1Z0-1127-25 Practice Test 21 — 15 Questions

Question 1

You are using the OCI Generative AI Playground with a Cohere Command R model. You want the model to generate more varied and creative responses. Which parameter should you increase?

Accepted Answer

Temperature. Temperature controls the randomness of the output. Higher temperature (e.g., 0.8) makes the model more creative and varied. Max tokens, frequency penalty, and stop sequences do not primarily affect creativity.

Answer

Stop sequences

Answer

Max tokens

Answer

Frequency penalty

Question 2

A data scientist needs to fine-tune a model using OCI Generative AI. They have prepared a dataset in JSONL format with prompt/completion pairs. The fine-tuning job is configured with the T-Few technique. What is a key characteristic of T-Few fine-tuning?

Accepted Answer

It modifies a small subset of parameters using lightweight adapter layers. T-Few is a parameter-efficient fine-tuning method that updates only a small fraction of model parameters, making it faster and more resource-efficient than full fine-tuning. It does not train all parameters, add new layers, or require unlabeled data.

Answer

It requires unlabeled data for unsupervised pre-training before fine-tuning

Answer

It updates all model parameters, requiring significant compute resources

Answer

It only trains a new classification head on top of a frozen base model

Question 3

A regulatory compliance team needs to restrict access to the OCI Generative AI service so that only users in the 'AI_Engineers' group can create fine-tuning jobs and endpoints. Which IAM policy statement should be used?

Accepted Answer

Allow group AI_Engineers to use generative-ai-family in compartment ABC. To allow a group to manage generative AI resources, you allow them to use the 'generative-ai-family' resource type. Specific verbs like 'inspect' limit visibility but not management. 'Read' only allows viewing, not creating.

Answer

Allow group AI_Engineers to inspect generative-ai-family in compartment ABC

Answer

Allow group AI_Engineers to manage generative-ai-family in tenancy

Answer

Allow group AI_Engineers to read generative-ai-family in compartment ABC

Question 4

An organization requires low-latency inference for a custom fine-tuned model deployed on OCI Generative AI. The model must be isolated from other tenants. Which infrastructure choice meets these requirements?

Accepted Answer

Provision a dedicated AI cluster with the required model units. A dedicated AI cluster provides isolated, low-latency inference for custom models. Shared infrastructure does not guarantee isolation and may have higher latency due to contention.

Answer

Use the shared infrastructure endpoint for the fine-tuned model

Answer

Deploy the model on OCI Data Science model deployment

Answer

Use an on-premises GPU server and call OCI GenAI via API gateway

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

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

Answer

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

Answer

Fine-tune a base LLM on the policy documents monthly

Question 6

A developer is building a multi-turn chatbot using the OCI Generative AI Chat API. Which THREE parameters or features should they configure to maintain coherent conversation history?

Accepted Answer

System prompt. System prompt sets the assistant's behavior. Preamble override can customize the initial context. Multi-turn conversation history is managed by sending previous messages in the API call. Temperature and stop sequences affect generation but not history management.

Answer

Temperature

Answer

Stop sequences

Question 7

A company is building a document summarization pipeline using OCI Generative AI. They need to summarize thousands of legal documents efficiently. Which approach minimizes cost while maintaining quality?

Accepted Answer

Use the OCI Generative AI Summarisation API with a prebuilt model. The Summarisation API is designed for efficient, cost-effective summarization. Dedicated clusters are expensive for batch workloads, and embedding-based approaches add complexity and cost.

Answer

Fine-tune a model on legal documents and deploy on a dedicated cluster

Answer

Use the Chat API with a system prompt instructing summarization

Answer

Use the Embedding API to vectorize documents and then use a custom summarization algorithm

Question 8

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

Fine-tune a base LLM on the policy documents monthly

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

Question 9

In the OCI Generative AI Playground, a developer wants to control how creative the model responses are. Which parameter should they adjust?

Accepted Answer

Temperature. Temperature controls randomness; higher values produce more creative outputs. The other parameters control other aspects of generation.

Answer

Presence penalty

Answer

Max tokens

Answer

Stop sequences

Question 10

An organization needs to deploy a fine-tuned model for real-time inference with strict latency requirements. They have provisioned a Dedicated AI Cluster with 2 model units. Which statement about this setup is accurate?

Accepted Answer

The cluster provides low-latency, dedicated inference for the fine-tuned model, and you are billed per model unit.. Dedicated AI Clusters provide low-latency dedicated inference, and model units are used to allocate capacity for running models, including custom fine-tuned ones. The cluster is specifically designed for hosting fine-tuned models with consistent performance.

Answer

You must use the shared infrastructure for fine-tuned models; dedicated clusters are only for base models.

Answer

The cluster automatically scales model units based on request load.

Answer

The cluster can only host OCI’s built-in models, not custom fine-tuned models.

Question 11

A company wants to deploy a fine-tuned model for real-time inference with consistent low latency. They are evaluating dedicated AI clusters. Which THREE factors should they consider when provisioning the cluster?

Accepted Answer

Number of clusters (for high availability). Model units determine compute capacity and cost, cluster size affects availability and fault tolerance, and the region impacts data residency and latency. The base model is already chosen, and fine-tuning timeout is irrelevant for inference.

Answer

Fine-tuning job timeout settings

Answer

The base model used for fine-tuning

Question 12

An organization wants to use OCI Generative AI for a high-volume summarization workload. They estimate 10 million tokens per month and need consistent low latency. Which pricing model is most cost-effective?

Accepted Answer

Provision a Dedicated AI Cluster with model units. On-demand pricing can be expensive at high volumes. Dedicated clusters offer predictable cost per model unit and low-latency dedicated inference, making them more cost-effective for high-volume, latency-sensitive workloads. Pay-as-you-go (on-demand) is suitable for low or variable usage.

Answer

Use the free tier

Answer

Use OCI Data Science notebook sessions

Answer

On-demand token-based pricing

Question 13

Which OCI Generative AI API is used to send a message and receive a model-generated response while maintaining a conversation history?

Accepted Answer

Chat API. The Chat API is specifically designed for multi-turn conversations, supporting system prompts and history. The Generate API is for single-turn text generation. InferenceClient is a client class, not an API endpoint. The Embeddings API is for vector generation.

Answer

InferenceClient

Answer

Embeddings API

Answer

Generate API

Question 14

A team is fine-tuning a Llama 3 model using OCI Generative AI. The training dataset contains 10,000 prompt-completion pairs in JSONL format. After submitting the fine-tuning job, it fails with a 'Data validation error'. What is the most likely cause?

Accepted Answer

The JSONL file uses 'input' and 'output' as key names instead of 'prompt' and 'completion'. OCI fine-tuning requires a specific JSONL format with 'prompt' and 'completion' keys. If the keys are named differently (e.g., 'input'/'output'), validation fails. Model units, compartment, and base model are not typically validation issues.

Answer

The base model selected does not support fine-tuning

Answer

The dataset is in the wrong compartment

Answer

The fine-tuning job does not have enough model units allocated

Question 15

During fine-tuning, a user notices the loss does not decrease after several epochs. The dataset is a JSONL file with 500 prompt/completion pairs. What is the MOST likely cause?

Accepted Answer

The dataset is too small; T-Few fine-tuning generally needs at least 1000 examples. Fine-tuning with T-Few typically requires at least 1000 examples for meaningful learning. The dataset size is likely insufficient.

Answer

The JSONL format is incorrect because it lacks system prompts

Answer

The base model is not compatible with the T-Few technique

Answer

The learning rate is too high, causing the model to diverge