1Z0-1127 Practice Test 22 — 15 Questions

Question 1

An organization deploys a fine-tuned model for legal document analysis using OCI Generative AI Service. They need to ensure that only authorized users in the 'LegalTeam' group can access the model endpoint. Which policy statement should be used?

Accepted Answer

Allow group LegalTeam to use generative-ai-model in compartment ABC. Option A is correct because the 'use' verb on the 'generative-ai-model' resource type grants the LegalTeam group permission to invoke the model endpoint for inference, which is the minimum privilege required for accessing a deployed fine-tuned model in OCI Generative AI Service. The 'use' permission specifically allows calling the model for text generation or analysis without granting broader management or read capabilities.

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Allow group LegalTeam to manage generative-ai-family in compartment ABC

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Allow group LegalTeam to read generative-ai-model in compartment ABC

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Allow group LegalTeam to inspect generative-ai-model in compartment ABC

Question 2

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. Option C is correct because 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.

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Using OCI Notifications service

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Using the OCI Console web interface

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Using OCI Events service

Question 3

A data scientist needs to generate vector embeddings for a large corpus of text documents to use in a semantic search application. Which OCI service is best suited for this task?

Accepted Answer

OCI Generative AI. OCI Generative AI is the correct choice because it provides a managed service for generating vector embeddings from text using large language models (LLMs) like Cohere. This service is specifically designed for tasks such as semantic search, where embeddings capture the meaning of text to enable similarity comparisons. OCI Vision, Speech, and Language focus on other modalities (images, audio, and NLP tasks like sentiment analysis) and do not offer embedding generation for semantic search.

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OCI Vision

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OCI Speech

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OCI Language

Question 4

A company needs to integrate OCI Generative AI Service with an existing application that uses OCI IAM for authentication. They want to use resource principal to allow the application to call the service without storing API keys. Which step is REQUIRED?

Accepted Answer

Create a dynamic group and a policy granting access to the Generative AI Service. Resource principal authentication in OCI requires the application to be represented by a dynamic group, which matches instances or resources based on defined rules. A policy must then grant that dynamic group access to the Generative AI Service. This avoids storing API keys by using OCI IAM's built-in resource principal token exchange.

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Create an OCI API key for the application

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Enable the Generative AI Service for resource principal in the tenancy

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Assign the application to a group with admin privileges

Question 5

A data scientist is using OCI Generative AI Service to generate product descriptions. They notice that the output often repeats phrases. Which parameter adjustment would MOST directly address this issue?

Accepted Answer

Increase the frequency penalty. Option C is correct because the frequency penalty directly reduces the likelihood of the model repeating the same phrases by penalizing tokens that have already appeared in the generated text. In OCI Generative AI Service, this parameter subtracts a fixed value from the log-probability of each token each time it is generated, making repeated tokens less likely to be chosen again. This is the most direct mechanism to address repetitive output.

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Increase the temperature

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Increase the max tokens

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Decrease the top-p value

Question 6

A company runs batch inference jobs daily using the OCI Generative AI service. The current cost is higher than expected. Which change would most effectively reduce cost while maintaining throughput?

Accepted Answer

Switch from on-demand to dedicated AI cluster with batch endpoint.. Switching from on-demand to a dedicated AI cluster with a batch endpoint reduces cost because dedicated clusters provide reserved capacity at a lower per-token rate compared to on-demand pay-per-token pricing, and batch endpoints allow you to process multiple inference requests in a single job, amortizing overhead and reducing idle time. This combination directly addresses the high cost of per-request on-demand pricing while maintaining the same throughput for daily batch jobs.

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Reduce the max token limit for all requests.

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Use a larger model to reduce retries.

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Increase the number of parallel requests to improve efficiency.

Question 7

A company fine-tunes an LLM on internal support tickets. After deployment, the model hallucinates company-specific product names. What is the most effective mitigation?

Accepted Answer

Implement RAG with a verified product database. RAG (Retrieval-Augmented Generation) grounds the LLM's output in a verified product database, providing factual context that prevents hallucination of company-specific product names. Unlike fine-tuning, which only adjusts model weights and can still produce plausible but incorrect names, RAG retrieves exact records at inference time, ensuring accuracy for proprietary terminology.

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Switch to a smaller model to reduce hallucination risk

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Use prompt engineering to remind the model to be accurate

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Fine-tune further with more ticket data

Question 8

Which THREE factors should be considered when choosing a base model for OCI Gen AI?

Accepted Answer

Availability in the desired region. Option A is correct because OCI Gen AI models are deployed in specific regions, and availability varies by region due to data residency requirements and infrastructure placement. Before selecting a base model, you must verify that the model is available in your desired OCI region to avoid deployment failures or latency issues.

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Supported languages

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Training data format

Question 9

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.

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Expand the token vocabulary

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Lower the temperature to 0

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Increase beam search width

Question 10

A company wants to use OCI Generative AI to analyze legal documents and extract key clauses. Which model type is best suited for this task?

Accepted Answer

Cohere Summarize. Cohere Summarize is specifically designed to condense long documents into concise summaries, making it ideal for extracting key clauses from legal documents. Unlike other Cohere models, Summarize focuses on distilling the most important information from text, which aligns with the task of identifying and extracting critical clauses.

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Cohere Command (generate)

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Cohere Chat

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Cohere Embed

Question 11

A developer wants to generate text using the OCI Generative AI service via the API. Which endpoint should they use to send a text generation request?

Accepted Answer

/v1/completions. Option C is correct because the OCI Generative AI service uses the /v1/completions endpoint for text generation requests, as documented in the OCI Generative AI API reference. This endpoint accepts a prompt and generates a continuation of the text, making it the appropriate choice for general text generation tasks.

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/v1/chat/completions

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/v1/embeddings

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/v1/models

Question 12

A data scientist is using a large language model to summarize customer support tickets. The model occasionally generates summaries that include hallucinated details not present in the original ticket. Which technique would best reduce hallucinations while maintaining summary quality?

Accepted Answer

Implement retrieval-augmented generation (RAG) to ground the model in relevant documents.. Retrieval-Augmented Generation (RAG) reduces hallucinations by grounding the model's output in external, verifiable documents retrieved from a knowledge base. Instead of relying solely on the model's parametric memory, RAG fetches relevant context (e.g., the original ticket) at inference time, ensuring the summary is factually aligned with the source. This maintains summary quality because the model can still generate fluent text while being constrained to the retrieved evidence.

Answer

Use a longer system prompt instructing the model to be factual.

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Fine-tune the model on a large corpus of general text to improve its knowledge.

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Increase the temperature parameter to 0.9 to encourage more deterministic outputs.

Question 13

A research team is experimenting with few-shot prompting to improve a model's performance on a complex reasoning task. They find that the model's performance degrades when the few-shot examples are too similar to each other. What is the likely cause and best remedy?

Accepted Answer

The examples lack diversity, causing the model to overfit to a narrow pattern. Use more diverse examples.. When few-shot examples are too similar, the model overfits to a narrow pattern, reducing its ability to generalize to the diverse reasoning paths required by the task. This is a known limitation of in-context learning: the model treats the examples as a template rather than as diverse demonstrations. Using more diverse examples exposes the model to a wider range of reasoning patterns, improving robustness.

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The model has not seen enough examples. Increase the number of few-shot examples.

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The examples are presented in a confusing order. Reorder them by difficulty.

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The temperature is too low, making the model too deterministic. Increase temperature slightly.

Question 14

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.

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BLEU score

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Perplexity

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ROUGE score

Question 15

A company is deploying a large language model in a customer-facing chatbot. The model's responses must be both accurate and safe. Which combination of techniques should be employed?

Accepted Answer

Use retrieval-augmented generation (RAG) for factual accuracy and a content safety filter for safe outputs.. Option B is correct because RAG grounds the model's responses in a verified external knowledge base, reducing hallucinations and improving factual accuracy, while a content safety filter (e.g., a classifier or guardrail) actively blocks toxic or unsafe outputs before they reach the user. This combination addresses both accuracy and safety independently, unlike a single system prompt which is easily bypassed.

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Use only a system prompt instructing the model to be accurate and safe.

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Use a high temperature for creativity and a safety classifier for blocking toxic outputs.

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Fine-tune the model on all historical chat logs and use a high temperature.