1Z0-1127-25 LLM Fundamentals Practice Test 2 — 15 Questions

Question 1

Which of the following best describes the role of positional encoding in the Transformer architecture?

Accepted Answer

To provide information about the order of tokens in the input sequence. Positional encoding is essential in the Transformer architecture because the self-attention mechanism processes all tokens in parallel and has no inherent sense of order. By adding sinusoidal or learned positional vectors to the input embeddings, the model gains information about the relative or absolute position of each token in the sequence, enabling it to understand word order and sequence structure.

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To compress the input sequence length

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To increase the dimensionality of the hidden states

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To reduce the effect of vanishing gradients

Question 2

An ML engineer is choosing an LLM for a code generation assistant. The model must generate syntactically correct code, handle multiple programming languages, and be cost-efficient. Which THREE characteristics should the engineer prioritize?

Accepted Answer

Model with a large context window (e.g., 128K tokens). A large context window (e.g., 128K tokens) is critical for code generation because it allows the model to process entire code files, long dependency chains, and multi-file projects in a single pass. This reduces the risk of syntax errors caused by missing imports or incomplete function definitions, and it supports the generation of coherent, syntactically correct code across multiple languages without truncation.

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Model optimized for text summarization tasks

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Model with the highest number of parameters regardless of performance

Question 3

Which component of the Transformer architecture allows the model to weigh the importance of different tokens in the input sequence when generating an output?

Accepted Answer

Self-attention mechanism. The self-attention mechanism is the core component of the Transformer architecture that computes attention scores between every pair of tokens in the input sequence. These scores determine how much each token should influence the representation of every other token, allowing the model to dynamically weigh the importance of different tokens when generating an output. This is achieved through scaled dot-product attention, where queries, keys, and values are derived from the input embeddings.

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Positional encoding

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Layer normalization

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Feed-forward network

Question 4

A machine learning engineer needs to select an embedding model to compute semantic similarity between customer reviews. Which property is MOST important for the embedding model to produce useful similarity scores?

Accepted Answer

The embedding model should produce high-dimensional vectors that can be compared using cosine similarity. Cosine similarity measures the angle between vectors, making it ideal for comparing semantic similarity regardless of vector magnitude. High-dimensional embeddings from specialized embedding models (e.g., those available in OCI GenAI) are specifically trained to produce isotropic vector spaces where cosine similarity correlates with semantic relatedness. This property is fundamental for tasks like clustering or retrieval-augmented generation (RAG) where relative direction, not absolute distance, matters.

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The embedding model should be a generative model like GPT

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The embedding model should be fine-tuned on a classification task

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The embedding model should have a large context window to encode long reviews

Question 5

Which sampling strategy selects the token with the highest probability at each step, resulting in deterministic and often repetitive outputs?

Accepted Answer

Greedy decoding. Greedy decoding selects the token with the highest probability at each step, making it deterministic and often leading to repetitive outputs because it always chooses the most likely next token without considering future alternatives. This contrasts with stochastic methods that introduce randomness or explore multiple paths.

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Beam search

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Temperature sampling

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Top-k sampling

Question 6

An organization wants to use an LLM for translation tasks but is concerned about data privacy and wants to keep all data within OCI. Which model family is natively available in OCI Generative AI service?

Accepted Answer

Oracle's own models (e.g., OCI Generative AI models). OCI Generative AI service natively provides Oracle's own LLM models (e.g., OCI Generative AI models) that are hosted entirely within OCI infrastructure, ensuring data privacy. While the service also offers native access to models from Cohere and Meta's Llama, using Oracle's own models guarantees full data residency within OCI without reliance on third-party API calls. Third-party models like Gemini, GPT-4, and Claude are not natively available in OCI Generative AI.

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Gemini from Google

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GPT-4 from OpenAI

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Claude from Anthropic

Question 7

Which THREE of the following are known limitations of large language models? (Select THREE)

Accepted Answer

Knowledge cutoff (lack of information after a certain date). Large language models are trained on static datasets that have a fixed cutoff date, after which they have no knowledge of new events, publications, or data. This is an inherent architectural limitation: the model's parameters are frozen at the end of training, so it cannot learn or incorporate information beyond that point without retraining or fine-tuning.

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Real-time awareness of current events

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Unlimited context window

Question 8

A team is building a RAG pipeline on OCI. Which THREE steps are essential components of a standard RAG pipeline? (Select THREE)

Accepted Answer

Embedding each chunk into a dense vector using an embedding model. Embedding each chunk into a dense vector using an embedding model is a fundamental step in a RAG pipeline. The embedding model converts text chunks into high-dimensional vector representations that capture semantic meaning, enabling efficient similarity search during retrieval. Without this step, the system cannot compare query intent with document content in a vector space.

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Training a custom LLM from scratch on the document corpus

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Fine-tuning the generation model on the retrieved chunks

Question 9

Which component of the transformer architecture allows the model to weigh the importance of different words in a sentence when processing input?

Accepted Answer

Self-attention mechanism. The self-attention mechanism is the core component of the transformer architecture that enables the model to dynamically assign weights to each word in a sentence relative to every other word. This allows the model to capture contextual relationships and dependencies, such as determining which words are most relevant to the current word being processed, regardless of their distance in the sequence.

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Layer normalization

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Positional encoding

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Feed-forward neural network

Question 10

A data scientist wants to reduce the cost of token usage when summarizing large documents using an LLM on OCI. Which tokenization approach is MOST likely to lower token count for English text?

Accepted Answer

Use a model that tokenizes with Byte-Pair Encoding (BPE). Byte-Pair Encoding (BPE) is a subword tokenization algorithm that iteratively merges the most frequent byte pairs, creating a fixed-size vocabulary of common subwords. For English text, BPE efficiently represents common words as single tokens and splits rare words into meaningful subword units, resulting in fewer tokens overall compared to other methods. This directly reduces token usage and associated costs when processing large documents with an LLM on OCI.

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Use a model that tokenizes with WordPiece

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Use a model that tokenizes with SentencePiece

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Use a model that tokenizes at the character level

Question 11

An organization is evaluating two LLMs for a code generation task. Model A has a perplexity of 1.5 on the validation set, and Model B has a perplexity of 3.0. However, Model A generates more syntactically incorrect code. Which conclusion is MOST valid?

Accepted Answer

Perplexity alone is insufficient; evaluate with task-specific metrics like BLEU or human review. Perplexity measures how well a language model predicts a sequence, but it does not directly assess code correctness or syntactic validity. Model A's lower perplexity (1.5) indicates it is more confident in its predictions, yet it produces more syntactically incorrect code, showing that perplexity alone is not a reliable indicator of code generation quality. Task-specific metrics like BLEU (for n-gram overlap) or human review are necessary to evaluate functional and syntactic correctness, making option C the most valid conclusion.

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Model A's perplexity indicates overfitting, so Model B is preferable

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Model B is better because higher perplexity correlates with more diverse outputs

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Model A is better because lower perplexity always indicates higher quality outputs

Question 12

Which of the following is a known limitation of large language models?

Accepted Answer

They sometimes produce plausible but incorrect information (hallucinations). A well-documented limitation of large language models (LLMs) is their tendency to generate plausible-sounding but factually incorrect or nonsensical information, commonly referred to as 'hallucinations'. This occurs because LLMs are trained to predict the next token based on statistical patterns in their training data, not to verify facts or maintain a ground-truth database. This limitation is a core challenge in deploying LLMs for tasks requiring high factual accuracy, such as legal or medical advice.

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They can generate text in multiple languages

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They support few-shot learning via in-context examples

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They can be fine-tuned for domain-specific tasks

Question 13

A data scientist needs to compare two summaries generated by different models against a reference summary. Which metric focuses on recall of n-grams and is commonly used for summarization evaluation?

Accepted Answer

ROUGE. ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is the correct metric because it focuses on recall of n-grams, measuring how many of the reference summary's n-grams appear in the generated summary. This makes it the standard metric for summarization evaluation, as it directly assesses content coverage.

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BLEU

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Perplexity

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BERTScore

Question 14

A team is building a system to detect duplicate customer support tickets. They have a dataset of 10,000 resolved tickets and want to find pairs with similar intent. Which approach would be MOST efficient and effective?

Accepted Answer

Embed each ticket using an embedding model and compute cosine similarity between all pairs. Embedding each ticket into a dense vector space and computing cosine similarity between all pairs is both efficient and effective for detecting duplicate intents. This approach leverages pre-trained embedding models (e.g., sentence-transformers) that capture semantic similarity, and pairwise cosine similarity scales well for 10,000 tickets (approximately 50 million comparisons) using optimized matrix operations. It avoids the quadratic cost of LLM inference per pair while preserving high accuracy for intent matching.

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Use a long-context LLM to process all tickets in a single prompt

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Use a generation model like Cohere Command to compare tickets one pair at a time

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Fine-tune a generation model on a classification task to predict duplicates

Question 15

A company wants to translate legal documents from English to Spanish. They have a small parallel corpus of 500 sentence pairs. Which approach is MOST likely to yield the best translation quality?

Accepted Answer

Fine-tune an encoder-decoder model like T5 on the parallel corpus. Fine-tuning an encoder-decoder model like T5 on the 500 sentence pairs is the best approach because it leverages the model's pre-trained knowledge of language structure and translation patterns, then adapts it to the specific legal domain with a small but relevant parallel corpus. This transfer learning method requires far less data than training from scratch and typically outperforms zero-shot prompting for specialized, low-resource translation tasks.

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Use a zero-shot prompt with a decoder-only model like GPT

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Train a new model from scratch on the 500 sentence pairs

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Use a rule-based machine translation system