1Z0-1127-25 Fundamentals of Large Language Models — 10 Questions

Question 1

Which TWO statements about tokens in large language models are correct?

Accepted Answer

Common tokenization methods include word-based and subword-based.. Common tokenization methods in large language models include word-based tokenization (splitting text into whole words) and subword-based tokenization (like Byte-Pair Encoding or WordPiece), which handle out-of-vocabulary words and morphological variations more effectively. Subword tokenization is widely used in models like GPT and BERT to balance vocabulary size and coverage.

Answer

All tokens have the same embedding size.

Answer

Tokens are only used during training.

Answer

Tokens are always whole words.

Question 2

A company wants to build a retrieval-augmented generation (RAG) system using OCI Generative AI and a vector database. Which model type should they use to convert documents into vector embeddings?

Accepted Answer

Embedding model (e.g., cohere.embed). In a RAG system, the retrieval step requires converting documents into dense vector representations that capture semantic meaning. An embedding model like Cohere Embed is specifically designed for this task, producing fixed-length vectors that can be indexed and queried in a vector database for similarity search.

Answer

Instruct model (e.g., cohere.command)

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Image generation model

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

Question 3

A developer is reviewing the model card for an LLM on OCI Generative AI and notices it was trained on a dataset that is predominantly English. The application will serve users in multiple languages. What is the most likely limitation of using this model without additional steps?

Accepted Answer

The model may produce lower quality responses in non-English languages.. B is correct because a model trained predominantly on English data will have learned linguistic patterns, vocabulary, and cultural contexts specific to English. When applied to non-English languages, the model's internal representations and generation capabilities are less optimized, leading to lower quality responses such as grammatical errors, unnatural phrasing, or misinterpretation of nuances.

Answer

The embedding vectors will be less accurate for any language.

Answer

The model will hallucinate facts more frequently.

Answer

The context window size will be effectively reduced.

Question 4

Based on the exhibit, which model is best suited for a conversational chatbot that needs to handle multi-turn dialogues?

Accepted Answer

cohere.command. D is correct because cohere.command is specifically designed for text generation and conversational tasks, including multi-turn dialogues. It is optimized to maintain context across multiple exchanges, making it the best choice among the listed models for a chatbot that needs to handle ongoing conversations.

Answer

cohere.embed

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A model with embeddings capability

Answer

cohere.base

Question 5

A developer in the GenAIDevelopers group tries to call the OCI Generative AI inference API but receives an unauthorized error. Which statement best explains the issue?

Accepted Answer

The developer lacks the required permission to 'use generative-ai-inference' to invoke the model.. The OCI Generative AI inference API requires the specific 'use generative-ai-inference' permission in an IAM policy. Without this permission, any API call to invoke a model will result in an unauthorized error, regardless of other permissions the developer might have. The error indicates the developer's group policy does not grant the necessary verb for the inference resource.

Answer

The developer does not have permission to use the generative-ai-model-family.

Answer

The policy does not allow any operations in the compartment.

Answer

The compartment name in the policy is incorrect.

Question 6

Based on the exhibit, what is the primary action the developer must take to successfully make the inference request?

Accepted Answer

Reduce max_new_tokens to 2000 to stay within the context length.. The error indicates that the combined length of the input prompt and the requested max_new_tokens exceeds the model's context length limit (e.g., 4096 tokens). Reducing max_new_tokens to 2000 ensures the total token count stays within the allowed context window, allowing the inference request to succeed. This is a fundamental constraint in transformer-based LLMs where the model cannot generate beyond its fixed context size.

Answer

Increase max_new_tokens to 5000 to get a longer response.

Answer

Ignore the error and retry the request.

Answer

Switch to a model in a different region.

Question 7

An OCI administrator wants to limit which users can invoke a specific LLM endpoint. Which resource type should be used?

Accepted Answer

IAM policies. IAM policies are the correct resource type because they define authorization rules that control which users, groups, or service principals can invoke specific OCI APIs, including the LLM inference endpoint. By writing a policy with a verb like 'inspect' or 'use' on the resource type 'ai-service-language-family' or a specific model family, the administrator can grant or deny access to the LLM endpoint. This is the standard OCI mechanism for fine-grained access control to AI services.

Answer

OCI Audit

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OCI Vault

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Network security groups

Question 8

A developer is using the OCI Generative AI Python SDK. They receive a 400 error 'InvalidParameter'. What is the most likely reason?

Accepted Answer

Invalid model name. The 400 'InvalidParameter' error in the OCI Generative AI Python SDK specifically indicates that the request contained a parameter that is not recognized or is malformed. The most common cause is providing an incorrect or unsupported model name, as the SDK validates model names against a predefined list of available models in the OCI Generative AI service. Other parameter errors, such as invalid temperature or top_p values, would also produce this error, but model name mismatches are the most frequent trigger.

Answer

Exceeded token limit

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Network timeout

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Missing API key

Question 9

A machine learning engineer is fine-tuning a model on OCI Data Science and notices that the training loss decreases but then suddenly increases. What is the most likely cause?

Accepted Answer

Reduce model size. The sudden increase in training loss after a period of decrease is often due to the model being too large for the dataset. An oversized model can create a complex loss landscape with erratic gradients, leading to divergence. Reducing the model size simplifies the architecture, resulting in more stable convergence and preventing the loss from spiking.

Answer

Add dropout

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Increase batch size

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Increase learning rate

Question 10

Which technique allows an LLM to be adapted to a new task with only a few examples?

Accepted Answer

Few-shot learning. Few-shot learning (A) is the correct technique because it enables an LLM to adapt to a new task by providing a small number of input-output examples directly in the prompt, without updating the model's weights. This leverages the model's in-context learning ability, where it generalizes from the provided examples to perform the task. It is distinct from fine-tuning, which requires retraining on a labeled dataset.

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Fine-tuning

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Pre-training

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Prompt engineering