1Z0-1127 Practice Test 1 — 25 Questions

Question 1

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?

Accepted Answer

The conversation history accumulates in the prompt, causing the model to focus on past messages. ConversationBufferMemory stores the entire conversation history, and each new query appends that history to the prompt. As history grows, the LLM may receive redundant context, causing repetition. Using ConversationSummaryMemory or limiting history length can mitigate this.

Answer

The chunk_size in the text splitter is too large

Answer

The LLM's max_tokens parameter is set too low

Answer

The retriever's similarity search returns irrelevant chunks

Question 2

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?

Accepted Answer

ConversationBufferMemory stored in-memory. ConversationBufferMemory stores the full message history in memory, suitable for short sessions. For session-only memory, in-memory persistence is sufficient; no external database is needed. Summary memory is more suited for long conversations where token limits are a concern.

Answer

ConversationBufferMemory persisted in a vector store

Answer

ConversationSummaryMemory stored in a local file

Answer

ConversationSummaryMemory persisted in Oracle Database

Question 3

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?

Accepted Answer

AgentExecutor to manage the ReAct loop. A LangChain agent requires a set of tools (vector store retriever as a tool, web search tool), and an AgentExecutor to orchestrate the reasoning and tool calls. The LLM is already part of the agent definition.

Answer

ConversationBufferMemory for storing chat history

Answer

A custom tool for logging agent steps

Question 4

Which TWO of the following are best practices when implementing a RAG application using OCI OpenSearch as a vector store?

Accepted Answer

Enable approximate nearest neighbor (ANN) search for large datasets.. Option C is correct because for large datasets, exact nearest neighbor (k-NN) search becomes computationally expensive and slow. OCI OpenSearch supports approximate nearest neighbor (ANN) search using algorithms like HNSW, which dramatically reduce latency while maintaining high recall, making it essential for production RAG applications with millions of vectors.

Answer

Use a large embedding dimension (e.g., 1536) to improve accuracy.

Answer

Set index.number_of_replicas to 0 to speed up indexing.

Answer

Store the embedding vectors in the _source field to simplify retrieval.

Question 5

What is the primary purpose of an embedding model in a RAG pipeline?

Accepted Answer

To convert text into numerical vectors.. The primary purpose of an embedding model in a RAG pipeline is to convert text into numerical vectors (embeddings) that capture semantic meaning. These vectors enable the retrieval component to efficiently find relevant documents by measuring similarity (e.g., cosine similarity) between the query and stored document embeddings. Without this conversion, the system cannot perform semantic search over unstructured text.

Answer

To generate human-like responses.

Answer

To rank search results.

Answer

To summarize long documents.

Question 6

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 7

A data scientist is using OCI Data Science to fine-tune a Cohere command model on domain-specific documents. They observe that the fine-tuned model generates repetitive text. What is the most likely cause?

Accepted Answer

The training dataset lacked diversity.. Repetitive text in fine-tuned models is a classic symptom of overfitting to a narrow or homogeneous training dataset. When the domain-specific documents lack diversity in phrasing, topics, or contexts, the model learns to latch onto the most common patterns and repeats them, rather than generalizing. This is not a hyperparameter tuning issue but a data quality issue.

Answer

The number of epochs was insufficient.

Answer

The learning rate was too high.

Answer

The batch size was too small.

Question 8

A company uses OCI Generative AI Agents to build a RAG application that answers questions from documents stored in OCI Object Storage. The knowledge base is updated daily. Which step is necessary to ensure the agent incorporates the latest documents?

Accepted Answer

Re-sync the knowledge base after updating the documents in Object Storage. The knowledge base indexes the data sources. To reflect changes in the source documents, you must re-sync or re-index the knowledge base. The agent endpoint or session API does not automatically refresh content.

Answer

Use the Embedding API to manually index each new document

Answer

Set the session API to refresh automatically

Answer

Recreate the agent each time documents change

Question 9

A security administrator needs to grant a group of data scientists the ability to use OCI Generative AI service resources in the compartment 'genai-dev'. They want to allow the group to create endpoints and run inference, but not to manage IAM policies. Which policy statement is correct?

Accepted Answer

Allow group DataScientists to use generative-ai-family in compartment genai-dev. Option A is correct because the 'use' verb in OCI IAM policies grants the ability to perform operational actions on resources without allowing management or administrative changes. For the OCI Generative AI service, 'use generative-ai-family' permits data scientists to create endpoints and run inference (the core actions needed), while explicitly preventing them from managing IAM policies or altering resource configurations. This aligns with the principle of least privilege required by the security administrator.

Answer

Allow group DataScientists to manage generative-ai-family in compartment genai-dev

Answer

Allow group DataScientists to inspect generative-ai-family in compartment genai-dev

Answer

Allow group DataScientists to read generative-ai-family in compartment genai-dev

Question 10

A company is using OCI Generative AI Agents to build a customer support assistant. They have uploaded product manuals to OCI Object Storage. Which two components are required to create the agent? (Select TWO)

Accepted Answer

Create a Generative AI Agent using the knowledge base. Option A is correct because a Generative AI Agent requires a knowledge base to provide domain-specific context for answering queries. The knowledge base is the source of truth that the agent uses to ground its responses, and it must be explicitly created and associated with the agent. Without a knowledge base, the agent would lack the product manual data needed for customer support.

Answer

Create an endpoint via InferenceClient

Answer

Upload data directly to the agent (not via knowledge base)

Answer

Provision a Dedicated AI Cluster

Question 11

A company wants to build a RAG application using OCI Generative AI Agents. Which TWO components are required to set up the agent?

Accepted Answer

A knowledge base. Option B is correct because a knowledge base is the core repository that stores the indexed content used by OCI Generative AI Agents to retrieve relevant information for answering queries. Without a knowledge base, the agent has no source of domain-specific data to ground its responses, making it a mandatory component.

Answer

An embedding model

Answer

A dedicated AI cluster

Answer

A fine-tuned model

Question 12

Refer to the exhibit. A developer runs the command and receives the error. What is the issue?

Accepted Answer

The max-tokens value exceeds the allowed range.. The max-tokens parameter is set to 600, which exceeds the allowed range of 1 to 500.

Answer

The message is too short.

Answer

The chat-id is invalid.

Answer

The endpoint is incorrect.

Question 13

A developer wants to integrate OCI GenAI into a Java application. Which SDK should they use?

Accepted Answer

OCI Java SDK.. Option B is correct because the OCI Java SDK provides native Java support for calling OCI services including GenAI.

Answer

OCI JavaScript SDK.

Answer

OCI Python SDK.

Answer

OCI CLI.

Question 14

Which TWO factors most significantly influence the computational cost of fine-tuning a large language model?

Accepted Answer

Number of model parameters. The number of model parameters directly determines the size of the weight matrices that must be updated during backpropagation. Fine-tuning requires storing gradients and optimizer states for each parameter, so the memory and compute scale linearly with parameter count. This is the primary driver of FLOPs (floating-point operations) per training step.

Answer

Batch size

Answer

Quantization bits

Answer

Dataset size

Question 15

An organization wants to use an LLM to summarize legal documents. Which consideration is most important for ensuring accurate summaries?

Accepted Answer

Fine-tune the model on a curated legal corpus. Legal documents require precise understanding, so fine-tuning on legal data is critical. Option B is wrong because larger models don't guarantee domain accuracy. Option C is wrong because pre-training from scratch is expensive and unnecessary. Option D is wrong because zero-shot may miss legal nuances.

Answer

Use the largest available general-purpose model

Answer

Rely on zero-shot summarization with careful prompting

Answer

Pre-train a new model from scratch on legal texts

Question 16

A healthcare startup is building an AI assistant to help doctors draft clinical notes from patient-physician conversations. They have a large language model that is fine-tuned on medical data. During testing, they notice the model occasionally generates plausible-sounding but incorrect medical recommendations. The startup wants to deploy the assistant to assist doctors, not replace them. They have the following options: (A) Deploy the model as-is and rely on doctors to catch errors, (B) Add a disclaimer that the model may make mistakes, (C) Implement a fact-checking pipeline that cross-references outputs with a trusted medical knowledge base before presenting to doctors, (D) Reduce the model's temperature to 0 to ensure deterministic outputs. Which option best balances safety and utility?

Accepted Answer

Implement a fact-checking pipeline that cross-references outputs with a trusted medical knowledge base.. Option A is correct because implementing a fact-checking pipeline that cross-references outputs with a trusted medical knowledge base directly mitigates the risk of hallucinated medical recommendations while preserving the assistant's utility. This approach leverages retrieval-augmented generation (RAG) principles to ground the model's outputs in verified facts, ensuring safety without sacrificing the flexibility needed for drafting clinical notes.

Answer

Add a disclaimer that the model may make mistakes.

Answer

Deploy the model as-is and rely on doctors to catch errors.

Answer

Reduce the model's temperature to 0 to ensure deterministic outputs.

Question 17

A team is fine-tuning a large language model for a domain-specific Q&A application. After fine-tuning, they observe that the model performs well on the training distribution but struggles with out-of-distribution (OOD) questions. Which approach would best improve OOD robustness?

Accepted Answer

Include a diverse set of examples from related domains in the fine-tuning dataset.. Option C is correct because incorporating diverse data during fine-tuning helps the model generalize to OOD inputs. Option A is wrong because increasing learning rate may cause catastrophic forgetting. Option B is wrong because reducing model size reduces capacity. Option D is wrong because early stopping on training loss may not help OOD.

Answer

Use early stopping based on training loss to avoid overfitting.

Answer

Reduce the model size to prevent overfitting to the training data.

Answer

Increase the learning rate during fine-tuning to adapt faster to new patterns.

Question 18

Which TWO measures can help reduce the risk of generating toxic or unsafe content when using OCI Generative AI Service?

Accepted Answer

Use few-shot prompting with examples that demonstrate safe and appropriate responses.. Few-shot prompting provides the model with explicit examples of safe, appropriate responses, which helps steer the model's behavior toward desired outputs and reduces the likelihood of generating toxic or unsafe content. This technique leverages in-context learning to align the model's responses with the provided examples, making it a practical measure for content safety.

Answer

Disable model monitoring and logging to reduce overhead.

Answer

Increase the temperature parameter to make output more deterministic.

Answer

Fine-tune the model on a large dataset without any safety filtering.

Question 19

Refer to the exhibit. A user runs the command shown and receives the error: 'ServiceError: NotAuthorizedOrNotFound'. What is the MOST likely cause?

Accepted Answer

The user does not have the 'inspect' permission on the model. The error 'NotAuthorizedOrNotFound' typically indicates either the model ID does not exist or the user lacks permission to view it. Option D is correct because the error message is generic to avoid information leakage. Option A would give a different error (e.g., invalid model ID), but the generic error suggests authorization or existence issues.

Answer

The CLI is not configured with OCI credentials

Answer

The model ID is incorrectly formatted

Answer

The model is in a different region than iad

Question 20

An enterprise deployed a custom fine-tuned model for generating financial reports. After the first month, the model's outputs began to include outdated information and occasional factual errors. The team suspects data drift. What is the best course of action?

Accepted Answer

Retrain the model on the latest financial data and monitor for drift.. Option D is correct because retraining with up-to-date data addresses the root cause of data drift. Option A is wrong because adjusting temperature may reduce creativity but not fix factual accuracy. Option B is wrong because increasing max tokens does not improve accuracy. Option C is wrong because switching to a different base model without retraining does not address drift.

Answer

Switch to a newer base model like Llama 3.1 without retraining.

Answer

Decrease the temperature parameter to 0.1 to reduce model creativity.

Answer

Increase the max tokens value to allow longer responses.