1Z0-1127-25 Practice Test 23 — 15 Questions

Question 1

A team is planning to use OCI Generative AI to summarize large documents. They need to choose between on-demand (pay-as-you-go) and dedicated cluster pricing. Which THREE factors should they consider when deciding? (Choose three.)

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

Accepted Answer

Throughput requirements and consistency. Latency sensitivity, throughput consistency, and cost predictability are key factors. On-demand is variable cost with potential contention; dedicated offers fixed cost with reserved capacity. Model accuracy does not differ based on pricing model, and data residency is a compliance factor but not directly tied to pricing model choice.

Answer

Model accuracy on summarization tasks

Answer

Data residency requirements

Question 2

Which OCI Generative AI model is best suited for generating embeddings from text that can be used for semantic search across multiple languages?

Accepted Answer

Cohere embed-multilingual-v3.0. Embed-multilingual-v3.0 is designed for multilingual embedding tasks. The other models are primarily for text generation or English-only embeddings.

Answer

Cohere Command R+

Answer

Meta Llama 3 70B

Answer

Cohere Rerank

Question 3

A developer is creating a fine-tuning job for a Cohere Command R model using OCI Generative AI. Which TWO of the following are required when submitting the fine-tuning job?

Accepted Answer

Base model name (e.g., Cohere Command R). The training dataset and the base model are mandatory inputs for a fine-tuning job. The compartment is part of the resource configuration, and the hyperparameters file is optional but not required.

Answer

A Dedicated AI Cluster ID

Answer

A pre-trained embedding model

Answer

Hyperparameters file in YAML format

Question 4

A developer wants to interactively test different prompts and parameters (temperature, top_p, frequency_penalty) with a Cohere Command R model before integrating it into an application. Which tool should they use?

Accepted Answer

OCI Generative AI Playground. The OCI Generative AI Playground provides a no-code interface to test models, adjust parameters, and see real-time outputs. It is designed for experimentation before production use.

Answer

OCI Console Resource Manager

Answer

OCI SDK for Python

Answer

OCI Data Science Notebooks

Question 5

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

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Train a custom model from scratch on the policy documents each month

Answer

Fine-tune a base LLM on the policy documents monthly

Question 6

A company needs to generate vector embeddings for a multilingual document set to support semantic search across English and French documents. Which embedding model should they use?

Accepted Answer

Cohere embed-multilingual-v3.0. The embed-multilingual-v3.0 model supports multiple languages, including English and French.

Answer

Cohere embed-english-v3.0

Answer

Cohere Command R

Answer

Meta Llama 3 8B

Question 7

A company wants to use OCI Generative AI Agents to create a RAG-powered customer support system. Which THREE components are essential for the agent to work?

Accepted Answer

A knowledge base. OCI Generative AI Agents require a knowledge base, a data source (like Object Storage), and a base LLM to generate responses. The other options are optional.

Answer

A Dedicated AI Cluster

Answer

A fine-tuned embedding model

Question 8

A developer is using the OCI Generative AI Chat API to build a multi-turn conversational agent. They want the model to remember previous exchanges within the same session. How should they manage conversation history?

Accepted Answer

Use the Chat API's message list parameter, appending each user and assistant message. The Chat API accepts a list of messages (including user and assistant turns) to maintain context. The developer should append each exchange to the message list and send it with each request.

Answer

Store history client-side and concatenate all previous outputs into the user prompt each time

Answer

Use the Generate API with a system prompt that includes the entire history

Answer

Rely on the model's built-in memory mechanism

Question 9

A developer is building a multilingual search application and needs to generate embeddings for user queries in multiple languages. Which two options are correct? (Select TWO)

Accepted Answer

Use the embed-multilingual-v3.0 model. OCI Generative AI provides embed-multilingual-v3.0 for multilingual support. The input type 'search_query' is designed for query embeddings. Embed-english-v3.0 is English-only. 'search_document' is for documents, not queries. 'classification' is for classification tasks.

Answer

Use the embed-english-v3.0 model

Answer

Set the input type to 'search_document'

Answer

Set the input type to 'classification'

Question 10

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. The 'use' verb on genai-family resources allows inference and use of models/endpoints without permitting management (create/update/delete). This matches the requirement.

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 11

A team wants to use OCI Generative AI Agents to build a question-answering system over documents stored in OCI Object Storage. They have created a knowledge base and are ready to test. Which API should they use to interact with the agent for multi-turn conversations?

Accepted Answer

Sessions API. The Sessions API is used to manage conversational sessions with OCI GenAI Agents, allowing multi-turn interactions. The Chat API is for direct LLM chat without agent capabilities. The Generate API is for single-turn text generation, and the Embedding API is for vector creation.

Answer

Chat API

Answer

Embedding API

Answer

Generate API

Question 12

A developer is using the OCI Generative AI Playground to test a Cohere Command R model. They want to reduce repetitiveness in the generated responses. Which parameter should they increase?

Accepted Answer

Frequency penalty. Frequency penalty penalizes tokens that have already appeared in the text, reducing repetition. Temperature increases randomness, top_p changes nucleus sampling, and max tokens controls length.

Answer

Max tokens

Answer

Top P

Answer

Temperature

Question 13

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. Providing a JSON schema with explicit instructions and constraints directly in the prompt is the most reliable way to enforce exact structure.

Answer

Use a few-shot example of the desired JSON output

Answer

Lower the temperature to 0 to make output deterministic

Answer

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

Question 14

A prompt engineering team is designing a system prompt for a chatbot that should adopt a professional and concise tone. Which prompt pattern is BEST suited for this requirement?

Accepted Answer

Role prompting in the system message: 'You are a professional customer support agent. Respond concisely and accurately.'. Role prompting explicitly sets the persona (e.g., 'You are an expert customer support agent') which guides tone and behavior. The system message is the ideal place for this.

Answer

Chain-of-thought prompting to ensure reasoning before answering

Answer

Zero-shot prompting with a direct instruction in the user message

Answer

Few-shot prompting with examples of professional responses in the user message

Question 15

Which prompt pattern is most suitable when a user needs to generate a structured report following a specific template every time?

Accepted Answer

Template pattern. Template patterns provide a fixed structure (e.g., fill-in-the-blanks) that the model follows, ensuring consistent output format.

Answer

Recipe pattern

Answer

ReAct pattern

Answer

Role prompting