1Z0-1127-25 Practice Test 18 — 15 Questions

Question 1

A company is deploying a RAG system for internal document search using OCI OpenSearch as the vector store. Users report that queries about recent policy changes return no results, even though the new policies were ingested. Which configuration is most likely missing?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The vector search index must have a refresh interval set to immediate.. OCI OpenSearch by default has a refresh interval (e.g., 1 second), which means recently ingested documents may not appear in search results immediately. Setting the refresh interval to 'immediate' forces the index to refresh after every write, making new documents searchable right away. Option A (hybrid search) improves relevance but does not affect document availability. Option B (normalizing embeddings) is important for cosine similarity but not for search availability. Option D (text-splitting chunker) is a preprocessing step for ingestion but does not control when documents become searchable.

Answer

The query should use a hybrid search combining keyword and vector.

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The embeddings must be normalized before indexing.

Answer

The ingestion pipeline should use a text-splitting chunker.

Question 2

When evaluating a summarization model, the team notices that the ROUGE-L score is high but human evaluators rate the summaries poorly for coherence. What does this discrepancy MOST likely indicate?

Accepted Answer

ROUGE-L is not sensitive to coherence and fluency, only to n-gram overlap. ROUGE-L measures longest common subsequence overlap, focusing on word order recall. It does not capture coherence, factual consistency, or fluency. High ROUGE-L with low human scores suggests the summaries are lexically similar but not well-formed.

Answer

The model is suffering from hallucination

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The reference summaries are too short

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The human evaluators are biased

Question 3

A developer is using the OCI Generative AI service and notices that the cost per API call is higher than expected. Which factor contributes MOST to the cost of an LLM inference call?

Accepted Answer

Number of input and output tokens. Most LLM APIs charge based on the number of input and output tokens. The token count directly affects cost. Model size, context window, and temperature are related but the direct billing metric is token count.

Answer

Model size (number of parameters)

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Context window size

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Temperature setting

Question 4

Which of the following is a key limitation of large language models that RAG (Retrieval-Augmented Generation) aims to address?

Accepted Answer

Hallucinations (factual errors). RAG addresses hallucinations by grounding the model's output in retrieved documents that contain factual information. Knowledge cutoff, bias, and context length constraints are separate issues that RAG may partially help with, but its primary purpose is to reduce factual errors.

Answer

Context length constraints

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Bias in training data

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Knowledge cutoff date

Question 5

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.

Answer

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 6

Which of the following sampling strategies is most likely to produce the most diverse and creative text?

Accepted Answer

Temperature sampling with temperature=1.2. Temperature sampling with a higher temperature (>1) increases the probability of less likely tokens, promoting creativity and diversity.

Answer

Beam search with width 5

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Greedy decoding

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Top-p sampling with p=0.1

Question 7

When using an LLM for code generation, a developer notices the model occasionally produces syntactically incorrect code. Which approach is most likely to reduce syntax errors while still allowing diverse output?

Accepted Answer

Set temperature to 0 and use greedy decoding. Lowering temperature reduces randomness, making outputs more deterministic and less prone to errors, while still allowing some variation.

Answer

Use top-k sampling with k=100

Answer

Increase the context window size

Answer

Increase the temperature to 1.5

Question 8

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

Answer

Perplexity

Question 9

Which tokenization algorithm is commonly used by models like GPT and BERT, and works by merging frequently occurring character pairs iteratively?

Accepted Answer

Byte-Pair Encoding (BPE). Byte-Pair Encoding (BPE) starts with characters and merges the most frequent pairs to create subword units. WordPiece uses a similar likelihood-based approach. SentencePiece is a framework that can use BPE or unigram.

Answer

Morpheme-based tokenization

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SentencePiece

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WordPiece

Question 10

Which of the following is a distinguishing feature of in-context learning compared to fine-tuning?

Accepted Answer

In-context learning does not update the model's weights; instead, examples are provided in the prompt. In-context learning does not update model weights; it provides examples in the prompt at inference time. Fine-tuning updates the model weights through additional training on a dataset.

Answer

In-context learning modifies the model's weights based on examples

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In-context learning is only possible with encoder-only models

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In-context learning requires additional training on a labeled dataset

Question 11

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.

Answer

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 12

Which component of the Transformer architecture allows the model to focus on different parts of the input sequence when generating each output token?

Accepted Answer

Self-attention mechanism. Self-attention computes attention scores between all pairs of positions, enabling the model to weigh the importance of different input tokens.

Answer

Positional encoding

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Feed-forward network

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Layer normalization

Question 13

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.

Answer

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 14

An OCI practitioner is comparing BERTScore with traditional n-gram metrics (ROUGE, BLEU) for evaluating summarization. Which THREE statements about BERTScore are true?

Accepted Answer

BERTScore uses pre-trained contextual embeddings from BERT. BERTScore uses contextual embeddings, captures semantic similarity, and correlates better with human judgment than n-gram metrics.

Answer

BERTScore does not require a reference text

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BERTScore computes similarity based on exact n-gram overlap

Question 15

An organization wants to deploy a model that can summarize long financial reports (5000+ tokens) without losing context. Which model architecture is best suited for this requirement?

Accepted Answer

Encoder-decoder model (e.g., T5). Encoder-decoder models like T5 or BART are designed for sequence-to-sequence tasks such as summarization, and can handle long inputs with their encoder.

Answer

Mixture-of-experts model

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Decoder-only model (e.g., GPT)

Answer

Encoder-only model (e.g., BERT)