1Z0-1127-25 Practice Exam 3 — 20 Questions

Question 1

A manufacturing company uses OCI OpenSearch to build a RAG application that retrieves procedural documents. After deployment, queries often return outdated procedures even though the vector index was refreshed. What is the most likely cause?

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

Accepted Answer

The full-text search index is not synchronized with the vector index after updates.. In a RAG application using OCI OpenSearch, the vector index and full-text search index are separate. When procedural documents are updated, the full-text search index may reflect changes immediately, but the vector index requires explicit re-indexing or synchronization to update embeddings. If the vector index is not refreshed after updates, queries can still retrieve outdated vector representations, leading to outdated results despite the index being refreshed.

Answer

The embedding model was fine-tuned on outdated data.

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The BM25 scoring algorithm prioritizes older documents due to term frequency.

Answer

The chunk overlap percentage is too high, causing duplicate context.

Question 2

An organization needs to select a tokenisation algorithm for a multilingual LLM that will process English, Chinese, and Korean text efficiently. Which tokenisation method is BEST suited for this requirement?

Accepted Answer

SentencePiece. SentencePiece is language-agnostic, works directly on raw text without requiring pre-tokenisation (e.g., whitespace splitting), and handles languages like Chinese and Korean where word boundaries are not obvious. BPE and WordPiece typically require pre-tokenisation, making them less suitable for CJK languages.

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WordPiece

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Byte-Pair Encoding (BPE)

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Character-level tokenisation

Question 3

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?

Accepted Answer

A memory component like ConversationBufferMemory. ConversationalRetrievalChain requires a Memory component to store and retrieve chat history. Without Memory, the chain treats each query independently. The retriever, document splitter, and embeddings are responsible for retrieval and storage, not conversation history.

Answer

A document splitter to chunk the history

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A retriever with appropriate search parameters

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An embedding model to vectorize the history

Question 4

A team fine-tuned a Cohere Command R model using the T-Few technique on a dataset of JSONL prompt/completion pairs. After deployment, they observe that the model's responses are too repetitive. Which parameter adjustment in the OCI Generative AI Playground would BEST address this issue?

Accepted Answer

Increase frequency penalty. Increasing the frequency penalty discourages the model from repeating tokens that have already appeared, reducing repetitive outputs. Temperature and max tokens address randomness and length, not repetition specifically. Stop sequences are for terminating generation early.

Answer

Increase max tokens

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Decrease temperature

Answer

Add a stop sequence

Question 5

Which THREE are essential steps in the prompt engineering process for an LLM?

Accepted Answer

Test the prompt with a variety of input examples. Testing the prompt with a variety of input examples is essential to evaluate the LLM's generalization, robustness, and sensitivity to different phrasing or contexts. This step helps identify edge cases, biases, or inconsistencies in the model's responses before deployment.

Answer

Fine-tune the model on a domain corpus

Answer

Quantize the model to INT8

Question 6

An organization needs to deploy a model that can both understand and generate text, such as for a translation task where the input is in English and output is in French. Which model architecture is most suitable?

Accepted Answer

Encoder-decoder (e.g., T5). Encoder-decoder architectures like T5 are designed for sequence-to-sequence tasks. The encoder processes the input sequence, and the decoder generates the output sequence, making it ideal for translation.

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Decoder-only (e.g., GPT)

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Encoder-only (e.g., BERT)

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Mixture of Experts (MoE)

Question 7

Which THREE factors should be considered when choosing between fine-tuning a model and using a pre-trained model with prompt engineering? (Select three.)

Accepted Answer

Size of available dataset. The size of the available dataset is a critical factor: fine-tuning requires a sufficiently large, labeled dataset (typically thousands of examples) to adjust model weights effectively, while prompt engineering can work with zero or few examples. If the dataset is too small, fine-tuning risks overfitting and poor generalization, making prompt engineering the safer choice.

Answer

Required response time

Answer

Internet connectivity

Question 8

A data scientist in group DataScientists uses the OCI Generative AI SDK to start a fine-tuning job in compartment AIResources. They receive the error shown. What is the most likely cause?

Accepted Answer

The fine-tuning job requires additional IAM policies for accessing the training data in Object Storage.. The error message indicates a permissions issue related to accessing training data in Object Storage. When using the OCI Generative AI SDK to start a fine-tuning job, the data scientist's IAM policies must explicitly grant read access to the bucket and objects containing the training data. Without these policies, the API call fails even if the user is in the correct group and the compartment exists.

Answer

The compartment AIResources does not exist.

Answer

The fine-tuning API is not yet available in that region.

Answer

The data scientist is not in the DataScientists group.

Question 9

In few-shot prompting, what is the primary purpose of including examples in the prompt?

Accepted Answer

To provide a template for the desired output format and reasoning pattern. Examples guide the model on the desired input-output pattern, improving task performance without fine-tuning.

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To reduce the need for a system prompt

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To increase the model's vocabulary

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To decrease the computational cost of inference

Question 10

Refer to the exhibit. A user receives this error when calling the OCI Gen AI inference endpoint. What is the most likely cause?

Accepted Answer

The model is not deployed in the region. The error indicates that the model is not available in the specified region. OCI Gen AI models are deployed regionally, and each region supports only a specific subset of models. If the user calls an endpoint in a region where the requested model has not been deployed, the service returns an error because the model's inference endpoint does not exist in that region's routing table.

Answer

The region name is misspelled

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The API key is expired

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The model name is incorrect

Question 11

An organization wants to use OCI Generative AI for real-time document translation. They need high availability across regions. Which deployment option meets this requirement?

Accepted Answer

Multiple dedicated AI clusters in different regions with a load balancer. Deploying multiple dedicated AI clusters across different regions with a load balancer ensures high availability by distributing traffic and providing failover if one region becomes unavailable. OCI Generative AI dedicated AI clusters are provisioned per region, and a load balancer can route requests to healthy clusters, meeting the requirement for real-time document translation with cross-region redundancy.

Answer

Single dedicated AI cluster in one region

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Single serverless endpoint

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Multiple serverless endpoints in different regions

Question 12

Which THREE models are available as part of the OCI Generative AI service?

Accepted Answer

Llama 3. Llama 3 is one of the open-source large language models (LLMs) available through the OCI Generative AI service, alongside Cohere models. OCI Generative AI provides managed access to Llama 3 for text generation tasks, allowing users to deploy and fine-tune it within Oracle Cloud Infrastructure.

Answer

GPT-4

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Stable Diffusion

Question 13

A company is using OCI Generative AI to generate code snippets and notices that the model sometimes produces code with security vulnerabilities. They have a small dataset of secure code examples. Which approach would be most effective to reduce vulnerabilities?

Accepted Answer

Use prompt engineering with security constraints in the instruction.. Prompt engineering allows the company to inject security constraints directly into the instruction without requiring additional training data or infrastructure. By crafting a prompt that explicitly requests secure code (e.g., 'Generate code that follows OWASP Top 10 best practices and avoids SQL injection, XSS, and buffer overflows'), the model can leverage its existing knowledge to produce safer outputs. This approach is immediate, cost-effective, and does not depend on the size or quality of the small secure code dataset.

Answer

Use a different base model.

Answer

Fine-tune the model on the small secure code dataset.

Answer

Deploy a custom model hosted elsewhere.

Question 14

A developer wants to integrate OCI Generative AI into a web application. Which API authentication method is recommended for programmatic access?

Accepted Answer

API key-based signing. OCI APIs require request signing using an API signing key (an RSA key pair) for programmatic access. The developer must generate a key pair, upload the public key to the OCI console, and then use the private key to sign each HTTP request using the OCI Signature Version 1 algorithm (based on RFC 2104 HMAC-SHA256). This ensures authentication without transmitting secrets over the wire.

Answer

Pre-authenticated request

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OAuth 2.0 client credentials

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Username and password in the header

Question 15

A developer is troubleshooting an OCI Generative AI inference request that returns a 400 Bad Request error. Which three common causes could result in this error? (Choose three.)

Accepted Answer

Incorrect endpoint URL. A 400 Bad Request error indicates the server cannot process the request due to client-side issues. An incorrect endpoint URL (A) is a common cause because the request is sent to the wrong OCI Generative AI service endpoint (e.g., using a chat endpoint for a text generation model), leading to a malformed request that the server rejects. Missing required parameters (C) in the request body, such as 'compartmentId' or 'modelId', also triggers a 400 error as the API cannot validate or process the inference without them. Exceeding the model's maximum token limit (D) results in a 400 error because the input or output exceeds the model's configured token capacity, which the API validates before processing.

Answer

Invalid API key in the request header

Answer

Network connectivity issues

Question 16

An enterprise is deploying a generative AI model that must comply with data residency regulations. Which two configurations should they implement? (Select TWO.)

Accepted Answer

Set up OCI IAM policies to prevent data egress from the region for the model's resources. OCI IAM policies can explicitly deny data egress from a specific region, ensuring that the generative AI model's resources (such as training data, model artifacts, and inference endpoints) remain within the region that satisfies data residency regulations. This is achieved by writing policy statements that restrict the movement of data across regional boundaries, which is a direct control for compliance.

Answer

Enable OCI Logging for all API calls

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Use OCI Object Storage with cross-region replication for redundancy

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Store encryption keys in an OCI Vault in a different region

Question 17

A DevOps engineer is setting up monitoring and logging for a generative AI inference endpoint. Which three resources should they enable? (Select THREE.)

Accepted Answer

OCI Logging for inference requests and responses. OCI Logging captures detailed logs of inference requests and responses, which is essential for auditing, debugging, and analyzing the behavior of a generative AI endpoint. This service provides a centralized repository for log data, enabling DevOps engineers to track input prompts and model outputs for compliance and troubleshooting purposes.

Answer

OCI VCN flow logs for network traffic

Answer

OCI Audit logs for all API calls

Question 18

A data scientist wants to fine-tune a generative AI model on proprietary customer data. What is a best practice for preparing the training dataset?

Accepted Answer

Curate a dataset of domain-specific examples with clear input-output pairs.. Fine-tuning a generative AI model on proprietary data requires a curated, domain-specific dataset with clear input-output pairs. This ensures the model learns the desired task (e.g., summarization, classification) without introducing noise or irrelevant patterns, which is critical for OCI Generative AI Service fine-tuning where data quality directly impacts model performance.

Answer

Randomly sample 1000 records from production logs.

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Use the same dataset as the base model's pre-training data.

Answer

Use the largest available public dataset from the internet.

Question 19

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?

Accepted Answer

OCIGenAIEmbeddings. OCIGenAIEmbeddings converts text into embeddings, and a vector store (e.g., FAISS, Chroma, OracleVS) stores those embeddings for similarity search. Document loaders and text splitters are used before embedding but are not part of the embedding/storage step itself.

Answer

DocumentLoader

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ChatOCIGenAI

Answer

TextSplitter

Question 20

A prompt engineer is designing a ReAct pattern prompt to enable an LLM to use external tools. Which of the following is a key characteristic of the ReAct pattern?

Accepted Answer

The model produces a chain of thought followed by an action, then observes the result and continues reasoning. ReAct interleaves reasoning steps (e.g., 'I need to find the current weather') with actions (e.g., 'Action: call weather API') and observations, allowing the model to reason and act in a cycle.

Answer

The model uses a single tool call to answer the question

Answer

The model generates a final answer without any intermediate steps

Answer

The model is fine-tuned specifically for tool use