Generative AI Leader Quick Quiz — 10 Questions

Question 1

A data scientist is trying to get online predictions from a Vertex AI endpoint but receives the error shown. What is the most likely cause?

Accepted Answer

The model has not been deployed to the specified endpoint. The error indicates that the model is not deployed to the endpoint. In Vertex AI, an endpoint is a resource that hosts one or more deployed models. If a model has not been deployed to the endpoint, any prediction request to that endpoint will fail with a 'model not found' or similar error, even if the endpoint ID and region are correct.

Answer

The region in the request does not match the endpoint region

Answer

The endpoint ID is incorrect

Answer

The model ID is incorrect

Question 2

A data scientist notices that a text generation model deployed on Vertex AI returns repetitive outputs after a few turns in a chat application. What is the most likely cause and the best parameter adjustment?

Accepted Answer

The top_p value is too high; reduce top_p to limit token sampling.. Repetitive outputs in a chat application after a few turns are typically caused by the model getting stuck in a loop due to high cumulative probability from top-p sampling. Reducing top_p limits the set of tokens considered at each step, forcing the model to explore less likely tokens and breaking the repetition cycle. This directly addresses the issue without sacrificing coherence, unlike temperature adjustments which affect randomness globally.

Answer

The max_output_tokens is too low; increase it to allow more diverse output.

Answer

The model is overfitted; switch to a smaller model.

Answer

The temperature is too low; increase temperature to add randomness.

Question 3

A company is deploying a generative AI model for medical diagnosis support. Which THREE considerations are critical for responsible AI?

Accepted Answer

Ensure the training data is diverse and representative.. Option A is correct because diverse and representative training data is critical for responsible AI in medical diagnosis. If the data lacks diversity, the model may exhibit bias, leading to inaccurate or harmful diagnoses for underrepresented groups. This directly impacts fairness, safety, and regulatory compliance in healthcare AI.

Answer

Maximize model throughput to handle high volumes.

Answer

Use the cheapest model to reduce costs.

Question 4

Which THREE considerations are critical when deploying a generative AI model using Vertex AI Endpoints for a latency-sensitive application? (Choose THREE.)

Accepted Answer

Model size and architecture. Model size and architecture directly impact inference latency because larger models with more parameters require more computation per request. For latency-sensitive applications, choosing a smaller or distilled model (e.g., Gemma 2B vs. 27B) or using quantization can reduce response times. Vertex AI Endpoints serve the model as-is, so the model's inherent computational cost is the primary driver of per-request latency.

Answer

Number of model versions

Answer

Number of model instances

Question 5

A company is deploying a generative AI model for customer support. They want to reduce hallucinations while maintaining fluency. They have a large dataset of previous support conversations. Which strategy should they prioritize?

Accepted Answer

Implement retrieval-augmented generation (RAG) using the conversation dataset as a knowledge base.. Retrieval-augmented generation (RAG) directly addresses hallucinations by grounding the model's responses in factual, retrieved data from the conversation dataset. This approach allows the model to generate fluent, contextually relevant answers while reducing the risk of inventing information, as it retrieves actual support interactions as evidence before generating a response.

Answer

Increase the beam search width to 10.

Answer

Fine-tune the model on the conversation dataset.

Answer

Set the temperature to 0.1.

Question 6

Which TWO techniques are commonly used to control the style and tone of a generative model's output?

Accepted Answer

Fine-tuning on a dataset with desired style. Option C is correct because fine-tuning on a dataset that embodies the desired style directly adjusts the model's weights, making it consistently produce outputs with that specific tone and style. This is a fundamental technique for customizing generative models, as it teaches the model the exact patterns, vocabulary, and stylistic nuances present in the training data.

Answer

Adjusting the temperature

Answer

Modifying the top_k value

Answer

Changing the top_p value

Question 7

A startup is building a generative AI content creation tool. They want to minimize operational costs while maintaining low latency for end users. Which deployment strategy should they adopt?

Accepted Answer

Use a serverless inference endpoint that scales to zero when not in use.. Option C is correct because serverless inference endpoints, such as AWS Lambda with SageMaker or Google Cloud Run, automatically scale to zero when idle, eliminating costs during periods of no traffic. This directly addresses the startup's goal of minimizing operational costs while maintaining low latency through rapid cold-start optimizations and provisioned concurrency for burst handling.

Answer

Deploy the model on edge devices to reduce cloud dependency.

Answer

Build an on-premises infrastructure to avoid cloud egress fees.

Answer

Provision dedicated GPU instances for consistent performance.

Question 8

A manufacturing company wants to use generative AI to create maintenance manuals from sensor data. The manuals must be accurate and reflect the latest equipment configurations. Which approach best ensures data freshness and consistency?

Accepted Answer

Use a retrieval-augmented generation (RAG) system that queries a live database of sensor configurations.. Option D is correct because a retrieval-augmented generation (RAG) system retrieves the most current equipment configurations directly from a live database at inference time, ensuring the generated manual reflects real-time sensor data without requiring model retraining. This approach decouples the static knowledge in the LLM from the dynamic data source, guaranteeing both accuracy and freshness while avoiding the latency and cost of continuous retraining.

Answer

Train the model in real-time as sensor data streams in.

Answer

Periodically retrain the model with the latest sensor data.

Answer

Have human technicians review and update the manuals manually.

Question 9

A marketing agency wants to generate images using Imagen on Vertex AI. They need to ensure the images are unique and avoid copyright issues. Which parameter adjustment is most relevant?

Accepted Answer

Use negative prompts. Negative prompts allow the model to exclude specific concepts, styles, or elements from generated images, directly reducing the risk of replicating copyrighted or trademarked content. By explicitly telling Imagen what not to include, the agency can steer outputs away from protected works without needing to modify training data or safety filters.

Answer

Increase training steps

Answer

Increase seed variability

Answer

Set safety threshold

Question 10

A retail company with a large FAQ database wants to build a generative AI customer service chatbot that can answer questions accurately with up-to-date information. Which business strategy should they prioritize?

Accepted Answer

Use retrieval-augmented generation (RAG) with vector search on the FAQ database.. Option A is correct because retrieval-augmented generation (RAG) with vector search allows the chatbot to dynamically retrieve the most relevant, up-to-date FAQ entries from a large database at inference time, grounding the generative model's responses in verified content without requiring retraining. This approach combines the flexibility of a pre-trained language model with the accuracy of real-time information retrieval, ensuring answers reflect the latest FAQ updates.

Answer

Train a new model from scratch using the FAQ data.

Answer

Fine-tune a foundational model on the entire FAQ dataset.

Answer

Use a general-purpose language model without any customization.

Generative AI Leader Quick Quiz — 10 Questions

Exhibit

Generative AI Leader Quick Quiz — 10 Questions

Exhibit