1Z0-1127-25 Practice Test 35 — 15 Questions

Question 1

Which of the following is NOT an available model in OCI Generative AI service?

Accepted Answer

OpenAI GPT-4. OCI Generative AI service offers a curated catalog of models from partners such as Cohere (Command, Embed, Rerank) and Meta (Llama 2/3), plus Oracle's own models. OpenAI GPT-4 is not part of the OCI Generative AI model catalog; OpenAI models are accessed through Azure OpenAI or OpenAI's own API, not OCI. Therefore GPT-4 is the model that is NOT available in OCI Generative AI.

Answer

Cohere Embed English v3.0

Answer

Meta Llama 3

Answer

Cohere Command R+

Question 2

A company is deploying a LangChain application on OCI and needs to implement error handling and rate limit management. Which THREE strategies should they consider? (Choose THREE.)

Accepted Answer

Implement retry logic with exponential backoff when receiving 429 (Too Many Requests) responses. A is correct because HTTP 429 (Too Many Requests) responses indicate rate limiting by the API provider. Implementing retry logic with exponential backoff is a standard resilience pattern that progressively increases wait times between retries, preventing further rate limit violations and allowing the system to recover gracefully without overwhelming the endpoint.

Answer

Increase the chunk_size parameter in the text splitter

Answer

Resubscribe to the model endpoint if errors occur

Question 3

A developer is using the OCI Generative AI API to generate text. The responses are often too short and incomplete. Which parameter adjustment is most likely to produce longer, more complete responses?

Accepted Answer

Increase the max_tokens parameter.. The max_tokens parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. By increasing max_tokens, the model is allowed to produce longer sequences, which directly addresses the issue of responses being too short and incomplete. In the OCI Generative AI API, this is the primary parameter for capping output length.

Answer

Decrease the max_tokens parameter.

Answer

Increase the top_p parameter.

Answer

Decrease the frequency_penalty parameter.

Question 4

An application using OCI Generative AI produces inconsistent responses to the same user query. The developer suspects the model's output variability is too high. Which parameter adjustment would most directly reduce output randomness?

Accepted Answer

Decrease the temperature parameter.. Temperature directly controls the randomness of token sampling in the model's output distribution. Lowering temperature (e.g., from 0.7 to 0.2) makes the model more deterministic by concentrating probability mass on the most likely next tokens, thus reducing output variability for the same query.

Answer

Increase the max tokens parameter.

Answer

Increase the top_p parameter.

Answer

Change the model to a smaller variant.

Question 5

A security administrator needs to grant a group of data scientists access to use OCI Generative AI resources (models, endpoints) in compartment 'GenAI-Prod', but not allow them to create or manage infrastructure. Which IAM policy statement should be used?

Accepted Answer

Allow group DataScientists to use genai-family in compartment GenAI-Prod. In OCI IAM, the verb 'use' grants the ability to use existing resources (e.g., invoke models, call endpoints) without creating or managing them. For OCI Generative AI, 'use genai-family' allows data scientists to work with models and endpoints in the compartment while preventing infrastructure management. The 'manage' verb would grant full control including create/delete, which is explicitly not desired.

Answer

Allow group DataScientists to inspect genai-family in compartment GenAI-Prod

Answer

Allow group DataScientists to read genai-family in compartment GenAI-Prod

Answer

Allow group DataScientists to manage genai-family in compartment GenAI-Prod

Question 6

A company's AI system uses RAG to answer customer questions. Users often get incomplete answers because the retrieved chunks do not contain all relevant information. Which step in the RAG pipeline is most likely the issue?

Accepted Answer

Chunking strategy (chunk size and overlap). Chunking determines how documents are split into pieces. If chunks are too small, key information may be split across chunks, causing incomplete retrieval. Adjusting chunk size and overlap can improve completeness.

Answer

Retrieval top-k setting

Answer

Generation model temperature

Answer

Embedding model selection

Question 7

What is the primary benefit of using a system prompt to set the persona and tone before the user message?

Accepted Answer

It sets the overall behavior, tone, and constraints for the model throughout the conversation. A system prompt is prepended to the conversation and establishes the model's persona, tone, constraints, and behavioral rules that persist across turns. It shapes every subsequent response without needing to repeat instructions in each user message.

Answer

It reduces the token cost of each user message

Answer

It automatically grounds the model in the latest training data

Answer

It replaces the need for few-shot examples

Question 8

Which prompting technique involves providing the model with a small number of input-output examples within the prompt to guide its behavior?

Accepted Answer

Few-shot prompting. Few-shot prompting provides a small number of input-output examples inside the prompt so the model can infer the desired pattern, format, or reasoning style. It is the standard technique for guiding behavior without fine-tuning.

Answer

Chain-of-thought prompting

Answer

Zero-shot prompting

Answer

Tree-of-thought prompting

Question 9

A company uses OCI Generative AI Agents to build a RAG application that answers questions from documents stored in OCI Object Storage. The knowledge base is updated daily. Which step is necessary to ensure the agent incorporates the latest documents?

Accepted Answer

Re-sync the knowledge base after updating the documents in Object Storage. The knowledge base indexes the data sources. To reflect changes in the source documents, you must re-sync or re-index the knowledge base. The agent endpoint or session API does not automatically refresh content.

Answer

Use the Embedding API to manually index each new document

Answer

Set the session API to refresh automatically

Answer

Recreate the agent each time documents change

Question 10

An AI engineer is designing a prompt to generate a report summary. The prompt currently says: 'Summarize the following text.' The output is often too verbose. Which modification would best enforce a concise, bullet-list format?

Accepted Answer

Change the prompt to: 'Summarize the following text in a bullet list of at most 5 items. Each bullet must be under 10 words.'. The revised prompt is correct because it adds explicit, measurable constraints — 'bullet list,' 'at most 5 items,' and 'under 10 words' — which directly shape the output format and length. LLMs respond to concrete instructions far more reliably than to vague ones like 'summarize,' so specifying count and word limits is the most deterministic way to enforce conciseness. This is a prompt-engineering fix, not a parameter-tuning fix.

Answer

Set temperature to 1.0 for more focused outputs

Answer

Increase the frequency penalty to 2.0

Answer

Add an example summary at the end of the prompt

Question 11

A developer is using the OCI Generative AI Chat API to build a conversational assistant. They want the assistant to adopt a formal tone regardless of user input. Which parameter should they set in the API request?

Accepted Answer

Set the system prompt to 'You are a formal assistant that responds in a professional tone.'. The system prompt (or preamble) sets the assistant's behavior consistently. Temperature affects randomness but not tone directly.

Answer

Set a high temperature (e.g., 0.9)

Answer

Set the frequency penalty to a high value

Answer

Set max tokens to a low value

Question 12

A researcher wants to compare the performance of two LLMs on OCI Generative AI: a base model and an instruct model. They notice the instruct model often refuses to generate certain types of content. Which factor most likely explains this behavior?

Accepted Answer

The instruct model has been fine-tuned with reinforcement learning from human feedback (RLHF) to align with safety guidelines.. Instruct models are typically fine-tuned using reinforcement learning from human feedback (RLHF) to align with safety guidelines and ethical constraints. This fine-tuning process teaches the model to refuse generating harmful, biased, or unsafe content, which explains why the instruct model refuses certain types of content while the base model does not.

Answer

The base model was programmed to follow stricter rules.

Answer

The instruct model was trained on a smaller dataset.

Answer

The base model rejects content more often.

Question 13

A data scientist is designing a prompt to extract structured information (e.g., JSON) from text using an instruct model on OCI Generative AI. The model sometimes outputs additional text beyond the JSON, breaking parsing. Which prompt engineering technique is most effective to enforce structured output?

Accepted Answer

Include a few-shot example of the expected JSON output in the prompt.. Few-shot prompting provides explicit examples of the desired output format, which instructs the model to follow the exact JSON structure and reduces the likelihood of extraneous text. This technique leverages the model's in-context learning ability to adhere to formatting constraints, making it the most effective for enforcing structured output in OCI Generative AI instruct models.

Answer

Use a base model instead of an instruct model.

Answer

Set the temperature to 0.0 to reduce randomness.

Answer

Increase max_tokens to allow for additional output.

Question 14

A model generates code with security issues. Which approach is best to mitigate this?

Accepted Answer

Add a system prompt with security guidelines. Adding a system prompt with security guidelines instructs the model to avoid generating insecure code, directly addressing the issue at the prompt level. This is a targeted mitigation that can be applied without changing the model or its parameters.

Answer

Reduce max_tokens

Answer

Increase temperature

Answer

Use a different model

Question 15

Refer to the exhibit. A developer runs the OCI CLI command and receives the output. However, the text "Hello, how are you?" is actually a mix of English and French words. Why does the model assign only 0.03 to French?

Accepted Answer

The text is overwhelmingly English, so the model assigns a low probability to French.. The model's output shows a probability distribution over languages, and the text is predominantly English with only a few French words. The model assigns a low probability (0.03) to French because the overwhelming majority of tokens are English, making the text far more likely to be classified as English. This reflects how language identification models evaluate the overall composition of the input.

Answer

The model is limited to identifying a single language per query.

Answer

The model cannot detect multiple languages in a single text.

Answer

The model's scores are normalized to sum to 1, so a high English score forces low others.