1Z0-1127-25 Practice Test 30 — 15 Questions

Question 1

An LLM generates a response that contains a plausible-sounding but factually incorrect statement about a historical event. This is an example of which known limitation?

Accepted Answer

Hallucination. Hallucination in LLMs refers to the generation of content that is plausible-sounding but factually incorrect or nonsensical. This occurs when the model's probabilistic next-token prediction produces statements that are not grounded in its training data or real-world facts, often due to overgeneralization or lack of factual recall mechanisms.

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Knowledge cutoff

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Bias in training data

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Context length constraint

Question 2

A security team requires that all OCI GenAI API calls be logged and audited. Despite enabling Audit logs in OCI, they do not see GenAI API calls. What is the most likely reason?

Accepted Answer

OCI Audit currently only records control-plane operations; data-plane operations like inference are not logged.. C is correct because OCI Audit service is designed to log control-plane operations (e.g., creating, updating, or deleting resources) but does not log data-plane operations such as inference API calls to the Generative AI service. The GenAI inference calls (e.g., generating text) are data-plane operations that occur on the service endpoint, not on the OCI control-plane API, so they are not captured by Audit logs. To log data-plane operations, you would need to use a different mechanism, such as OCI Vault for key usage or custom logging via API Gateway.

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The audit log retention policy is too short and logs were overwritten.

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The user is not a tenancy administrator.

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The API calls are made by an OCI function, which is not logged.

Question 3

Which statement accurately describes the T-Few fine-tuning technique used in OCI Generative AI?

Accepted Answer

It is a parameter-efficient fine-tuning method that updates only a fraction of the model parameters.. The T-Few fine-tuning technique is a parameter-efficient fine-tuning (PEFT) method that updates only a small fraction of the model's parameters, typically by introducing and training adapter layers or using low-rank updates. This approach significantly reduces computational and memory requirements compared to full fine-tuning, making it suitable for adapting large language models with limited resources. In OCI Generative AI, T-Few enables efficient customization without retraining the entire model.

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It automatically adjusts hyperparameters during inference.

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It does not require any training data and works by prompting only.

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It updates all model parameters, requiring substantial compute resources.

Question 4

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

Accepted Answer

They can generate factually incorrect information (hallucinations). Large language models (LLMs) are known to generate factually incorrect information, often called 'hallucinations.' This occurs because LLMs are probabilistic models that predict the next token based on training data patterns, not verified facts. They lack a grounding mechanism to validate outputs against real-world truth, making hallucinations a fundamental limitation.

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They always require internet access

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They cannot perform translation between languages

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They can only process numerical data

Question 5

A data scientist is creating a fine-tuning job in OCI Generative AI. They have prepared a JSONL dataset with prompt/completion pairs. What is the correct format for each line in the JSONL file?

Accepted Answer

{"prompt": "What is OCI?", "completion": "Oracle Cloud Infrastructure is a cloud computing platform."}. The OCI Generative AI fine-tuning service expects JSONL files where each line contains exactly the keys "prompt" and "completion". This format matches the service's internal training pipeline, which maps the prompt to the input and the completion to the expected output during supervised fine-tuning.

Answer

{"input": "What is OCI?", "output": "Oracle Cloud Infrastructure is a cloud computing platform."}

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{"text": "What is OCI?", "label": "Oracle Cloud Infrastructure is a cloud computing platform."}

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{"question": "What is OCI?", "answer": "Oracle Cloud Infrastructure is a cloud computing platform."}

Question 6

A developer is building a LangChain application that uses OCI Generative AI service. They want to implement streaming responses from the LLM to improve user experience. Which TWO actions are necessary to enable streaming?

Accepted Answer

Use a StreamingStdOutCallbackHandler or custom callback to handle stream events. LangChain's streaming architecture requires a callback handler (like `StreamingStdOutCallbackHandler` or a custom one) to process tokens as they are emitted from the LLM. Without a callback, the stream events are generated but not consumed, so the application cannot output partial results in real time.

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Increase the chunk_size to speed up streaming

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Disable memory to avoid buffering the stream

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Use async LangChain syntax for streaming

Question 7

A prompt engineer is designing a prompt for legal document analysis. The model should output JSON with a specific structure. Which TWO practices are essential to include in the prompt?

Accepted Answer

Define the JSON schema in the system prompt. Defining the JSON schema in the system prompt explicitly instructs the model on the exact structure and data types required for the output. This is a foundational practice in prompt engineering for structured data generation, as it reduces ambiguity and ensures the model adheres to a predefined format, which is critical for downstream parsing in legal document analysis.

Answer

Use a stop sequence of "}"

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Increase max tokens to 5000

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Set temperature to 1.0 for creativity

Question 8

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 9

Which TWO factors should be considered when selecting a base model for fine-tuning on OCI Generative AI service?

Accepted Answer

The model's size and number of parameters. When selecting a base model for fine-tuning on OCI Generative AI service, the model's size and number of parameters (B) directly impact computational cost, training time, and the model's capacity to learn from your dataset. The model's license and terms of use (C) are critical because commercial use, redistribution, and fine-tuning rights vary per model (e.g., Llama 2 vs. GPT-based models), and violating these can lead to legal or compliance issues.

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The model's training dataset size

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The model's training framework (PyTorch vs TensorFlow)

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The model's built-in features like content filtering

Question 10

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

Question 11

A researcher wants to compare two summarization models. Model A achieves a higher ROUGE-L score than Model B, but human evaluators prefer Model B's summaries. Which of the following is the MOST likely reason?

Accepted Answer

ROUGE-L measures n-gram overlap, which may not align with human judgment of quality. ROUGE-L measures n-gram overlap, which may not capture semantic quality. Human evaluators often prefer summaries that are fluent, coherent, and concise, even if they use different wording. The discrepancy indicates that ROUGE-L alone is insufficient for evaluation.

Answer

Model A overfits to the training data

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Model B has a larger context window

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Model A is an encoder-decoder model while Model B is decoder-only

Question 12

A team is using LangChain's ConversationalRetrievalChain with ConversationBufferMemory to build a chatbot. After a few turns, the chatbot starts repeating information from earlier messages. What is the MOST likely cause?

Accepted Answer

The conversation history accumulates in the prompt, causing the model to focus on past messages. ConversationBufferMemory stores the entire conversation history, and each new query appends that history to the prompt. As history grows, the LLM may receive redundant context, causing repetition. Using ConversationSummaryMemory or limiting history length can mitigate this.

Answer

The chunk_size in the text splitter is too large

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The LLM's max_tokens parameter is set too low

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The retriever's similarity search returns irrelevant chunks

Question 13

A developer needs to include the conversation history in a prompt for a LangChain chatbot. They want to insert previous exchanges between the user and the AI into the prompt at a specific position. Which component should they use?

Accepted Answer

MessagesPlaceholder. MessagesPlaceholder is the correct component because it is specifically designed to inject a list of messages (e.g., conversation history) at a designated position within a ChatPromptTemplate. It acts as a slot that gets replaced with the actual message list at runtime, allowing the developer to control exactly where previous exchanges appear in the prompt.

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PromptTemplate

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ChatPromptTemplate

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ConversationBufferMemory

Question 14

What is the primary purpose of the self-attention mechanism in a transformer model?

Accepted Answer

To process tokens in parallel while modeling long-range dependencies. The self-attention mechanism allows each token in the input sequence to attend to every other token, computing a weighted sum of their representations. This enables the model to capture long-range dependencies directly without the sequential processing constraints of RNNs, and because the attention scores for all tokens can be computed simultaneously, the mechanism supports parallel processing of the entire sequence.

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To reduce the number of parameters in the model

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To convert tokens into fixed-length vectors

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To ensure the model is autoregressive

Question 15

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.