1Z0-1127 Fundamentals of Large Language Models — Timed 30 Questions

Question 1

A company is deploying a large language model for a customer service chatbot. The model needs to understand industry-specific jargon and maintain low latency. Which approach best balances these requirements?

Accepted Answer

Fine-tune a small open-source LLM on domain-specific data. Fine-tuning a small open-source LLM on domain-specific data is the best approach because it adapts the model to understand industry-specific jargon while keeping the model small enough to maintain low latency. Unlike larger models, a fine-tuned small model can run efficiently on local hardware, reducing inference time and avoiding the overhead of external API calls or large model sizes.

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Employ retrieval-augmented generation (RAG) with a general model

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Rely solely on prompt engineering with a general model

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Use a large general-purpose LLM with zero-shot prompting

Question 2

A data scientist observes that their fine-tuned LLM performs well on training data but generates repetitive and dull responses in production. What is the most likely cause and best solution?

Accepted Answer

The temperature is set too low; increase temperature during inference. The model's repetitive and dull responses indicate that the temperature parameter is too low, causing the model to always select the most probable tokens, leading to deterministic and monotonous outputs. Increasing temperature during inference introduces randomness into token sampling, allowing for more diverse and creative responses. This is a common issue in production LLMs where low temperature settings optimized for training metrics fail to produce engaging real-world outputs.

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The model is overfitted; apply stronger regularization

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The training data lacks diversity; add more varied examples

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The model has too many layers; reduce model size

Question 3

An organization wants to use an LLM to summarize legal documents. Which consideration is most important for ensuring accurate summaries?

Accepted Answer

Fine-tune the model on a curated legal corpus. Legal documents require precise understanding, so fine-tuning on legal data is critical. Option B is wrong because larger models don't guarantee domain accuracy. Option C is wrong because pre-training from scratch is expensive and unnecessary. Option D is wrong because zero-shot may miss legal nuances.

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Use the largest available general-purpose model

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Rely on zero-shot summarization with careful prompting

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Pre-train a new model from scratch on legal texts

Question 4

A developer is building a code generation assistant. The model occasionally produces syntactically correct but semantically wrong code. Which technique directly addresses semantic correctness?

Accepted Answer

Apply RLHF using human-validated code examples. Reinforcement Learning from Human Feedback (RLHF) directly addresses semantic correctness by fine-tuning the model using human-validated code examples. This process teaches the model to prefer outputs that are not only syntactically valid but also logically correct and aligned with developer intent, reducing semantically wrong code generation.

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Expand the token vocabulary

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Lower the temperature to 0

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Increase beam search width

Question 5

A company fine-tunes an LLM on internal support tickets. After deployment, the model hallucinates company-specific product names. What is the most effective mitigation?

Accepted Answer

Implement RAG with a verified product database. RAG (Retrieval-Augmented Generation) grounds the LLM's output in a verified product database, providing factual context that prevents hallucination of company-specific product names. Unlike fine-tuning, which only adjusts model weights and can still produce plausible but incorrect names, RAG retrieves exact records at inference time, ensuring accuracy for proprietary terminology.

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Switch to a smaller model to reduce hallucination risk

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Use prompt engineering to remind the model to be accurate

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Fine-tune further with more ticket data

Question 6

A team wants to evaluate an LLM's performance on a text classification task. Which metric is most appropriate for a balanced dataset?

Accepted Answer

Accuracy. Accuracy is the most appropriate metric for evaluating an LLM on a text classification task with a balanced dataset because it directly measures the proportion of correctly predicted labels out of total predictions. For balanced classes, accuracy provides a reliable and intuitive performance indicator without the distortion caused by class imbalance.

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BLEU score

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Perplexity

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ROUGE score

Question 7

An LLM-based application must comply with data privacy regulations by not memorizing personally identifiable information (PII). Which technique best reduces memorization of PII?

Accepted Answer

Train with differential privacy. Differential privacy (DP) is the correct technique because it directly limits the model's ability to memorize training data, including PII, by adding calibrated noise to the gradient updates during training. This ensures that the model's parameters do not encode specific individual records, providing a formal mathematical guarantee against memorization. Other options like model size, temperature, or training epochs do not address the root cause of memorization in the training process.

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Use a larger model with more parameters

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Decrease the temperature during inference

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Increase the number of training epochs

Question 8

Which TWO factors most significantly influence the computational cost of fine-tuning a large language model?

Accepted Answer

Number of model parameters. Model size (parameters) directly determines FLOPs and memory. Training sequence length affects memory and compute per step. Option C is wrong because batch size affects throughput but not fundamental cost per token. Option D is wrong because quantization usually reduces cost. Option E is wrong because dataset size affects total steps but per-step cost is dominated by model size and sequence length.

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Batch size

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Quantization bits

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Dataset size

Question 9

Which TWO techniques are commonly used to reduce the memory footprint of LLM inference?

Accepted Answer

Quantization. Quantization reduces the memory footprint by lowering the precision of model weights and activations from FP32 to lower bit-widths like INT8 or FP16, which directly decreases the memory required to store and compute with the model. KV cache optimization reduces memory usage by efficiently managing the key-value cache during autoregressive decoding, often through techniques like shared memory, pruning, or compression, which is critical for long-context inference.

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Increasing batch size

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Gradient checkpointing

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Using full precision (FP32)

Question 10

Which THREE are essential steps in the prompt engineering process for an LLM?

Accepted Answer

Test the prompt with a variety of input examples. Option A is correct because testing the prompt with a variety of input examples is essential to evaluate the LLM's generalization, robustness, and sensitivity to different phrasing or contexts. This step helps identify edge cases, biases, or inconsistencies in the model's responses before deployment.

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Fine-tune the model on a domain corpus

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Quantize the model to INT8

Question 11

A data scientist is using a large language model to summarize customer support tickets. The model occasionally generates summaries that include hallucinated details not present in the original ticket. Which technique would best reduce hallucinations while maintaining summary quality?

Accepted Answer

Implement retrieval-augmented generation (RAG) to ground the model in relevant documents.. Retrieval-Augmented Generation (RAG) reduces hallucinations by grounding the model's output in external, verifiable documents retrieved from a knowledge base. Instead of relying solely on the model's parametric memory, RAG fetches relevant context (e.g., the original ticket) at inference time, ensuring the summary is factually aligned with the source. This maintains summary quality because the model can still generate fluent text while being constrained to the retrieved evidence.

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Use a longer system prompt instructing the model to be factual.

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Fine-tune the model on a large corpus of general text to improve its knowledge.

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Increase the temperature parameter to 0.9 to encourage more deterministic outputs.

Question 12

An enterprise is deploying a chat application using a large language model. Users report that the model sometimes generates toxic or biased responses. Which best practice should be applied to mitigate this issue?

Accepted Answer

Implement a content filtering layer using a safety classifier to detect and block toxic outputs.. Option D is correct because implementing a content filtering layer using a safety classifier is a proven best practice to detect and block toxic or biased outputs in real-time. This approach acts as a guardrail, intercepting harmful responses before they reach users, and is independent of the model's internal parameters or training data.

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Use few-shot prompting with examples of toxic responses so the model learns to avoid them.

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Increase the max_tokens parameter to allow the model more context to correct itself.

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Disable the temperature parameter to make outputs deterministic.

Question 13

A team is fine-tuning a large language model for a domain-specific Q&A application. After fine-tuning, they observe that the model performs well on the training distribution but struggles with out-of-distribution (OOD) questions. Which approach would best improve OOD robustness?

Accepted Answer

Include a diverse set of examples from related domains in the fine-tuning dataset.. Option C is correct because incorporating diverse data during fine-tuning helps the model generalize to OOD inputs. Option A is wrong because increasing learning rate may cause catastrophic forgetting. Option B is wrong because reducing model size reduces capacity. Option D is wrong because early stopping on training loss may not help OOD.

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Use early stopping based on training loss to avoid overfitting.

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Reduce the model size to prevent overfitting to the training data.

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Increase the learning rate during fine-tuning to adapt faster to new patterns.

Question 14

A developer is using a large language model to generate code snippets. The model often produces code that is syntactically correct but functionally incorrect. What is the most effective way to improve the functional correctness of the generated code?

Accepted Answer

Provide few-shot examples of correct code in the prompt.. Option A is correct because providing few-shot examples of correct code in the prompt directly demonstrates the desired functional behavior to the model. This technique, known as few-shot prompting, grounds the model's output in concrete examples, significantly improving the likelihood that the generated code will be functionally correct by aligning the model's pattern completion with the intended logic, not just syntax.

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Increase the temperature parameter to generate more creative solutions.

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Ask the model to only output syntactically valid code.

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Set max_tokens to a very high value to allow the model more room to think.

Question 15

A company is deploying a large language model in a customer-facing chatbot. The model's responses must be both accurate and safe. Which combination of techniques should be employed?

Accepted Answer

Use retrieval-augmented generation (RAG) for factual accuracy and a content safety filter for safe outputs.. Option B is correct because RAG grounds the model's responses in a verified external knowledge base, reducing hallucinations and improving factual accuracy, while a content safety filter (e.g., a classifier or guardrail) actively blocks toxic or unsafe outputs before they reach the user. This combination addresses both accuracy and safety independently, unlike a single system prompt which is easily bypassed.

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Use only a system prompt instructing the model to be accurate and safe.

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Use a high temperature for creativity and a safety classifier for blocking toxic outputs.

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Fine-tune the model on all historical chat logs and use a high temperature.

Question 16

A research team is experimenting with few-shot prompting to improve a model's performance on a complex reasoning task. They find that the model's performance degrades when the few-shot examples are too similar to each other. What is the likely cause and best remedy?

Accepted Answer

The examples lack diversity, causing the model to overfit to a narrow pattern. Use more diverse examples.. When few-shot examples are too similar, the model overfits to a narrow pattern, reducing its ability to generalize to the diverse reasoning paths required by the task. This is a known limitation of in-context learning: the model treats the examples as a template rather than as diverse demonstrations. Using more diverse examples exposes the model to a wider range of reasoning patterns, improving robustness.

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The model has not seen enough examples. Increase the number of few-shot examples.

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The examples are presented in a confusing order. Reorder them by difficulty.

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The temperature is too low, making the model too deterministic. Increase temperature slightly.

Question 17

Which TWO of the following are common applications of large language models in enterprise settings?

Accepted Answer

Summarizing lengthy legal documents.. Option A is correct because large language models (LLMs) excel at abstractive summarization, which involves condensing lengthy legal documents into concise summaries while preserving key facts and legal reasoning. This is a common enterprise application for legal departments, as LLMs can process large volumes of text and generate coherent, context-aware summaries without requiring manual reading.

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Performing real-time signal processing for audio streams.

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Replacing relational databases for data storage.

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Enhancing low-resolution images through super-resolution.

Question 18

Which THREE of the following are known limitations of large language models that practitioners must consider?

Accepted Answer

Hallucination of facts not present in the input.. Option A is correct because large language models (LLMs) are prone to hallucination, where they generate plausible-sounding but factually incorrect information that was not present in the input. This occurs because LLMs are next-token predictors without a built-in fact-checking mechanism, and they can invent details, citations, or events to maintain coherence. Practitioners must implement retrieval-augmented generation (RAG) or external verification to mitigate this risk.

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Limited to processing only one language at a time.

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Inability to process inputs longer than a few hundred tokens.

Question 19

Refer to the exhibit. A developer ran the OCI CLI command shown and received the JSON output. What does the output indicate about the model's confidence and why?

Accepted Answer

The model is highly confident the text is positive, as indicated by the 0.98 score.. Option D is correct because the JSON output shows a sentiment score of 0.98 for 'positive', which is very close to 1.0, indicating the model is highly confident that the text is positive. In sentiment analysis models, scores represent probabilities for each class, and a value near 1.0 for one class with much lower scores for others reflects strong confidence.

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The model is uncertain because all scores are roughly equal.

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The model is neutral because the neutral score is lowest.

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The model is unsure because the scores are probabilities that sum to 1.

Question 20

Refer to the exhibit. A developer runs the OCI CLI command and receives the output. However, the text "Hello, how are you?" is actually a mix of English and French words. Why does the model assign only 0.03 to French?

Accepted Answer

The text is overwhelmingly English, so the model assigns a low probability to French.. Option A is correct because the model's output shows a probability distribution over languages, and the text is predominantly English with only a few French words. The model assigns a low probability (0.03) to French because the overwhelming majority of tokens are English, making the text far more likely to be classified as English. This reflects how language identification models evaluate the overall composition of the input.

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The model is limited to identifying a single language per query.

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The model cannot detect multiple languages in a single text.

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The model's scores are normalized to sum to 1, so a high English score forces low others.