1Z0-1127 Fundamentals of Large Language Models Practice Test 4 — 15 Questions

Question 1

A team is fine-tuning an LLM on OCI Generative AI for a domain-specific task. They have a dataset of 10,000 labeled examples. What is a best practice to avoid catastrophic forgetting during fine-tuning?

Accepted Answer

Include a small percentage of general-domain data in the training mix.. Option D is correct because catastrophic forgetting occurs when a fine-tuned model loses previously learned general knowledge. By including a small percentage (e.g., 5–10%) of general-domain data in the training mix, the model retains its broad capabilities while adapting to the new domain-specific task. This technique, often called 'replay' or 'experience replay,' is a standard practice in continual learning for LLMs.

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Increase the learning rate to speed up adaptation.

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Use only the new domain-specific data for fine-tuning.

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Reduce the number of training epochs to the minimum.

Question 2

A company uses an LLM to generate product descriptions. The outputs are consistently too verbose and include irrelevant details. The prompt includes a simple instruction: 'Describe the product.' Which adjustment to the prompt is most likely to yield concise, relevant descriptions?

Accepted Answer

Add constraints like 'Max 30 words. Focus on key features.'. Option C is correct because adding explicit constraints like 'Max 30 words. Focus on key features.' directly instructs the LLM to limit verbosity and prioritize relevant details. This technique, known as prompt engineering with constraints, is the most effective way to control output length and content without altering model parameters or relying on examples that may not generalize.

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Set temperature to 0.

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Increase max_tokens to 500.

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Include a few examples of desired short descriptions.

Question 3

Which TWO factors most directly impact the consistency of text generated by an LLM when the same prompt is used multiple times?

Accepted Answer

Top_p. Top_p (nucleus sampling) directly impacts consistency by controlling the cumulative probability threshold for token selection. A lower Top_p (e.g., 0.1) restricts the model to only the most probable tokens, reducing randomness and making outputs more deterministic across repeated prompts. This parameter, along with Temperature, is a primary lever for managing output variability in LLMs.

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Batch size

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Max_tokens

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Seed

Question 4

Which THREE are known challenges when deploying large language models in production?

Accepted Answer

Bias in training data perpetuating stereotypes. Option A is correct because large language models (LLMs) are trained on vast, unfiltered internet text corpora that inherently contain societal biases. These biases are learned and can be amplified during inference, leading to outputs that perpetuate harmful stereotypes, which is a well-documented production challenge.

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Fast inference speed due to parallelization

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Low memory footprint

Question 5

Which TWO are advantages of using retrieval-augmented generation (RAG) over fine-tuning for incorporating new knowledge?

Accepted Answer

Enables the model to access up-to-date information without retraining. Option B is correct because RAG retrieves relevant, up-to-date information from an external knowledge base at inference time, allowing the model to answer questions about recent events or proprietary data without requiring any retraining. This is a key advantage over fine-tuning, which would need a new training cycle to incorporate the same new knowledge.

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Better at capturing domain-specific writing style

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Eliminates the need for a vector database

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Reduces token usage and latency compared to fine-tuning

Question 6

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

Accepted Answer

cohere.command. Option A is correct because cohere.command has the 'chat' capability explicitly listed. Options B and C only have text-generation or embeddings. Option D (embed) is not for generation.

Answer

cohere.embed

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A model with embeddings capability

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cohere.base

Question 7

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.. Option C is correct. The policy only allows creating inference requests but not to 'inspect' or 'use' inference. The developer needs the 'use' permission on the inference resource to actually call it. Option A is wrong because model-family is allowed. Option B is wrong because the policy allows operations. Option D is wrong because the compartment is correct.

Answer

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

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The policy does not allow any operations in the compartment.

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The compartment name in the policy is incorrect.

Question 8

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.. Option B is correct because the total (6000 + 4000 = 10000) exceeds 8192. Reducing max_new_tokens or prompt length lowers the total. Option A (increase max_new_tokens) worsens it. Option C (change region) is irrelevant. Option D (ignore and retry) will fail again.

Answer

Increase max_new_tokens to 5000 to get a longer response.

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Ignore the error and retry the request.

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Switch to a model in a different region.

Question 9

A company uses OCI Generative AI service to power a chatbot. After deployment, the chatbot starts generating inappropriate responses. Which action should be taken first?

Accepted Answer

Adjust the prompt template to include safety instructions.. Option D is correct because adjusting the prompt template to include safety instructions is the fastest and most direct way to mitigate inappropriate responses without retraining or changing model parameters. In OCI Generative AI, prompt engineering—including explicit safety guidelines—can immediately constrain the model's output behavior by providing clear guardrails in the context window.

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Increase the temperature parameter.

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Fine-tune the model on customer-specific data.

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Switch to a larger model.

Question 10

A data scientist wants to improve the accuracy of a summarization model on medical texts. Which OCI service feature is most suitable?

Accepted Answer

OCI Generative AI fine-tuning. C is correct because OCI Generative AI fine-tuning allows a data scientist to adapt a pre-trained large language model (LLM) specifically for medical text summarization by training it on domain-specific data. This improves accuracy by aligning the model's outputs with the terminology, context, and nuances of medical literature, which generic models may not capture well.

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OCI Data Flow

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OCI Language service

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OCI Anomaly Detection

Question 11

An architect needs to ensure that an LLM deployed in OCI does not reveal sensitive information in its outputs. Which technique should be used?

Accepted Answer

Output filtering via custom inference wrapper. Option C is correct because output filtering via a custom inference wrapper allows the architect to inspect and sanitize the model's generated text before it reaches the user, preventing the leakage of sensitive information such as PII, credentials, or internal data. This technique operates at the application layer, intercepting the LLM's response and applying rules or regex patterns to redact or block prohibited content, which is essential for compliance and data security in production deployments.

Answer

Limiting max tokens

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OCI Data Safe masking

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Input sanitization

Question 12

A developer notices that an LLM's responses are too verbose. Which parameter adjustment would most effectively reduce verbosity?

Accepted Answer

Decrease max_tokens. Decreasing max_tokens directly limits the maximum length of the LLM's response, which is the most straightforward way to reduce verbosity. This parameter caps the number of tokens the model can generate, forcing it to produce shorter completions. Other parameters like frequency_penalty, top_p, and temperature influence the style, diversity, or randomness of the output but do not directly control response length.

Answer

Increase frequency_penalty

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Increase top_p

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Decrease temperature

Question 13

A team wants to deploy an LLM for real-time inference with low latency. Which OCI deployment option is best?

Accepted Answer

OCI Data Science Model Deployment with GPU shapes. OCI Data Science Model Deployment with GPU shapes is the best option because it provides managed, scalable, low-latency inference endpoints for LLMs. GPU shapes (e.g., VM.GPU.A10) are essential for the parallel matrix computations required by transformer-based models, and the deployment service supports auto-scaling and load balancing to maintain real-time response times.

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OCI Functions with CPU

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OCI Events

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OCI Streaming

Question 14

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 15

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. Option A is correct because poor quality embeddings often cause retrieval of irrelevant documents. Checking and improving embeddings (e.g., using a better model or fine-tuning) should be the first step. Option B (increasing retrieved documents) may include more noise. Option C (different LLM) does not address retrieval. Option D (lower temperature) affects generation but not retrieval.

Answer

Use a different LLM

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Increase the number of retrieved documents

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Lower the temperature