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

Question 1

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 2

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.

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Perplexity

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ROUGE

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F1 score

Question 3

Which TWO factors are most likely to cause hallucinations in LLMs?

Accepted Answer

High temperature. A high temperature setting increases the randomness of token sampling, making the model more likely to generate plausible-sounding but factually incorrect or nonsensical outputs. This directly contributes to hallucinations by encouraging the model to deviate from the most probable, grounded responses.

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Short context window

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Excessive fine-tuning

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Low top-p

Question 4

Which THREE techniques are commonly used to improve the quality of text generation?

Accepted Answer

Temperature scaling. Temperature scaling is correct because it controls the randomness of token probability distributions by dividing logits before softmax; lower temperatures (e.g., 0.1) make the model more deterministic, while higher temperatures (e.g., 1.5) increase diversity. This directly influences the quality of generated text by balancing coherence and creativity.

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Greedy decoding

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Random sampling

Question 5

Which TWO are advantages of using LoRA for fine-tuning?

Accepted Answer

Requires less GPU memory. LoRA (Low-Rank Adaptation) reduces GPU memory requirements because it freezes the original model weights and injects trainable low-rank matrices into specific layers. This means only a tiny fraction of parameters need gradients and optimizer states, drastically lowering memory consumption during fine-tuning compared to full fine-tuning.

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Guarantees higher accuracy

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Increases model size

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Improves inference speed

Question 6

Refer to the exhibit. A user in group GenAIGroup cannot see models in the Production compartment using OCI Generative AI. What is the most likely issue?

Accepted Answer

Resource type should be 'generative-ai-model'. The resource type in the policy is specified as 'oci-generativeai:model' but the correct resource type for OCI Generative AI models is 'generative-ai-model'. The policy syntax is invalid, so access is denied. Option B is correct.

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Statement should be 'use' instead of 'read'

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Missing 'inspect' permission

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Policy syntax incorrect (missing quotes)

Question 7

Refer to the exhibit. The output is very short and cuts off mid-sentence. Which parameter is most likely the cause?

Accepted Answer

Max-tokens is too low. The 'max-tokens' parameter limits the number of tokens in the generated response. Setting it to 500, while typically sufficient, might still cause truncation if the model's context window is nearly full or if the prompt is long. However, among options, 'max-tokens' is the direct control for output length. Option C is correct.

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Temperature is too high

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Model ID incorrect

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Top-p is too high

Question 8

Refer to the exhibit. A user deployed a custom model via OCI Data Science and registered it in the Model Catalog. They use the correct OCID but get this error. What is the most likely issue?

Accepted Answer

The model is not deployed to an endpoint. A model must be deployed to an endpoint to be accessible via the inference API. Registration in the Model Catalog alone does not create an endpoint. Option A is correct.

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The model is not fine-tuned

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The compartment OCID is missing

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The model is in a different region

Question 9

A data scientist is using OCI Data Science to fine-tune a Cohere command model on domain-specific documents. They observe that the fine-tuned model generates repetitive text. What is the most likely cause?

Accepted Answer

The training dataset lacked diversity.. Repetitive text in fine-tuned models is a classic symptom of overfitting to a narrow or homogeneous training dataset. When the domain-specific documents lack diversity in phrasing, topics, or contexts, the model learns to latch onto the most common patterns and repeats them, rather than generalizing. This is not a hyperparameter tuning issue but a data quality issue.

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The number of epochs was insufficient.

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The learning rate was too high.

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The batch size was too small.

Question 10

A company uses OCI Generative AI to power a chatbot for customer support. They notice that the model's responses sometimes contain factual inaccuracies. Which strategy would best reduce hallucination?

Accepted Answer

Implementing Retrieval-Augmented Generation (RAG).. Retrieval-Augmented Generation (RAG) grounds the model's responses in retrieved factual information, directly reducing hallucination. Increasing temperature increases randomness, fine-tuning on a larger corpus may not fix factual accuracy, and reducing max tokens does not affect correctness.

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Increasing the temperature parameter.

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Reducing the max token limit.

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Fine-tuning the model on a larger general corpus.

Question 11

An architect is designing a multi-tenant application using OCI Generative AI. Each tenant has custom instructions and data. To minimize cost while maintaining isolation, which deployment approach is recommended?

Accepted Answer

Shared base model with per-tenant system prompts and retrieval.. A shared base model with per-tenant customization via system prompts and retrieval (RAG) is cost-effective and provides isolation through prompt engineering and data segregation. Dedicated fine-tuned endpoints are expensive, a single large model with conditional logic risks prompt injection, and on-premises deployment may not be feasible or scalable.

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Dedicated fine-tuned endpoint per tenant.

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On-premises deployment of open-source models.

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Single large fine-tuned model with conditional logic.

Question 12

Which two factors are essential for calculating the cost of using OCI Generative AI for text generation? (Choose two.)

Accepted Answer

Number of input tokens.. The cost of using OCI Generative AI for text generation is primarily determined by the number of input tokens (the prompt you send) and the number of output tokens (the generated response). OCI charges per token processed, making these two factors essential for cost calculation.

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Model architecture (encoder-only vs decoder-only).

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Number of API calls per minute.

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Temperature setting.

Question 13

Which three techniques are commonly used to reduce the risk of prompt injection in LLM applications? (Choose three.)

Accepted Answer

Output filtering.. Output filtering (B) is correct because it acts as a post-processing defense that scans the LLM's generated output for malicious content, such as leaked system prompts or injected commands, before it reaches the user. This technique helps mitigate the impact of successful prompt injections by catching and neutralizing harmful outputs that bypass input controls.

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Enabling prompt validation against regex patterns.

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Increasing temperature.

Question 14

Which three statements about transformer architecture are correct? (Choose three.)

Accepted Answer

The softmax function is used in the attention mechanism to normalize attention scores.. Option A is correct because the softmax function is applied to the raw attention scores (the dot products between queries and keys) to convert them into a probability distribution that sums to 1. This normalization allows the model to assign a relative weight to each token in the sequence, ensuring that the weighted sum of values is stable and interpretable.

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The feed-forward network applies a different set of weights for each token position.

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The encoder-decoder structure is used in GPT models.

Question 15

An OCI AI Language text classification request returns the output shown. Which conclusion is most accurate?

Accepted Answer

The text is classified as Positive with high confidence.. The output shows a sentiment label of 'Positive' with a confidence score of 0.98, indicating the model is highly confident in its classification. Option B correctly identifies this as a positive sentiment with high confidence, which is the most accurate conclusion based on the provided data.

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The model is uncertain about the sentiment.

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The API endpoint is misconfigured.

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The --endpoint parameter is optional.