1Z0-1127 Practice Test 7 — 10 Questions

Question 1

A developer sends this request but receives an error: "modelId not found". Which is the most likely cause?

Accepted Answer

The modelId "cohere.command" is deprecated.. The error indicates the model ID is not recognized. "cohere.command" is a legacy model ID; the correct current ID might be "cohere.command-r" or similar. Compartment issues, temperature range, or parameter spelling would produce different errors.

Answer

The temperature parameter is out of range.

Answer

The compartment ID is incorrect.

Answer

The presencePenalty parameter is misspelled.

Question 2

A startup needs to deploy an LLM for a simple FAQ chatbot on OCI with low latency. Which model choice is most appropriate?

Accepted Answer

Use a smaller, task-specific fine-tuned model.. Option B is correct because a smaller fine-tuned model offers faster inference and sufficient accuracy for simple FAQs. Option A is overkill and slow, Option C may still be large, and Option D adds unnecessary complexity.

Answer

Use a medium-sized model with high precision.

Answer

Use an ensemble of models.

Answer

Use the largest available model for best quality.

Question 3

A developer integrates OCI GenAI into a mobile app to provide product descriptions. The responses sometimes include explanations or questions instead of the requested format. The developer is using a simple prompt: 'Describe product X.' The app expects a single paragraph. Which corrective action should the developer take?

Accepted Answer

Add a structured prompt with format instructions and an example.. Option B is correct because adding a structured prompt with format instructions and an example guides the model to output exactly as needed. Option A may increase irrelevant content, Option C may not fix the format issue, and Option D could make responses repetitive but still not enforce the format.

Answer

Lower the temperature to 0 to make responses deterministic.

Answer

Increase the max tokens to allow longer responses.

Answer

Switch to a different model with better language understanding.

Question 4

A company wants to build a retrieval-augmented generation (RAG) system using OCI Generative AI and a vector database. Which model type should they use to convert documents into vector embeddings?

Accepted Answer

Embedding model (e.g., cohere.embed). Option C is correct because embedding models are specifically designed to generate vector representations of text for retrieval. Option A (instruct models) are for generation. Option B (base models) are for general text generation. Option D (image models) are for images.

Answer

Instruct model (e.g., cohere.command)

Answer

Image generation model

Answer

Base model (e.g., cohere.base)

Question 5

A developer in the GenAIDevelopers group tries to call the OCI Generative AI inference API but receives an unauthorized error. Which statement best explains the issue?

Accepted Answer

The developer lacks the required permission to 'use generative-ai-inference' to invoke the model.. Option C is correct. The policy only allows creating inference requests but not to 'inspect' or 'use' inference. The developer needs the 'use' permission on the inference resource to actually call it. Option A is wrong because model-family is allowed. Option B is wrong because the policy allows operations. Option D is wrong because the compartment is correct.

Answer

The developer does not have permission to use the generative-ai-model-family.

Answer

The policy does not allow any operations in the compartment.

Answer

The compartment name in the policy is incorrect.

Question 6

A developer is using the OCI Generative AI Python SDK. They receive a 400 error 'InvalidParameter'. What is the most likely reason?

Accepted Answer

Invalid model name. Option C is correct because an invalid model name is a common cause of InvalidParameter errors. Option A (missing API key) would cause authentication errors (401). Option B (exceeded token limit) may cause a different error. Option D (network timeout) would cause a timeout error.

Answer

Exceeded token limit

Answer

Network timeout

Answer

Missing API key

Question 7

Which technique allows an LLM to be adapted to a new task with only a few examples?

Accepted Answer

Few-shot learning. Few-shot learning (option A) uses a handful of examples in the prompt to guide the model's behavior without fine-tuning. This is a form of prompt engineering specifically designed for few-shot scenarios.

Answer

Fine-tuning

Answer

Pre-training

Answer

Prompt engineering

Question 8

Refer to the exhibit. The output is very short and cuts off mid-sentence. Which parameter is most likely the cause?

Accepted Answer

Max-tokens is too low. The 'max-tokens' parameter limits the number of tokens in the generated response. Setting it to 500, while typically sufficient, might still cause truncation if the model's context window is nearly full or if the prompt is long. However, among options, 'max-tokens' is the direct control for output length. Option C is correct.

Answer

Temperature is too high

Answer

Model ID incorrect

Answer

Top-p is too high

Question 9

The job fails with "InvalidParameter: trainingDatasetUri". What should the administrator check first?

Accepted Answer

Whether the bucket exists and the file is accessible.. The error indicates the training dataset URI is invalid. The most common cause is that the bucket does not exist or the file path is incorrect. Model compatibility, budget, and parameter format would yield different errors.

Answer

Whether the compartment has sufficient budget.

Answer

Whether the model ID supports fine-tuning.

Answer

Whether the parameters JSON is correctly formatted.

Question 10

A company wants to create a chatbot that answers questions based on a large internal document set that is updated weekly. They have limited ML expertise. Which approach is recommended?

Accepted Answer

Use retrieval-augmented generation (RAG) with a vector database.. Option C is correct because retrieval-augmented generation (RAG) allows dynamic updates without retraining and is easier to implement. Option A requires frequent retraining, Option B may exceed context limits, and Option D is too resource-intensive.

Answer

Fine-tune a model on the entire document set.

Answer

Train a custom model from scratch.

Answer

Include all documents in the system prompt.

1Z0-1127 Practice Test 7 — 10 Questions

Exhibit

1Z0-1127 Practice Test 7 — 10 Questions

Exhibit