1Z0-1127-25 Fundamentals of Large Language Models Practice Test 5 — 15 Questions

Question 1

A startup needs to deploy a large language model for a customer support chatbot that requires low latency and cost efficiency. They are evaluating OCI Generative AI models. Which model type is most appropriate?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Instruct model (e.g., cohere.command). The startup requires low latency and cost efficiency for a customer support chatbot. Instruct models like cohere.command are specifically fine-tuned to follow conversational instructions and generate concise, task-oriented responses, making them ideal for interactive chatbot applications. They balance performance and cost better than base models, which lack instruction-following capability, and embedding models, which are designed for semantic search rather than text generation.

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Embedding model (e.g., cohere.embed)

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Image generation model

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

Question 2

An AI engineer is testing a large language model on OCI Generative AI and receives this error: 'Token limit exceeded. Maximum context length is 4096 tokens.' The prompt is 4000 tokens long. What is the most effective way to resolve the issue without losing important context?

Accepted Answer

Reduce the prompt length by summarizing or trimming less relevant information.. The error indicates that the combined prompt and generated output exceed the model's maximum context length of 4096 tokens. Since the prompt alone is 4000 tokens, there is very little room for the model to generate a response. Trimming or summarizing less relevant parts of the prompt directly reduces the token count, allowing the model to produce a complete output without exceeding the limit. This approach preserves the most critical context while staying within the model's constraints.

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Switch to a model with a larger context window, if available.

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Increase the max_tokens parameter in the API call.

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Split the prompt into multiple requests and combine outputs.

Question 3

A data scientist is designing a prompt to extract structured information (e.g., JSON) from text using an instruct model on OCI Generative AI. The model sometimes outputs additional text beyond the JSON, breaking parsing. Which prompt engineering technique is most effective to enforce structured output?

Accepted Answer

Include a few-shot example of the expected JSON output in the prompt.. Few-shot prompting provides explicit examples of the desired output format, which instructs the model to follow the exact JSON structure and reduces the likelihood of extraneous text. This technique leverages the model's in-context learning ability to adhere to formatting constraints, making it the most effective for enforcing structured output in OCI Generative AI instruct models.

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Use a base model instead of an instruct model.

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Set the temperature to 0.0 to reduce randomness.

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Increase max_tokens to allow for additional output.

Question 4

An architect is optimizing an LLM application that processes long documents. The model has a 4096 token limit, but the documents are often 8000 tokens. They are using a chunking strategy. However, model responses sometimes miss key information that spans across chunks. Which technique most directly addresses this issue?

Accepted Answer

Use overlapping chunks to maintain context continuity.. Overlapping chunks ensure that tokens at the boundaries of one chunk are also present at the start of the next, preserving context continuity. This prevents the model from losing information that spans across chunk boundaries, which is a common issue when processing documents longer than the model's 4096-token context window.

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Randomly select parts of the document to include.

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Increase the max_tokens parameter for longer outputs.

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Use a model with a larger context window.

Question 5

A researcher wants to compare the performance of two LLMs on OCI Generative AI: a base model and an instruct model. They notice the instruct model often refuses to generate certain types of content. Which factor most likely explains this behavior?

Accepted Answer

The instruct model has been fine-tuned with reinforcement learning from human feedback (RLHF) to align with safety guidelines.. Instruct models are typically fine-tuned using reinforcement learning from human feedback (RLHF) to align with safety guidelines and ethical constraints. This fine-tuning process teaches the model to refuse generating harmful, biased, or unsafe content, which explains why the instruct model refuses certain types of content while the base model does not.

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The base model was programmed to follow stricter rules.

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The instruct model was trained on a smaller dataset.

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The base model rejects content more often.

Question 6

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.. 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 7

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.'. 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 8

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 9

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

Accepted Answer

Bias in training data perpetuating stereotypes. 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 10

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. 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 11

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.. 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 12

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 13

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. 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.

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Limiting max tokens

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OCI Data Safe masking

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Input sanitization

Question 14

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.

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Increase frequency_penalty

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Increase top_p

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Decrease temperature

Question 15

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