1Z0-1127-25 OCI Generative AI Service Practice Test 8 — 15 Questions

Question 1

A company is using OCI Generative AI Agents to build a customer support assistant. They have uploaded product manuals to OCI Object Storage. Which two components are required to create the agent? (Select TWO)

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

Accepted Answer

Create a Generative AI Agent using the knowledge base. A Generative AI Agent requires a knowledge base to provide domain-specific context for answering queries. The knowledge base is the source of truth that the agent uses to ground its responses, and it must be explicitly created and associated with the agent. Without a knowledge base, the agent would lack the product manual data needed for customer support.

Answer

Create an endpoint via InferenceClient

Answer

Upload data directly to the agent (not via knowledge base)

Answer

Provision a Dedicated AI Cluster

Question 2

Which OCI Generative AI model family is optimized for generating text embeddings that capture semantic meaning for tasks like clustering and classification?

Accepted Answer

Cohere Embed. Cohere Embed models are specifically designed for embedding text into vectors. Cohere Command and Meta Llama are generative models, not embedding models.

Answer

Cohere Rerank

Answer

Meta Llama 3

Answer

Cohere Command R

Question 3

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 4

A data scientist needs to fine-tune a Llama 3 model for a legal document classification task. They have a dataset of 10,000 labeled examples. Which fine-tuning technique available in OCI Generative AI is most suitable for efficiently adapting the model with limited computational overhead?

Accepted Answer

T-Few fine-tuning. T-Few fine-tuning is a parameter-efficient technique that updates only a small number of weights, making it suitable for fine-tuning large models with limited compute. It is the technique offered by OCI GenAI for fine-tuning.

Answer

Full fine-tuning all model parameters

Answer

LoRA (Low-Rank Adaptation)

Answer

Prefix tuning

Question 5

An organization plans to deploy a custom fine-tuned model for a real-time chat application requiring consistent low-latency responses. They expect high throughput during business hours. Which OCI Generative AI infrastructure choice best meets these requirements?

Accepted Answer

Provision a Dedicated AI Cluster with the required model units. A Dedicated AI Cluster provides reserved compute with low latency and high throughput, ideal for production workloads with custom models. Shared infrastructure may have variable performance.

Answer

Use the shared infrastructure on-demand endpoint

Answer

Use the OCI Generative AI Playground for production

Answer

Deploy the model on OCI Data Science using a custom container

Question 6

A developer is building a summarization pipeline using OCI Generative AI. They want to ensure the summary includes key points from the entire document without truncation. Which parameter should they primarily adjust?

Accepted Answer

Max_tokens. The max_tokens parameter controls the maximum length of the generated output. Increasing it allows longer summaries, preventing truncation.

Answer

Frequency_penalty

Answer

Temperature

Answer

Top_p

Question 7

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 8

A team has fine-tuned a Cohere Command R model using T-Few on a dataset of 5,000 prompt/completion pairs. After deployment, they notice the model sometimes generates off-topic responses. Which action is most likely to improve response relevance without requiring new training data?

Accepted Answer

Set a preamble override with instructions to stay on topic. Preamble override allows setting a system message that guides the model's behavior, helping to keep responses on topic. Adjusting it is a low-cost intervention.

Answer

Decrease the max_tokens

Answer

Increase the temperature to 1.5

Answer

Increase the frequency_penalty

Question 9

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 10

A data engineer is building a RAG application using OCI Generative AI Agents. They have documents stored in OCI Object Storage. Which resource must they create to make these documents searchable by the agent?

Accepted Answer

Create a data source pointing to the Object Storage bucket. In OCI Generative AI Agents, a data source points to Object Storage buckets and indexes the documents. Knowledge bases combine data sources, but the first step is creating the data source.

Answer

Create a Dedicated AI Cluster

Answer

Create an endpoint

Answer

Create a fine-tuning job

Question 11

A company needs to classify customer support tickets into 20 categories. They have a labeled dataset of 50,000 examples. They want to use OCI Generative AI Embedding API to generate embeddings, then train a classifier. Which input type should they use for the embedding API when processing the training examples?

Accepted Answer

classification. For training a classifier, the 'classification' input type is optimized to generate embeddings that work well for classification tasks. Other types are for different use cases.

Answer

search_query

Answer

clustering

Answer

search_document

Question 12

An organization wants to deploy a chatbot that uses a custom fine-tuned model. They have provisioned a Dedicated AI Cluster with 4 model units. During peak hours, they observe high latency and want to reduce it. What is the most cost-effective change?

Accepted Answer

Increase the number of model units in the dedicated cluster. Adding more model units increases throughput and reduces latency by distributing the load. This is the direct way to improve performance on a dedicated cluster.

Answer

Reduce the max_tokens parameter in the application

Answer

Switch to shared infrastructure to get more capacity

Answer

Enable response streaming

Question 13

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 14

A company is using OCI Generative AI Agents to build a customer support assistant. They have uploaded product manuals to OCI Object Storage. Which two components are required to create the agent? (Select TWO)

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

Accepted Answer

Configure the agent to use only the knowledge base and disable internet search. To ground the agent in provided documents, they should use a knowledge base, set a preamble to restrict knowledge, and disable internet search. Fine-tuning is not needed.

Answer

Increase the temperature to 2.0 for more creative responses

Answer

Fine-tune the underlying model on the onboarding documents

Question 15

A data scientist is evaluating the cost of deploying a fine-tuned model for a high-volume production application. They need low latency but are cost-sensitive. Which TWO considerations should they evaluate when choosing between on-demand (shared) and dedicated cluster pricing?

Accepted Answer

The cost per model unit per hour for a dedicated cluster. The cost of model units per hour and the expected token volume help determine whether dedicated or on-demand is more economical. Cluster provisioning time and streaming availability are not direct cost factors.

Answer

Whether the model supports streaming responses

Answer

The number of days required to provision the dedicated cluster

Answer

The availability of the model in the OCI Generative AI Playground