1Z0-1127-25 Fundamentals of Large Language Models Practice Test 2 — 15 Questions

Question 1

An application using OCI GenAI experiences high response times. Which change will most directly reduce latency?

Accepted Answer

Reduce the number of output tokens requested.. Reducing the number of output tokens requested directly decreases the amount of text the model must generate per request. Since latency in OCI GenAI is proportional to the total number of tokens processed (input + output), fewer output tokens mean fewer autoregressive decoding steps, which directly reduces response time.

Answer

Increase the max tokens parameter.

Answer

Switch to a fine-tuned version of the same model.

Answer

Enable batched inference.

Question 2

A developer integrates OCI GenAI into a mobile app to provide product descriptions. The responses sometimes include explanations or questions instead of the requested format. The developer is using a simple prompt: 'Describe product X.' The app expects a single paragraph. Which corrective action should the developer take?

Accepted Answer

Add a structured prompt with format instructions and an example.. Adding a structured prompt with format instructions and an example directly addresses the issue of the model producing off-format responses. By explicitly specifying the expected output (e.g., 'Provide a single paragraph description without questions or explanations') and including a few-shot example, the developer constrains the model's generation to match the desired format, leveraging prompt engineering to guide the LLM's behavior without changing model parameters or architecture.

Answer

Lower the temperature to 0 to make responses deterministic.

Answer

Increase the max tokens to allow longer responses.

Answer

Switch to a different model with better language understanding.

Question 3

A machine learning team is fine-tuning a 7B parameter Llama 2 model on a custom dataset of 10,000 documents using OCI Data Science and GPU instances. They encounter out-of-memory (OOM) errors during the fine-tuning process. They are using a batch size of 8 and a sequence length of 2048. They cannot increase the GPU memory. Which change should they prioritize to resolve the OOM?

Accepted Answer

Enable gradient accumulation with steps of 4 or more.. Gradient accumulation divides the batch into micro-batches, reducing per-step memory usage while maintaining the effective batch size. This directly addresses OOM errors without increasing GPU memory. Option B (mixed precision) can reduce memory but may not be sufficient with a batch size of 8 and sequence length 2048. Option C changes the model, which may not be necessary or feasible. Option D (reducing epochs) does not affect per-step memory and may still cause OOM.

Answer

Use mixed precision training (FP16).

Answer

Reduce the model size by using a 3B parameter version.

Answer

Decrease the number of training epochs.

Question 4

An enterprise wants to deploy a large language model for processing sensitive internal documents. They must ensure that data does not leave their OCI tenancy. Which OCI GenAI deployment option meets this requirement?

Accepted Answer

Dedicated AI Cluster with on-demand model hosting. A Dedicated AI Cluster with on-demand model hosting provides a fully isolated, single-tenant infrastructure within your OCI tenancy. This ensures that all data processing and model inference occur on dedicated hardware that never leaves your tenancy, meeting strict data residency and security requirements for sensitive internal documents.

Answer

Using a third-party model via OCI Marketplace

Answer

Accessing models through OCI Console only

Answer

Using the OCI GenAI API with the default endpoint

Question 5

A research team is using OCI Data Science and OCI GenAI to build a multilingual chatbot for customer service. They have training data in English, Spanish, and French. The model currently struggles with code-switching—users often mix languages in a single query (e.g., 'Quiero cancel my order'), and the model responds inconsistently, sometimes in English, sometimes mixing incorrectly. The team wants to improve performance on code-switching while maintaining fluency in each language. They have limited compute resources and cannot deploy separate models per language. Which approach should they take?

Accepted Answer

Fine-tune a multilingual model on a combined dataset that includes code-switching examples.. Fine-tuning a single multilingual model (e.g., Cohere Command with multilingual support) on a combined dataset that includes code-switching examples directly teaches the model to handle mixed-language inputs, improving performance while maintaining fluency in each language without requiring separate models. Option A is incorrect because training separate models per language would prevent any code-switching capability and increase resource usage. Option C is incorrect because language detection and translation loses the original code-switching context and adds latency. Option D is incorrect because multilingual embeddings improve retrieval but do not directly improve generation fluency for code-switching.

Answer

Train separate fine-tuned models for each language and route queries based on detected language.

Answer

Use language detection to route the query to a specific language model, then translate the response.

Answer

Use a multilingual embedding model for retrieval to improve context understanding.

Question 6

Which TWO statements about large language model (LLM) capabilities are correct?

Accepted Answer

LLMs can perform zero-shot learning without any task-specific training.. LLMs are pre-trained on vast and diverse datasets, enabling them to perform zero-shot learning by generalizing from their training to new tasks without any task-specific fine-tuning. This capability stems from the model's ability to understand prompts and generate relevant responses based on patterns learned during pre-training, not from explicit instruction on the new task.

Answer

LLMs have a fixed context window that cannot be extended.

Answer

LLMs always produce factually accurate outputs.

Answer

LLMs require fine-tuning for every new task.

Question 7

Refer to the exhibit. A data scientist runs this inference request and receives a response that is incomplete and seems to stop mid-sentence. Which parameter should be adjusted to allow the model to generate longer outputs?

Accepted Answer

maxTokens. The `maxTokens` parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. When a response stops mid-sentence, it indicates the token limit was reached before the model could complete its output. Increasing `maxTokens` allows the model to generate longer, complete responses.

Answer

temperature

Answer

topP

Answer

frequencyPenalty

Answer

presencePenalty

Question 8

A multinational corporation is deploying a generative AI chatbot for customer support using Oracle Cloud Infrastructure's Generative AI service. The chatbot is powered by a large language model (LLM) accessed via the on-demand serving mode. During initial testing, the chatbot provides accurate answers for well-known products but frequently hallucinates or gives incorrect specifications for niche products. The company maintains a comprehensive internal database of product specifications, updated daily. The support team prefers not to fine-tune the LLM due to cost and maintenance overhead. Additionally, the chatbot must respond within 2 seconds to maintain a good customer experience. The team considers several approaches: A. Increasing the 'temperature' parameter to make the model more creative, hoping it will generate more accurate responses when unsure. B. Using few-shot prompting with three manually curated examples of correct product specifications included in every prompt. C. Implementing a Retrieval Augmented Generation (RAG) pipeline that retrieves relevant product documents from the internal database and prepends them to the prompt before inference. D. Reducing the 'topP' parameter to 0.1 to force the model to sample only from the highest probability tokens, thereby reducing randomness. Which approach best meets the requirements of improving factual accuracy while maintaining low latency?

Accepted Answer

Implement a Retrieval Augmented Generation (RAG) pipeline that retrieves relevant product documents from the internal database and prepends them to the prompt before inference.. Retrieval Augmented Generation (RAG) directly addresses the hallucination problem by providing the LLM with up-to-date, factual product specifications from the internal database as context in the prompt. This approach improves factual accuracy without fine-tuning, and because retrieval can be optimized (e.g., using vector search with approximate nearest neighbor algorithms), it can meet the 2-second latency requirement. RAG leverages the LLM's existing knowledge while grounding responses in authoritative external data.

Answer

Reduce the 'topP' parameter to 0.1 to force the model to sample only from the highest probability tokens, thereby reducing randomness.

Answer

Use few-shot prompting with three manually curated examples of correct product specifications included in every prompt.

Answer

Increase the 'temperature' parameter to make the model more creative, hoping it will generate more accurate responses when unsure.

Question 9

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.

Answer

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

Answer

Rely solely on prompt engineering with a general model

Answer

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

Question 10

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.

Answer

The model is overfitted; apply stronger regularization

Answer

The training data lacks diversity; add more varied examples

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The model has too many layers; reduce model size

Question 11

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.

Answer

Use the largest available general-purpose model

Answer

Rely on zero-shot summarization with careful prompting

Answer

Pre-train a new model from scratch on legal texts

Question 12

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.

Answer

Expand the token vocabulary

Answer

Lower the temperature to 0

Answer

Increase beam search width

Question 13

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.

Answer

Switch to a smaller model to reduce hallucination risk

Answer

Use prompt engineering to remind the model to be accurate

Answer

Fine-tune further with more ticket data

Question 14

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.

Answer

BLEU score

Answer

Perplexity

Answer

ROUGE score

Question 15

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.

Answer

Use a larger model with more parameters

Answer

Decrease the temperature during inference

Answer

Increase the number of training epochs