1Z0-1127-25 Fundamentals of Large Language Models Practice Test 8 — 15 Questions

Question 1

A developer runs an OCI GenAI chat request with system prompt "You are a sarcastic assistant." The output is offensive. How can the developer enforce safety policies?

Accepted Answer

Use the OCI GenAI content moderation filter.. The OCI GenAI content moderation filter is specifically designed to enforce safety policies by detecting and blocking offensive, harmful, or policy-violating content in both input prompts and model outputs. By enabling this filter, the developer can prevent the model from generating offensive responses even when a system prompt like 'You are a sarcastic assistant' encourages undesirable behavior.

Answer

Change model to LLAMA.

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Increase maxTokens.

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Set temperature to 0.

Question 2

A data engineer wants to migrate a large corpus of PDFs to OCI for use with GenAI. Which storage and preprocessing approach is most efficient for RAG?

Accepted Answer

Store PDFs in OCI Object Storage, then use OCI AI Document Understanding to extract text and create embeddings.. OCI Object Storage is optimized for large-scale, unstructured data like PDFs, and OCI AI Document Understanding provides a managed service to extract text from PDFs, which can then be directly fed into embedding pipelines for RAG. This eliminates the need for manual preprocessing or local compute, ensuring scalability and integration with GenAI services.

Answer

Convert PDFs to text locally, upload to OCI Database, use SQL queries to retrieve.

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Use OCI Data Flow to process in batch and store in NoSQL.

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Store PDFs in OCI File Storage, mount to compute, run offline extraction.

Question 3

A developer is using OCI GenAI to generate structured data. They often get responses that include additional commentary or markdown. Which prompt engineering technique should they use to ensure only JSON output?

Accepted Answer

Add 'Return only JSON' at the end of the prompt.. Explicitly instructing the model to 'Return only JSON' directly constrains the output format, reducing the likelihood of extraneous commentary or markdown. This technique leverages prompt engineering to guide the model's behavior without altering inference parameters like temperature or top_p, which control randomness rather than output structure.

Answer

Set top_p to 0.1.

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Use a model with a larger context window.

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Increase the temperature to 1.5.

Question 4

A user has a prompt that exceeds the model's token limit. What is the best practice to handle this?

Accepted Answer

Summarize the earlier parts of the prompt and include the summary.. When a prompt exceeds the model's token limit, the best practice is to summarize the earlier parts of the prompt and include the summary. This preserves the essential context without exceeding the token limit, as the model's context window is fixed (e.g., 4,096 tokens for GPT-3.5 or 8,192 for GPT-4). Summarization reduces token count while retaining key information, enabling the model to process the entire input within its constraints.

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Increase the max tokens parameter in the API call.

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Truncate the prompt and hope the model understands.

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Split the input into multiple calls and merge results.

Question 5

A company wants to create a chatbot that answers questions based on a large internal document set that is updated weekly. They have limited ML expertise. Which approach is recommended?

Accepted Answer

Use retrieval-augmented generation (RAG) with a vector database.. Retrieval-Augmented Generation (RAG) with a vector database is the recommended approach because it allows the chatbot to answer questions based on a large, frequently updated document set without requiring model retraining. RAG retrieves relevant document chunks at query time using vector similarity search, then passes them as context to the LLM, ensuring up-to-date answers with minimal ML expertise.

Answer

Fine-tune a model on the entire document set.

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Train a custom model from scratch.

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Include all documents in the system prompt.

Question 6

A multi-turn chatbot needs to maintain context across user queries. The context window is limited. What design should be used?

Accepted Answer

Use a summary of previous turns and add new input.. Summarizing previous turns and appending the new input efficiently manages the limited context window of large language models (LLMs). This approach preserves essential conversational context without exceeding token limits, ensuring coherent multi-turn interactions.

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Store context in a separate database and retrieve each time.

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Reset context after each turn.

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Keep the entire conversation history in each request.

Question 7

A financial institution uses an LLM for generating investment advice. They are concerned about hallucinations. Which method is most effective?

Accepted Answer

Use RAG with a verified corpus of regulations and reports.. Retrieval-Augmented Generation (RAG) grounds the LLM's output in a verified, external knowledge base (e.g., regulations and reports). By retrieving relevant documents at inference time, RAG reduces the model's reliance on its parametric memory, directly mitigating hallucinations in high-stakes domains like financial advice.

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Fine-tune on general financial data.

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Increase the temperature to get more creative responses.

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Use a larger model to improve accuracy.

Question 8

A development team wants to generate code snippets from natural language. Which model strategy should they adopt?

Accepted Answer

Use a code-specific model like Code Llama.. Code Llama is a specialized variant of Llama 2 that has been fine-tuned on code datasets, enabling it to generate syntactically and semantically correct code from natural language prompts. This makes it the optimal choice for code generation tasks, as general-purpose LLMs lack the targeted training on code structures and programming languages.

Answer

Use a general-purpose LLM like Llama 2.

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Use a multimodal model.

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Use an embedding model for text.

Question 9

An AI assistant needs to solve complex math word problems step by step. Which prompting technique is most suitable?

Accepted Answer

Chain-of-thought prompting with few-shot examples.. Chain-of-thought prompting with few-shot examples is most suitable because it guides the LLM to break down complex math word problems into intermediate reasoning steps, mimicking human problem-solving. Few-shot examples provide a template for the desired reasoning structure, which significantly improves accuracy on multi-step arithmetic tasks compared to direct answer generation.

Answer

Zero-shot prompting with the problem only.

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Prompting with a high temperature setting.

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Using a model with a larger context window.

Question 10

Which two are essential components of the Transformer architecture? (Select TWO)

Accepted Answer

Self-attention mechanism. The self-attention mechanism is essential because it allows each token in the input sequence to attend to every other token, capturing long-range dependencies without the sequential bottleneck of RNNs. This mechanism computes attention scores using queries, keys, and values, enabling parallel processing and forming the core of the Transformer's ability to model context.

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Pooling layers

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Recurrent connections

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Convolutional layers

Question 11

Which three factors most significantly affect the quality of an LLM's output? (Select THREE)

Accepted Answer

Clarity of the prompt. The clarity of the prompt directly determines how well the LLM interprets the user's intent. A well-structured, unambiguous prompt reduces ambiguity and guides the model toward generating relevant and coherent responses, while a vague or poorly worded prompt often leads to off-target or nonsensical output.

Answer

Model's context window size

Answer

Number of GPUs used during inference

Question 12

Which three characteristics of LLMs can lead to hallucinations? (Select THREE)

Accepted Answer

Ability to generate plausible-sounding text. LLMs are trained to generate text that is statistically plausible and coherent, but they lack mechanisms to verify factual accuracy. This means they can produce sentences that sound convincing and grammatically correct while being entirely false, which is a direct cause of hallucinations.

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Overconfidence in predictions

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Large vocabulary size

Question 13

A customer support company uses Cohere Command on OCI to answer user queries. They have enabled grounding with a knowledge base of product manuals. However, for about 20% of queries, the model provides incorrect product recommendations that are not in the manuals. The team has verified the knowledge base is up to date. What is the most likely cause and solution?

Accepted Answer

The query phrasing may not match the knowledge base; improve the retrieval system or use query rewriting.. The most likely cause is that the query phrasing does not align well with the knowledge base content, leading to retrieval of irrelevant or no documents. Even with an up-to-date knowledge base, if the query is phrased differently from the manual's text, grounding may fail to retrieve the correct information. Improving the retrieval system (e.g., using semantic search) or implementing query rewriting can bridge this gap, enabling the model to use the appropriate context and provide accurate recommendations. Option A might help if temperature were high, but the core issue is retrieval, not creativity. Option B (switching to a larger model) does not address the retrieval problem. Option D (increasing retrieved documents) could introduce more noise if the retrieved documents are already irrelevant.

Answer

The model's temperature is too high, causing creative responses. Lower temperature to 0.

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The model is hallucinating; switch to a larger model.

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The grounding settings are too restrictive; increase the number of retrieved documents.

Question 14

A company is building a chatbot using OCI Generative AI service. They want to ensure that the model responses are grounded in their internal knowledge base. Which approach should they use?

Accepted Answer

Retrieval-Augmented Generation (RAG). Retrieval-Augmented Generation (RAG) is the correct approach because it retrieves relevant documents from the company's internal knowledge base at inference time and provides them as context to the LLM, ensuring the model's responses are grounded in verifiable, up-to-date information without modifying the model itself. This directly addresses the requirement to ground responses in an internal knowledge base while avoiding the cost and complexity of retraining.

Answer

Prompt engineering with few-shot examples

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Fine-tuning the model on the internal knowledge base

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Model distillation to compress the knowledge base

Question 15

A data scientist is using OCI Data Science with the Generative AI service to fine-tune a Cohere Command model on a custom dataset of customer support tickets. After training, the model produces poor, irrelevant responses. What is the most likely cause?

Accepted Answer

Insufficient training data quality or quantity. Insufficient training data quality or quantity is the most likely cause because fine-tuning a Cohere Command model on a custom dataset of customer support tickets requires a sufficiently large and representative dataset to teach the model domain-specific patterns. If the dataset is too small, noisy, or lacks diversity, the model will fail to generalize and produce irrelevant responses, even with correct tokenization and training hyperparameters.

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Incorrect tokenizer configuration

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Too many epochs causing overfitting

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Model architecture mismatch between fine-tuned and base model