answer

T-Few fine-tuning. OCI Generative AI uses T-Few, which updates only a small number of parameters via learned transformations, reducing computational cost while maintaining performance. Adapter, LoRA, and prefix tuning are general PEFT methods but not the specific technique offered by OCI.

Answer

Prefix tuning

Answer

Adapter fine-tuning

Answer

LoRA fine-tuning

Question 14

A company is deploying a generative AI model for a real-time inference API. To ensure high availability and cost efficiency under variable load, which two configurations should they implement? (Choose two.)

Accepted Answer

Set the number of model deployment replicas to at least 2. Deploying at least two replicas ensures high availability by eliminating a single point of failure; if one replica fails, the other can still serve inference requests. This is a standard best practice for production workloads on OCI, where model deployment replicas are distributed across fault domains to maintain service continuity.

Answer

Use a single replica with a larger GPU to handle all traffic

Answer

Deploy the model in a single availability domain to simplify management

Answer

Disable connection draining on the load balancer

Question 15

An organization is implementing a RAG system using OCI GenAI. Which two are best practices for optimizing retrieval and generation? (Choose two.)

Accepted Answer

Use semantic search (embeddings) for document retrieval. Semantic search using embeddings retrieves documents based on meaning rather than keyword matching, which significantly improves the relevance of context provided to the LLM in a RAG system. This aligns with best practices for OCI GenAI, where embedding models convert text into vector representations for similarity search in a vector database.

Answer

Use the same embedding model for both retrieval and generation

Answer

Store all documents in a single large index

Answer

Disable summarization to save inference costs