1Z0-1127-25 LLM Fundamentals Practice Test 6 — 15 Questions

Question 1

A practitioner wants to generate embeddings for a set of legal documents to enable semantic search. Which type of model should they use?

Accepted Answer

An embedding model like Cohere Embed. Embedding models (e.g., Cohere Embed, OpenAI text-embedding-ada) are specialized to produce dense vector representations. Generation models (like GPT) produce text, not embeddings.

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A large language model fine-tuned for classification

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A vision transformer model

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A generative LLM like Cohere Command

Question 2

What is the key advantage of multi-head attention over single-head attention in transformer models?

Accepted Answer

It allows the model to focus on different parts of the sequence simultaneously from different representation subspaces. Multi-head attention allows the model to attend to information from different representation subspaces at different positions, capturing a richer understanding.

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It eliminates the need for positional encoding

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It reduces the total number of parameters

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It makes the model non-autoregressive

Question 3

A team is deploying an LLM-based application that must adhere to strict data residency requirements. All processing must occur within a specific OCI region. Which OCI service should they use to host and serve the LLM?

Accepted Answer

OCI Data Science with a model deployment endpoint. OCI Data Science with a model deployment endpoint is the correct choice because it provides a managed infrastructure for hosting and serving LLMs within a specific OCI region, ensuring all processing and data remain within that region to meet strict data residency requirements. The model deployment endpoint runs on dedicated compute resources in the chosen region, allowing low-latency inference while adhering to regional data boundaries.

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OCI Streaming

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OCI Functions

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OCI Object Storage

Question 4

A researcher is comparing BLEU and ROUGE scores for a machine translation model. They notice that the BLEU score is high but the ROUGE score is low. Which scenario is MOST consistent with this observation?

Accepted Answer

The model outputs concise translations that capture key words but miss some reference phrases. High BLEU and low ROUGE indicate that the generated text has high precision (many n-grams match reference) but low recall (missing many reference n-grams). This often occurs when the output is short or overly cautious.

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The model outputs very long and verbose translations

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The model is overfitting to the training data

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The reference translations are of poor quality

Question 5

Which of the following is a decoder-only model architecture?

Accepted Answer

GPT-3. GPT is a decoder-only model. BERT is encoder-only. T5 is encoder-decoder.

Answer

T5

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BART

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BERT

Question 6

An enterprise is building a document Q&A application with OCI Generative AI. They want to minimize hallucinations. Which TWO techniques should they implement? (Choose two.)

Accepted Answer

Fine-tune the model on domain-specific data. RAG grounds answers in retrieved documents, and fine-tuning on domain-specific data reduces errors. Increasing context window alone does not reduce hallucinations; greedy decoding reduces creativity but not factual errors.

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Increase the model's context window to its maximum

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Use a higher temperature in sampling

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Apply greedy decoding

Question 7

A data scientist is evaluating an LLM for a summarization task. They have a set of human-written reference summaries. Which THREE metrics are commonly used to evaluate summarization quality? (Choose three.)

Accepted Answer

BLEU. ROUGE, BLEU, and BERTScore are all used for summarization evaluation. Perplexity measures model confidence, and cosine similarity is for embedding comparison.

Answer

Cosine similarity

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Perplexity

Question 8

An OCI customer is deploying a chatbot using a pre-trained LLM. They are concerned about the model generating biased or harmful content. Which TWO strategies should they implement as part of their responsible AI approach? (Choose two.)

Accepted Answer

Set up a human-in-the-loop review for sensitive queries. Content filtering and human review are direct mitigations. Training from scratch is impractical; modifying sampling does not address bias; increasing context window is irrelevant.

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Train the model from scratch on a curated dataset

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Increase the context window to include more examples

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Use top-k sampling with k=1

Question 9

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

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Fine-tune a base LLM on the policy documents monthly

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Use a larger foundation model with a longer context window and paste all documents into each prompt

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Train a custom model from scratch on the policy documents each month

Question 10

A data scientist is evaluating two LLMs for a summarization task. Model X scores 45 on ROUGE-L, while Model Y scores 42. However, in human evaluation, Model Y is preferred 60% of the time. What is the most likely explanation?

Accepted Answer

ROUGE-L measures lexical overlap, which may not capture the semantic quality that humans value. ROUGE-L measures the longest common subsequence (LCS) between generated and reference summaries, focusing on lexical (word-level) overlap. It does not assess semantic meaning, fluency, or factual correctness. Human evaluators often prefer summaries that are coherent and capture key ideas, even if they use different wording, which explains why Model Y can score lower on ROUGE-L but be preferred 60% of the time.

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Human evaluators are biased and cannot be trusted for objective assessment

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Model Y overfits to the training data, causing poor generalisation

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ROUGE-L is not a reliable metric for summarization because it only measures recall

Question 11

Which of the following is a primary limitation of large language models that can lead to generating factually incorrect information?

Accepted Answer

Hallucinations. Hallucinations are a primary limitation of large language models because they cause the model to generate text that is factually incorrect, nonsensical, or not grounded in the training data. This occurs due to the probabilistic nature of token prediction, where the model prioritizes fluency and coherence over factual accuracy, especially when the prompt lacks sufficient context or the model is asked to recall specific facts not well-represented in its training.

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Bias in training data

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Context window limitation

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Knowledge cutoff

Question 12

A team is implementing a RAG pipeline in OCI. They have a large collection of PDF documents. After chunking and embedding the documents, retrieval quality is poor. Which step is MOST likely the root cause?

Accepted Answer

The chunk size is too large, causing each chunk to contain multiple topics. Chunking strategy (size and overlap) directly affects how well the retrieval step can find relevant passages. Too large or poorly split chunks can dilute semantic meaning.

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The retrieval step uses greedy decoding

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The embedding model is a generation model, not an embedding model

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Cosine similarity is not appropriate for comparing embeddings

Question 13

A practitioner needs to choose a pre-trained model for a sentiment analysis task on customer reviews. The model must be efficient for inference and capable of handling multiple languages. Which architecture is MOST suitable?

Accepted Answer

Mixture of Experts model. Mixture of Experts (MoE) models are designed for efficient inference by activating only a subset of parameters per input, reducing computational cost. They can also support multiple languages by allocating different experts to different language patterns, making them highly suitable for multilingual sentiment analysis. In contrast, encoder-only BERT models are efficient but may not scale as well for multilingual tasks without large capacity, encoder-decoder models are optimized for sequence-to-sequence tasks, and decoder-only models are primarily generative and less efficient for classification.

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Encoder-only BERT model

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Encoder-decoder T5 model

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Decoder-only GPT model

Question 14

An organization wants to deploy an LLM for legal document analysis where accuracy is critical, and the model must not reference any external data outside the provided legal corpus. Which approach BEST satisfies these requirements?

Accepted Answer

Use RAG with a vector store containing only the legal documents, and set the retriever to return a fixed number of chunks with high similarity threshold. RAG can ground generation in a curated corpus, and with strict retrieval settings (e.g., only retrieving from the legal corpus), the model will not use any outside knowledge, reducing hallucinations.

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Use a decoder-only model with zero-shot prompting

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Use a fine-tuned encoder-only model for classification only

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Use a large foundation model with a high temperature setting

Question 15

A company has a large dataset of legal documents in multiple languages. They need to find documents semantically similar to a query. Which step is essential for this task?

Accepted Answer

Use a text embedding model to convert documents into dense vector representations. Embedding models convert text into dense vectors that capture semantic meaning. Cosine similarity between query and document embeddings is then used to find similar documents.

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Apply BPE tokenization to all documents

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Fine-tune a generation model on the legal documents

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Use beam search to identify similar passages