1Z0-1127-25 Practice Test 39 — 15 Questions

Question 1

Which TWO methods can be used to invoke a generative AI model deployed on OCI?

Accepted Answer

Sending HTTP requests to the model endpoint URL. Generative AI models deployed on OCI expose a RESTful endpoint that accepts HTTP requests (typically POST with JSON payloads) for inference. This is the standard method for programmatic access to the model, allowing integration with any HTTP client.

Answer

Using OCI Notifications service

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Using the OCI Console web interface

Answer

Using OCI Events service

Question 2

A retail company uses OCI Generative AI Agents to power a product recommendation chatbot on their e-commerce website. The chatbot is integrated with a knowledge base containing product descriptions, customer reviews, and inventory data. Recently, the chatbot has started recommending out-of-stock products frequently, leading to customer frustration. The development team verified that the knowledge base is updated in real-time with inventory data. The chatbot's configuration uses a chunking strategy with a chunk size of 500 tokens and an overlap of 50 tokens. The team suspects the issue is related to how the agent retrieves information. They have access to OCI Logging and Monitoring. Which course of action should the team take first?

Accepted Answer

Increase the chunk overlap from 50 to 150 tokens to ensure inventory status is captured in multiple chunks.. The core issue is that the chatbot retrieves chunks that contain product descriptions but may miss the inventory status because the chunking strategy does not reliably include both pieces of information together. Increasing the chunk overlap from 50 to 150 tokens ensures that inventory data, which may be at the boundary of a chunk, is captured in multiple overlapping chunks, thereby increasing the likelihood that the retrieval step returns a chunk containing both the product and its current stock level. This directly addresses the retrieval gap without altering model behavior or infrastructure.

Answer

Decrease the chunk size to 250 tokens to make chunks more specific.

Answer

Reduce the temperature parameter of the model to 0.2 to reduce hallucinations.

Answer

Enable auto-scaling on the AI cluster to improve response speed.

Question 3

A company wants to deploy a custom generative AI model for generating synthetic data for training other models. The model requires approximately 20GB of memory and must be accessible via a REST API with authentication. Additionally, the team needs to monitor for data drift over time. Which combination of OCI services best meets these requirements with minimal operational overhead?

Accepted Answer

OCI Data Science Model Deployment with OCI Monitoring and OCI Logging. OCI Data Science Model Deployment provides a managed environment for hosting custom generative AI models with REST API endpoints and built-in authentication via OCI IAM. It integrates natively with OCI Monitoring and OCI Logging to track data drift and operational metrics without requiring additional infrastructure setup, minimizing operational overhead.

Answer

OCI Compute with custom Docker container and Prometheus monitoring

Answer

OCI Functions with API Gateway for authentication

Answer

OCI Data Flow with OCI Data Catalog for model registry

Question 4

An OCI Generative AI practitioner observes that a Cohere Command model generates responses with outdated information about a recent event. The model was fine-tuned six months ago. Which technique should be applied to incorporate new knowledge without retraining the model?

Accepted Answer

Implement a RAG pipeline that indexes the latest documents into a vector store and retrieves relevant passages at query time. RAG retrieves relevant, current documents at inference time, providing up-to-date context without modifying the model parameters.

Answer

Use a longer context window and include all new articles in the prompt

Answer

Fine-tune the model again with the new data

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Increase the temperature parameter to encourage more creative outputs

Question 5

A prompt engineer is using the self-consistency technique to improve answer reliability. Which TWO steps are essential when implementing self-consistency?

Accepted Answer

Aggregate the outputs (e.g., by majority voting or marginalizing over reasoning steps) to select the most consistent answer. Self-consistency involves two essential steps. First, generate multiple independent reasoning paths by running the prompt several times with a non-zero temperature to ensure diversity (option E). Second, aggregate the outputs (e.g., by majority voting or marginalizing over reasoning steps) to select the most consistent answer (option D). Options D and E are correct. Option B is not essential because chain-of-thought prompting is a separate technique that can be used with self-consistency but is not required for the method.

Answer

Use tree-of-thought to explore all possible reasoning branches

Answer

Use a chain-of-thought prompt to guide the generation of reasoning paths.

Answer

Set temperature to 0 for reproducible outputs

Question 6

A company uses OCI Generative AI to power a chatbot for customer support. They notice that the model's responses sometimes contain factual inaccuracies. Which strategy would best reduce hallucination?

Accepted Answer

Implementing Retrieval-Augmented Generation (RAG).. Retrieval-Augmented Generation (RAG) grounds the model's responses in retrieved factual information, directly reducing hallucination. Increasing temperature increases randomness, fine-tuning on a larger corpus may not fix factual accuracy, and reducing max tokens does not affect correctness.

Answer

Increasing the temperature parameter.

Answer

Reducing the max token limit.

Answer

Fine-tuning the model on a larger general corpus.

Question 7

A company wants to build a multi-modal RAG system that can retrieve both text and images based on a user query. Which approach is most aligned with OCI GenAI capabilities?

Accepted Answer

Utilize a multi-modal embedding model from OCI GenAI to embed both text and images into a common vector space. OCI GenAI supports multi-modal models like Cohere's multimodal embedding model, which can embed text and images into a shared vector space, enabling retrieval across modalities. Separate text and image models would not align the vectors. OCR-based text-only approach loses image semantics. Using multiple vector stores complicates retrieval.

Answer

Use OCI Document Understanding to convert images to text, then index text

Answer

Use separate vector stores for text and image embeddings

Answer

Use image captioning to generate text descriptions and index those

Question 8

A team wants to deploy an LLM for real-time inference with low latency. Which OCI deployment option is best?

Accepted Answer

OCI Data Science Model Deployment with GPU shapes. OCI Data Science Model Deployment with GPU shapes is the best option because it provides managed, scalable, low-latency inference endpoints for LLMs. GPU shapes (e.g., VM.GPU.A10) are essential for the parallel matrix computations required by transformer-based models, and the deployment service supports auto-scaling and load balancing to maintain real-time response times.

Answer

OCI Functions with CPU

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OCI Events

Answer

OCI Streaming

Question 9

A startup needs to deploy a large language model for a customer support chatbot that requires low latency and cost efficiency. They are evaluating OCI Generative AI models. Which model type is most appropriate?

Accepted Answer

Instruct model (e.g., cohere.command). The startup requires low latency and cost efficiency for a customer support chatbot. Instruct models like cohere.command are specifically fine-tuned to follow conversational instructions and generate concise, task-oriented responses, making them ideal for interactive chatbot applications. They balance performance and cost better than base models, which lack instruction-following capability, and embedding models, which are designed for semantic search rather than text generation.

Answer

Embedding model (e.g., cohere.embed)

Answer

Image generation model

Answer

Base model (e.g., cohere.base)

Question 10

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?

Accepted Answer

The output_parser is expecting a string, but the model returns an AIMessage object. In LangChain's LCEL, the `|` operator passes the output of one component as input to the next. A typical LLM model invocation returns an `AIMessage` object (or `LLMResult`), not a plain string. The `output_parser` in this chain expects a string input (e.g., `StrOutputParser`), but receives an `AIMessage`, causing a type mismatch. This is the most common cause of the described failure.

Answer

The | operator requires all components to have the same input/output types

Answer

The prompt is not correctly formatting the input for the model

Answer

The model's streaming mode is enabled, causing the output to be streamed as chunks

Question 11

A developer is building a code generation assistant. The model occasionally produces syntactically correct but semantically wrong code. Which technique directly addresses semantic correctness?

Accepted Answer

Apply RLHF using human-validated code examples. Reinforcement Learning from Human Feedback (RLHF) directly addresses semantic correctness by fine-tuning the model using human-validated code examples. This process teaches the model to prefer outputs that are not only syntactically valid but also logically correct and aligned with developer intent, reducing semantically wrong code generation.

Answer

Expand the token vocabulary

Answer

Lower the temperature to 0

Answer

Increase beam search width

Question 12

An organization is deploying a generative AI model that requires GPU acceleration for inference. They are using OCI Data Science Model Deployment. The model is expected to handle variable traffic, with occasional spikes. Which scaling option should they configure to ensure cost-efficiency and responsiveness?

Accepted Answer

Configure autoscaling with a minimum of 1 and maximum of 10 GPU instances. Autoscaling with a minimum of 1 and maximum of 10 GPU instances allows the deployment to dynamically adjust capacity in response to variable traffic and spikes, ensuring cost-efficiency by scaling down during low demand and responsiveness by scaling up during peaks. OCI Data Science Model Deployment supports autoscaling policies that can be configured with GPU shapes, making it the optimal choice for a generative AI model requiring GPU acceleration.

Answer

Use OCI Generative AI on-demand API with a serverless endpoint

Answer

Use CPU-only instances and rely on batching

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Deploy with a fixed number of 1 GPU instance

Question 13

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.)

Accepted Answer

RecursiveCharacterTextSplitter. A is correct because RecursiveCharacterTextSplitter is the standard text splitter in LangChain for splitting documents into chunks while preserving semantic boundaries (e.g., paragraphs, sentences). It recursively splits on different separators (like newlines, spaces) to maintain context, which is critical for effective retrieval in a RAG pipeline.

Answer

CSVLoader

Answer

Chroma

Question 14

Refer to the exhibit. The dashboard shows latency grouped by modelId, but some points are missing for certain modelIds. Which of the following is the most likely reason?

Accepted Answer

The modelIds with missing data may have been deleted or are inactive. In OCI's Generative AI service, model deployments are associated with specific modelIds. If a modelId is deleted or its deployment is deactivated, the corresponding telemetry data (e.g., latency metrics) will no longer be reported, causing gaps in the dashboard. The dashboard aggregates metrics only for active modelIds, so missing points indicate that those modelIds are no longer in service.

Answer

The metric name is misspelled

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The aggregation interval is too short

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The compartmentId is incorrect

Question 15

A developer is using the ReAct pattern to build a reasoning agent. Which of the following best describes the two main steps in this pattern?

Accepted Answer

Reason and Act. ReAct stands for Reason + Act. The model first reasons about the question, then decides on an action (e.g., a tool call) to gather information, iterating until a final answer is reached.

Answer

Retrieve and Generate

Answer

Refine and Aggregate

Answer

Generate and Evaluate