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

Question 1

Refer to the exhibit. A user runs the command shown and receives the error: 'ServiceError: NotAuthorizedOrNotFound'. What is the MOST likely cause?

Accepted Answer

The user does not have the 'inspect' permission on the model. The error 'NotAuthorizedOrNotFound' in OCI is a generic error that can occur either when the resource does not exist or the user lacks the necessary permission to access it. Given that the model ID and region are likely correct (otherwise a different error like 'InvalidParameter' would appear), the most probable cause is that the user does not have the 'inspect' permission on the model. This permission is required to view model details. Option A would result in a credential configuration error, not this generic error. Option C would produce an invalid model ID error. Option D is less likely because if the model were in a different region, the error would typically indicate a region mismatch rather than a generic authorization error. Therefore, option B is correct.

Answer

The CLI is not configured with OCI credentials

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The model ID is incorrectly formatted

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The model is in a different region than iad

Question 2

Which TWO statements about large language model (LLM) capabilities are correct?

Accepted Answer

LLMs can perform zero-shot learning without any task-specific training.. Option B is correct because LLMs are pretrained on broad corpora and can generalize to new tasks from a prompt alone, which is the definition of zero-shot learning without task-specific training. Option C is correct because LLMs are trained on large amounts of source code alongside natural language, enabling them to parse, generate, and reason about code constructs such as functions, syntax, and APIs. Option A is wrong because context windows are not fixed permanently; they can be extended through techniques like positional interpolation, longer-context training, or retrieval augmentation. Option D is wrong because LLMs can hallucinate and produce plausible but factually incorrect outputs. Option E is wrong because fine-tuning is optional, not required for every new task, since prompting and in-context learning often suffice.

Answer

LLMs have a fixed context window that cannot be extended.

Answer

LLMs always produce factually accurate outputs.

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LLMs require fine-tuning for every new task.

Question 3

A company wants to deploy a private instance of a large language model on OCI for sensitive data processing. What is the recommended approach?

Accepted Answer

Use OCI Dedicated AI Clusters for a private endpoint.. OCI Dedicated AI Clusters provide a fully isolated, private endpoint for deploying large language models, ensuring that sensitive data never traverses the public internet. This approach meets the requirement for private inference with no data leaving the customer's tenancy, unlike public endpoints or third-party hosting.

Answer

Use OCI Data Science with a publicly accessible model.

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Use the OCI Generative AI public endpoint with data encryption.

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Use third-party model hosting outside OCI.

Question 4

A company uses a RAG pipeline with OCI Data Science and Cohere embeddings. They notice that retrieval recall is low for domain-specific acronyms. What is the best practice to improve this?

Accepted Answer

Expand acronyms to their full forms during document preprocessing and indexing.. Expanding acronyms to their full forms during document preprocessing and indexing ensures that the embedding model can map the acronym to its semantic meaning, improving retrieval recall for domain-specific terms. Cohere embeddings are trained on general text, so without expansion, acronyms like 'NLP' may not match queries for 'Natural Language Processing' in vector space. This preprocessing step directly addresses the root cause of low recall for acronyms.

Answer

Reduce the cosine similarity threshold in the vector search.

Answer

Fine-tune the embedding model with domain-specific acronyms.

Answer

Increase the chunk size to include more context around acronyms.

Question 5

A developer is using Cohere Command R+ via OCI Generative AI and wants to ground answers in a provided set of documents using the `documents` parameter. Which prompt structure ensures the model correctly adheres to the documents?

Accepted Answer

Use a preamble: 'You answer questions based only on the provided documents. If the answer is not in the documents, say you don't know.' Then pass documents via the `documents` parameter in the request. Command R+ is a RAG-optimized model that expects a preamble (system-style instruction) telling it to ground responses strictly in the supplied documents, plus the documents passed via the dedicated `documents` parameter. This combination triggers the model's grounded generation mode, where it cites and restricts answers to the provided content. Without the preamble, the model may blend parametric knowledge with the documents.

Answer

Set a system prompt that says 'You are a helpful assistant' and pass documents in the `documents` parameter with no further instruction

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Place the documents in the conversation history as an assistant message before the user's question

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Include the documents in the user message after the question

Question 6

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

Answer

BERTScore computes similarity based on exact n-gram overlap

Question 7

A developer needs to extract structured data from news articles in JSON format, but the model occasionally outputs extra fields or omits required fields. Which prompt engineering approach would MOST reliably enforce the exact schema?

Accepted Answer

Specify the exact JSON schema in the task instruction and include constraints such as required fields. Specifying the exact JSON schema in the task instruction and including constraints such as required fields is the most reliable way to enforce a strict output schema. This approach explicitly tells the model what fields are required and what structure to follow, reducing the chance of extra or missing fields. It leverages the model's instruction-following capability.

Answer

Use a few-shot example of the desired JSON output

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Lower the temperature to 0 to make output deterministic

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Use chain-of-thought prompting to reason about the article before outputting JSON

Question 8

A team wants to create a prompt library for their organization's LLM applications. Which practice is MOST important for maintaining prompt quality over time?

Accepted Answer

Version-control prompts and define evaluation metrics for each prompt variant. Version-controlling prompts alongside defined evaluation metrics is the foundational practice for prompt-library quality because it enables reproducibility, rollback, and measurable comparison between variants. Without version history, teams cannot tell which prompt produced which output or why a regression occurred. Without evaluation metrics, 'improvements' are subjective and cannot be validated against held-out data. Together they form the minimum viable MLOps loop for prompt management.

Answer

Store all prompts in a shared document without version history

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Assign a single person to manage all prompts without review

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Keep prompts only in the codebase without separate documentation

Question 9

A developer is using the OCI Generative AI Chat API to build a multi-turn conversational assistant. They want the assistant to adopt a formal tone throughout the conversation. Which parameter should they set in the API request to achieve this?

Accepted Answer

preamble_override. The 'preamble_override' parameter sets the system-level instruction (e.g., 'You are a formal assistant...') that persists across turns. The other options control generation statistics, not system behavior.

Answer

temperature

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frequency_penalty

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max_tokens

Question 10

What is the primary purpose of setting a low temperature (e.g., 0.1) when generating text with an LLM?

Accepted Answer

Make responses more deterministic and focused. Temperature scales the logits before the softmax function. A low temperature like 0.1 sharpens the probability distribution, making the highest-probability token overwhelmingly likely to be selected. This produces more deterministic, focused, and repeatable outputs, which is desirable for tasks requiring factual accuracy or consistent formatting.

Answer

Increase creativity and diversity of responses

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Enable the model to use external tools

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Reduce the maximum number of tokens generated

Question 11

A developer wants the model to generate a bullet list of steps to troubleshoot a network issue. Which output format specification should be included in the prompt?

Accepted Answer

Request 'bullet list' in the output format. To get a bullet list of troubleshooting steps, the prompt should explicitly request a 'bullet list' in the output format specification. Explicit format instructions are the most direct and reliable way to control the model's output structure, and they work regardless of temperature or examples.

Answer

Use a few-shot example with numbered steps

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Provide a table format

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Set temperature to 0

Question 12

An AI application uses chain-of-thought prompting to solve math problems. The team observes that the model sometimes arrives at correct answers but with flawed reasoning. Which technique can specifically help by aggregating multiple reasoning paths?

Accepted Answer

Self-consistency prompting. Self-consistency prompting specifically addresses flawed reasoning by sampling multiple diverse reasoning paths (typically with higher temperature) and aggregating the final answers, usually by majority vote. Because correct reasoning paths tend to converge on the same answer while flawed ones diverge, the aggregated answer is more reliable than any single chain.

Answer

Role prompting

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Tree-of-thought prompting

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Few-shot prompting

Question 13

Which of the following metrics is most suitable for evaluating a translation model's output against multiple reference translations?

Accepted Answer

BLEU. BLEU (Bilingual Evaluation Understudy) is the most suitable metric for evaluating a translation model's output against multiple reference translations because it measures n-gram precision between the candidate translation and one or more reference translations. It directly quantifies how many words and phrases in the candidate match those in the references, making it the standard metric for machine translation tasks.

Answer

ROUGE

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Perplexity

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BERTScore

Question 14

Which parameter controls the creativity and randomness of a model's output by adjusting the probability distribution before sampling the next token?

Accepted Answer

Temperature. Temperature scales the logits (pre-softmax scores) before sampling, flattening or sharpening the probability distribution. Lower values make output more deterministic; higher values increase randomness and creativity.

Answer

Frequency penalty

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Max tokens

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Top-k

Question 15

A prompt engineer is using the self-consistency technique to improve answer reliability. Which TWO steps are essential when implementing self-consistency?

Accepted Answer

Aggregate the outputs (e.g., by majority voting or marginalizing over reasoning steps) to select the most consistent answer. Self-consistency requires generating diverse reasoning traces, so option E is correct: you must sample multiple independent reasoning paths by running the prompt several times with a non-zero temperature (e.g., temperature > 0) to obtain varied chains. Option D is also correct because after sampling those paths you must aggregate the outputs, typically via majority voting over final answers or marginalizing over reasoning steps, to select the most consistent answer. Option B is not essential to self-consistency itself; chain-of-thought prompting is a common underlying technique, but the defining steps are sampling multiple paths and aggregating them, not merely using a CoT prompt. Option A is wrong because tree-of-thought is a separate, more structured search method, not a required component of self-consistency. Option C is wrong because temperature 0 produces deterministic, identical outputs, which defeats the purpose of generating multiple diverse reasoning paths.

Answer

Use tree-of-thought to explore all possible reasoning branches

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Use a chain-of-thought prompt to guide the generation of reasoning paths.

Answer

Set temperature to 0 for reproducible outputs