1Z0-1127-25 Practice Questions 2 — 50 Questions

Question 1

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.

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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 2

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.

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GPT-4

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Stable Diffusion

Question 3

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.

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Use a different base model.

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

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Deploy a custom model hosted elsewhere.

Question 4

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.

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Pre-authenticated request

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OAuth 2.0 client credentials

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Username and password in the header

Question 5

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.

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Invalid API key in the request header

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Network connectivity issues

Question 6

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.

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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 7

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.

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OCI VCN flow logs for network traffic

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OCI Audit logs for all API calls

Question 8

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.

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Randomly sample 1000 records from production logs.

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

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Use the largest available public dataset from the internet.

Question 9

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.

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DocumentLoader

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ChatOCIGenAI

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TextSplitter

Question 10

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.

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The model uses a single tool call to answer the question

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The model generates a final answer without any intermediate steps

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The model is fine-tuned specifically for tool use

Question 11

A prompt engineer is testing two versions of a prompt for a content generation task. They want to measure which version produces more factual and concise outputs. Which evaluation approach is BEST?

Accepted Answer

Run A/B tests on a diverse set of inputs and score outputs based on predefined criteria. A/B testing with clear metrics (factuality, conciseness) is the standard method for comparing prompt variants. Manual inspection on a few cases is not statistically robust; other options are not comparative.

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Use only the first output from each prompt and manually compare

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Ask the model to self-evaluate its outputs

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Increase the temperature to see which prompt handles randomness better

Question 12

An administrator created the above IAM policies. A member of the GenerativeAIAdmins group reports they cannot invoke the model endpoint. Which permission is missing?

Accepted Answer

Permission to use or manage generative-ai-endpoint. The error occurs because the IAM policy grants permissions for 'generative-ai-model' but not for 'generative-ai-endpoint'. Invoking a model endpoint requires the 'use' or 'manage' permission on the 'generative-ai-endpoint' resource type, as the endpoint is the runtime interface that handles inference requests. Without this permission, the API call to the endpoint is denied, even if the user has access to the underlying model.

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Permission to access the compartment

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Permission to manage generative-ai-model

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Permission to read the model's training data

Question 13

Refer to the exhibit. A developer runs the OCI CLI command and receives the output. However, the text "Hello, how are you?" is actually a mix of English and French words. Why does the model assign only 0.03 to French?

Accepted Answer

The text is overwhelmingly English, so the model assigns a low probability to French.. The model's output shows a probability distribution over languages, and the text is predominantly English with only a few French words. The model assigns a low probability (0.03) to French because the overwhelming majority of tokens are English, making the text far more likely to be classified as English. This reflects how language identification models evaluate the overall composition of the input.

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The model is limited to identifying a single language per query.

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The model cannot detect multiple languages in a single text.

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The model's scores are normalized to sum to 1, so a high English score forces low others.

Question 14

A company is deploying a generative AI service on OCI using the OCI Data Science service with a large language model (LLM) in a VCN. The model inference endpoint must be accessible only from a private subnet within the same VCN. Which networking component should be configured to enable this?

Accepted Answer

Service Gateway. A Service Gateway enables private subnet resources to access OCI services (including the OCI Data Science model deployment endpoint) without traversing the internet. Since the inference endpoint must be accessible only from a private subnet within the same VCN, the Service Gateway provides the necessary private connectivity by routing traffic over the OCI network fabric, not through a NAT or internet gateway.

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NAT Gateway

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Dynamic Routing Gateway (DRG)

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Internet Gateway

Question 15

An organization is deploying a large language model on OCI using a dedicated AI cluster. They need to minimize inference latency. Which configuration step is most critical?

Accepted Answer

Configure the cluster to use high-bandwidth RDMA networking. RDMA (Remote Direct Memory Access) bypasses the CPU and kernel to transfer data directly between GPU memories, drastically reducing latency and CPU overhead. In a dedicated AI cluster on OCI, high-bandwidth RDMA networking (e.g., using RoCE v2 or InfiniBand) is the most critical step to minimize inference latency because model parallelism and tensor parallelism across nodes depend on fast, low-latency interconnects. Without RDMA, even with optimized model parallelism, the network becomes the bottleneck, increasing per-token latency.

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Set up a load balancer across multiple regions

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Use a single VM shape to reduce network hops

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Disable model parallelism to simplify setup

Question 16

Which THREE components are required to deploy a custom generative AI model on OCI Data Science model deployment?

Accepted Answer

An inference script (e.g., score.py) to handle prediction requests. OCI Data Science model deployment requires an inference script (typically score.py) to define how the model processes incoming prediction requests. This script is the entry point that loads the model artifact and executes inference logic, making it an essential component for serving predictions.

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A load balancer to distribute traffic

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An API signing key for authentication

Question 17

Which THREE steps are required to deploy a custom generative AI model using OCI Data Science Model Deployment?

Accepted Answer

Create a model artifact (e.g., pickle, ONNX) with inference code. Deploying a custom generative AI model via OCI Data Science Model Deployment requires packaging the model and its inference code into a standardized artifact format (e.g., pickle, ONNX). This artifact is the core input that the deployment runtime loads to serve predictions, making it an essential step in the workflow.

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Fine-tune the model using OCI Generative AI service

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Register the model in OCI Generative AI service

Question 18

A data scientist is using the OCI Generative AI service to generate text completions. The API calls are returning HTTP 400 errors with the message 'Invalid model parameters'. What is the most likely cause?

Accepted Answer

One or more model parameters (e.g., temperature, top_p) are outside the accepted range. The HTTP 400 error with 'Invalid model parameters' directly indicates that one or more of the parameters sent in the API request (such as temperature, top_p, max_tokens, or stop sequences) are outside the acceptable range defined by the OCI Generative AI service. For example, temperature must be between 0 and 1, and top_p between 0 and 1, and sending a value like 2.0 for temperature would trigger this error. The other options (expired key, rate limit, incorrect endpoint) would produce different HTTP status codes or error messages.

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The API key is expired

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The request exceeds the rate limit

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The endpoint URL is incorrect

Question 19

During multi-turn conversation with an OCI GenAI model, the model repeats user messages from earlier turns. What is the most likely cause?

Accepted Answer

Low presence penalty. A low presence penalty reduces the model's incentive to avoid repeating previously mentioned content. In multi-turn conversations, this can cause the model to echo user messages from earlier turns because the penalty is too weak to discourage repetition of tokens that have already appeared in the context window.

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Low top-p

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High temperature

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High frequency penalty

Question 20

A company wants to build a customer support chatbot using OCI Generative AI. They have a large number of historical support tickets. Which approach is most effective for leveraging this data to improve the chatbot's responses?

Accepted Answer

Fine-tune the Cohere Command model on the historical tickets using OCI Data Science.. Fine-tuning the Cohere Command model on the historical support tickets using OCI Data Science is the most effective approach because it adapts the model's weights to the specific domain language, terminology, and resolution patterns found in the company's data. This supervised learning process creates a specialized model that can generate accurate, context-aware responses for customer support queries, unlike generic prompting methods that lack deep domain adaptation.

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Use a pre-loaded prompt template from the OCI console.

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Increase the temperature parameter to 1.0 to encourage diverse responses.

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Use zero-shot prompting with the base model and include few-shot examples in the prompt.