1Z0-1127-25 Fundamentals of Large Language Models Practice Test 4 — 15 Questions

Question 1

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.. 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.

Answer

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.

Answer

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

Question 2

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 3

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 excel at natural language understanding and generation tasks such as abstractive summarization, which is widely used in enterprise legal, compliance, and contract-review workflows to condense lengthy documents. Option C is correct because LLMs are commonly applied in developer tooling (e.g., GitHub Copilot, Amazon CodeWhisperer) to translate natural language prompts into boilerplate code, unit tests, and scaffolding. Option B is not a typical LLM application: real-time audio signal processing relies on DSP algorithms and streaming frameworks (e.g., FFT, WebRTC), not transformer text models. Option D is incorrect because relational databases provide ACID transactions, schemas, and indexing that LLMs cannot replace; LLMs may assist with query generation but not storage. Option E is incorrect because image super-resolution is a computer-vision task handled by convolutional or diffusion models (e.g., SRCNN, ESRGAN), not by large language models.

Answer

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 4

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 are probabilistic next-token predictors and can generate fluent but fabricated content (hallucinations) that is not grounded in the provided input or any verified source. Option B is correct because models trained on unfiltered web-scale corpora can reproduce toxic, hateful, or harmful language, which is why content moderation and safety alignment are required in production systems. Option D is correct because LLMs learn statistical patterns from their training data, so societal biases present in that data can be amplified or reproduced in outputs, requiring bias evaluation and mitigation. Option C is incorrect because modern LLMs are typically multilingual and can process and generate text in many languages, sometimes even within a single prompt. Option E is incorrect because although context windows are finite, current models support thousands to hundreds of thousands of tokens, so the claim that they cannot process more than a few hundred tokens is technically inaccurate.

Answer

Limited to processing only one language at a time.

Answer

Inability to process inputs longer than a few hundred tokens.

Question 5

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.. 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.

Answer

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.

Question 6

You are a machine learning engineer at a large e-commerce company. You have been tasked with deploying a large language model to power a customer service chatbot that handles product returns and refunds. The model will answer customer queries based on a knowledge base of return policies and FAQs. The company has strict requirements: (1) responses must be factually accurate and grounded in the knowledge base, (2) the system must be cost-effective, and (3) latency should be under 2 seconds per response. You decide to use a pre-trained LLM from OCI Data Science and implement retrieval-augmented generation (RAG). You have two options for the retriever: a dense embedding-based retriever (e.g., using OCI AI Language embeddings) or a sparse keyword-based retriever (e.g., BM25). You also need to decide on the generation model size: a 7B parameter model or a 70B parameter model. You run a pilot test: with the dense retriever + 7B model, average latency is 1.8 seconds and accuracy is 85%. With the sparse retriever + 7B model, latency is 1.2 seconds but accuracy drops to 75%. With the 70B model (any retriever), latency exceeds 5 seconds. Which combination should you choose to meet all requirements?

Accepted Answer

Dense retriever + 7B model.. (dense retriever + 7B model) is correct because it meets all three requirements: factual accuracy (85% accuracy from dense retrieval grounding), latency under 2 seconds (1.8 seconds), and cost-effectiveness (7B model is cheaper to run than 70B). The dense retriever provides better semantic matching for nuanced return policy queries, while the 7B model keeps inference fast and affordable.

Answer

Sparse retriever + 70B model.

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Dense retriever + 70B model.

Answer

Sparse retriever + 7B model.

Question 7

A healthcare startup is building a chatbot to answer patient inquiries using a large language model (LLM) deployed on OCI Data Science AI Quick Actions. The chatbot must comply with HIPAA regulations, so all patient data must remain within the OCI tenancy and never be sent to third-party APIs. The team has fine-tuned a Llama 2 7B model on de-identified medical records using OCI Data Science notebooks. The model is deployed as a managed endpoint via AI Quick Actions. Early testing shows that the chatbot sometimes generates responses containing specific patient names or dates of birth that were present in the fine-tuning dataset. Moreover, the model occasionally hallucinates medication dosages that are not medically accurate. Which course of action should the team take to address both issues while maintaining HIPAA compliance?

Accepted Answer

Re-fine-tune the model using differential privacy to limit memorization of training data, and implement retrieval-augmented generation (RAG) with a curated medical knowledge base to ground medication-related responses.. It addresses both memorization of PII and hallucination of medication dosages while maintaining HIPAA compliance. Differential privacy during fine-tuning limits the model's ability to memorize specific patient data, and retrieval-augmented generation (RAG) grounds responses in a curated medical knowledge base, reducing hallucinations without sending data outside the OCI tenancy.

Answer

Deploy a rule-based post-processing script that checks each response against a list of known patient names and medication dosages, and rejects any response containing them.

Answer

Switch to a larger model (e.g., Llama 2 70B) to improve accuracy and reduce hallucinations, and apply output filtering to remove any detected PII from responses.

Answer

Increase the fine-tuning dataset size with more varied de-identified records to reduce overfitting, and apply a temperature setting of 0 to make outputs deterministic.

Question 8

A developer is using the OCI Generative AI API to generate text. The responses are often too short and incomplete. Which parameter adjustment is most likely to produce longer, more complete responses?

Accepted Answer

Increase the max_tokens parameter.. The max_tokens parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. By increasing max_tokens, the model is allowed to produce longer sequences, which directly addresses the issue of responses being too short and incomplete. In the OCI Generative AI API, this is the primary parameter for capping output length.

Answer

Decrease the max_tokens parameter.

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Increase the top_p parameter.

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Decrease the frequency_penalty parameter.

Question 9

During fine-tuning of a large language model on OCI, you notice that the model's performance on the validation set is not improving after several epochs, but the training loss continues to decrease. What is the most likely cause?

Accepted Answer

The model is overfitting to the training data.. When training loss continues to decrease while validation performance stalls or worsens, the model is overfitting to the training data: it is memorizing training examples rather than learning generalizable patterns. This divergence between training and validation metrics is the classic signature of overfitting. The most likely cause is overfitting, and remedies include regularization, dropout, early stopping, or more data.

Answer

The learning rate is too high.

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The validation set is not representative.

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The training data is too small.

Question 10

A user wants to use OCI Generative AI to generate marketing copy. They want the output to be more creative and varied. Which parameter should they adjust?

Accepted Answer

Increase the temperature parameter.. Increasing the temperature parameter makes the model's output more random and diverse, which is ideal for creative tasks like generating marketing copy. A higher temperature (e.g., 0.7–1.0) increases the probability of sampling less likely tokens, leading to more varied and imaginative text. Setting temperature to 0 would make the output deterministic and repetitive, which is the opposite of what the user wants.

Answer

Set temperature to 0.

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Decrease the temperature parameter.

Answer

Increase the max_tokens parameter.

Question 11

An organization is concerned about the safety of generated content. Which OCI feature allows them to define custom policies to block inappropriate outputs?

Accepted Answer

Content filtering and safety controls in Generative AI. OCI Generative AI includes built-in content filtering and safety controls that allow organizations to define custom policies to block inappropriate or harmful outputs. These controls operate at the model inference layer, enabling fine-grained filtering based on categories such as toxicity, hate speech, or personally identifiable information (PII). This directly addresses the concern about generated content safety.

Answer

OCI IAM policies

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OCI Audit logs

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OCI Vault

Question 12

A data scientist is using the OCI Generative AI SDK to create embeddings for a large corpus of legal documents. They want to perform semantic search. Which endpoint should they use?

Accepted Answer

/v1/embed. The /v1/embed endpoint is specifically designed to generate vector embeddings from input text, which are numerical representations that capture semantic meaning. For semantic search over a large corpus of legal documents, embeddings must be created to enable similarity comparisons, making this the correct choice.

Answer

/v1/classify

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/v1/generate

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/v1/chat

Question 13

Which of the following best describes the role of attention in transformer models?

Accepted Answer

It allows the model to focus on relevant parts of the input sequence when generating output.. The attention mechanism in transformer models dynamically computes a weighted sum of all input tokens, allowing the model to focus on the most relevant parts of the input sequence when generating each output token. This is achieved through scaled dot-product attention, which assigns higher weights to tokens that are more contextually important, enabling the model to capture long-range dependencies effectively.

Answer

It assigns equal weight to all words in the input.

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It is used only during training, not inference.

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It replaces the need for positional encoding.

Question 14

A company wants to deploy a private instance of a large language model on OCI for sensitive data processing. What is the recommended approach?

Accepted Answer

Use OCI Dedicated AI Clusters for a private endpoint.. OCI Dedicated AI Clusters provide a fully isolated, private endpoint for deploying large language models, ensuring that sensitive data never traverses the public internet. This approach meets the requirement for private inference with no data leaving the customer's tenancy, unlike public endpoints or third-party hosting.

Answer

Use OCI Data Science with a publicly accessible model.

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Use the OCI Generative AI public endpoint with data encryption.

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Use third-party model hosting outside OCI.

Question 15

During inference with OCI Generative AI, you notice that the model is generating repetitive phrases. Which combination of parameters can help reduce repetition?

Accepted Answer

Top_p = 0.9, frequency_penalty = 0.5. A high Top_p value (0.9) allows the model to consider a diverse set of tokens, reducing the chance of getting stuck in repetitive loops, while a positive frequency_penalty (0.5) actively penalizes tokens that have already been generated, discouraging the model from repeating the same phrases. Together, these parameters balance creativity and repetition suppression.

Answer

Top_p = 0.1, frequency_penalty = 0.5

Answer

Top_p = 0.9, frequency_penalty = 0.0

Answer

Top_p = 1.0, frequency_penalty = 0.0