1Z0-1127-25 Fundamentals of Large Language Models Practice Test 6 — 15 Questions

Question 1

An AI specialist is troubleshooting why a fine-tuned model produces inconsistent results across different inference calls. What is the most likely cause?

Accepted Answer

The temperature is set too high. Temperature controls the randomness of token sampling during inference. A high temperature (e.g., >1.0) increases the probability of selecting less likely tokens, causing the model to produce varied outputs for the same input across different calls. This is the most direct cause of inconsistent results when the base model and fine-tuning are otherwise sound.

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The base model is not suitable

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The model is overfitted

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The fine-tuning dataset is too small

Question 2

A company uses RAG (Retrieval-Augmented Generation) with OCI OpenSearch and OCI Generative AI. The system retrieves irrelevant documents. What is the first step to debug?

Accepted Answer

Check the embeddings quality. When RAG retrieves irrelevant documents, the most common root cause is poor embedding quality—vectors that fail to capture semantic similarity between the query and the documents. Checking embeddings (e.g., cosine similarity scores, dimensionality, or model used) is the logical first step before adjusting retrieval parameters or the LLM itself.

Answer

Use a different LLM

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Increase the number of retrieved documents

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Lower the temperature

Question 3

Which TWO of the following are benefits of using OCI Generative AI service compared to self-hosting an LLM?

Accepted Answer

Built-in content safety filters. OCI Generative AI service includes built-in content safety filters that automatically detect and block harmful or inappropriate content (e.g., hate speech, violence, sexual content) without requiring manual configuration. This is a key advantage over self-hosting, where you must implement and maintain your own content moderation pipeline.

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Lower latency always

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No data egress costs

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Full control over model weights

Question 4

Which THREE factors should be considered when choosing between a fine-tuning and a prompt engineering approach?

Accepted Answer

Need for model personalization. Model personalization is a key driver for choosing fine-tuning over prompt engineering. Fine-tuning modifies the model's weights to adapt it to a specific domain or task, enabling deeper customization that prompt engineering alone cannot achieve, especially when the desired behavior requires learning new patterns or knowledge not present in the base model.

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Latency requirements

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Availability of foundation model in OCI

Question 5

Which TWO techniques can help reduce bias in LLM outputs?

Accepted Answer

Using diverse training data. Using diverse training data helps the model learn from a wide range of perspectives, reducing the risk of over-representing any single group or viewpoint. This directly mitigates bias by ensuring the training distribution is more representative of the real world, rather than skewed toward a dominant demographic or cultural norm.

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Setting temperature to 0

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Using only English data

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Increasing model size

Question 6

Refer to the exhibit. What is the primary reason the response is incomplete?

Accepted Answer

The max-tokens limit is too low.. The response is incomplete because the max-tokens limit is too low, causing the model to truncate its output before completing the full answer. When the token budget is exhausted, the generation stops mid-sentence or mid-thought, leaving the response unfinished regardless of prompt length or other parameters.

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The temperature is not set.

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The model-id is incorrect.

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The prompt is too short.

Question 7

Refer to the exhibit. What is the solution?

Accepted Answer

Use a different base model that supports fine-tuning.. The exhibit indicates that the base model does not support fine-tuning, which is a prerequisite for adapting a large language model to a specific task or domain. Using a different base model that supports fine-tuning allows the model to be customized through supervised learning on task-specific data, enabling it to learn new patterns and improve performance. This is the correct solution because without fine-tuning capability, the model cannot be effectively adapted regardless of other hyperparameter adjustments.

Answer

Change the learning rate.

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Increase the training epochs.

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Use a different compartment.

Question 8

Refer to the exhibit. A user in GenAI-Users group tries to run a text generation inference but gets permission denied. What is the most likely issue?

Accepted Answer

The operation condition is too restrictive.. The policy attached to the GenAI-Users group includes a condition that restricts the operation to a specific compartment or resource, but the user is attempting to run inference in a different compartment or without meeting the condition. Since the condition is too restrictive, the IAM policy denies the action even though the user is in the correct group and the resource type is valid.

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The policy resource type is wrong.

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The group name mismatch.

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The user is not in the compartment.

Question 9

A company wants to use OCI Generative AI to summarize customer reviews. Which model parameter should be adjusted to control the creativity of the summary?

Accepted Answer

Temperature. Temperature controls the randomness of token selection in the model's output distribution. A higher temperature (e.g., 0.9) makes the summary more creative and diverse, while a lower temperature (e.g., 0.1) makes it more deterministic and focused. For summarizing customer reviews, adjusting temperature directly influences how novel or conservative the generated text will be.

Answer

Frequency penalty

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Top-k

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Presence penalty

Question 10

An engineer sets beam search width to 1 during inference on OCI Generative AI. What is the most likely effect on output?

Accepted Answer

Faster inference. Beam search width of 1 is equivalent to greedy decoding, where only the single most probable token is selected at each step. This eliminates the need to maintain and compare multiple candidate sequences, significantly reducing computational overhead and memory access, which directly speeds up inference.

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More memory usage

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More diverse outputs

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Better quality

Question 11

A model generates code with security issues. Which approach is best to mitigate this?

Accepted Answer

Add a system prompt with security guidelines. Adding a system prompt with security guidelines (option D) instructs the model to follow best practices, directly addressing security concerns without changing model training.

Answer

Reduce max_tokens

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Increase temperature

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Use a different model

Question 12

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 13

What is the role of the softmax function in the output layer of an LLM?

Accepted Answer

Convert logits to probabilities. The softmax function in the output layer of an LLM converts the raw, unnormalized scores (logits) produced by the final linear layer into a probability distribution over the vocabulary. This allows the model to output a valid probability for each token, where all probabilities sum to 1, enabling sampling or greedy decoding for next-token prediction.

Answer

Apply attention

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Tokenize input

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Compute gradients

Question 14

A developer uses OCI Generative AI's chat endpoint with a system message placed after user messages. The model ignores the system message. What is the most likely reason?

Accepted Answer

The system message is placed after user messages. In OCI Generative AI's chat endpoint, the system message must be placed before user messages to establish the model's behavior and context. When placed after user messages, the model treats it as part of the conversation history rather than a directive, causing it to be ignored. This ordering is a fundamental requirement for the chat API's message structure.

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The system message is too long

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Temperature is set too high

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The model has not been fine-tuned for instruction following

Question 15

An OCI GenAI model generates English to French translation. Which metric is most appropriate to evaluate its quality?

Accepted Answer

BLEU. BLEU (Bilingual Evaluation Understudy) is the standard metric for machine translation tasks because it measures the n-gram overlap between the generated translation and one or more reference translations, directly assessing fluency and adequacy. For English-to-French translation, BLEU correlates well with human judgment of translation quality, making it the most appropriate choice.

Answer

Perplexity

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ROUGE

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F1 score