Generative AI Leader Practice Test — Free Google Cloud Generative AI Leader Generative AI Leader Questions with Explanations

A company wants to use a pre-trained language model for customer support summarization. They need to ensure responses are concise and accurate. Which prompt engineering technique is most effective?

A startup wants to use a pre-trained model to generate product descriptions without training. Which Google Cloud service should they use?

A healthcare company is using Vertex AI to build a generative AI assistant that helps doctors draft clinical notes. The assistant uses a fine-tuned PaLM 2 model deployed on a private endpoint. Recently, doctors have reported that the assistant takes over 30 seconds to respond, causing workflow delays. Additionally, the monthly Vertex AI costs have increased by 40% without a proportional increase in usage. The model responses are generally accurate but sometimes include irrelevant details. The company wants to improve response time and cost while maintaining acceptable quality. A review of logs shows that most requests are for similar note types (e.g., progress notes, discharge summaries) and that the same prompt is used repeatedly with minor variations. What should the company do first?

Which Google Cloud service provides a managed environment for prompt engineering and model evaluation?

You are an ML engineer at a retail company. You have deployed a generative AI model on Vertex AI to generate product descriptions. The model uses a custom container and is deployed to a single endpoint. Recently, you noticed that inference latency has increased significantly during peak hours, causing timeouts. You have checked the logs and found that the CPU utilization on the deployed instances is consistently above 90% during peak hours. The model is currently deployed with a single machine type (n1-standard-4) and no scaling. You need to reduce latency without incurring excessive cost. What should you do?

A global corporation with 50,000 employees has seen rapid adoption of GenAI across marketing, product, and engineering teams. Each team selected its own models and cloud accounts, resulting in fragmented governance, unexpected costs, and varying output quality. The CFO demands a unified strategy to control costs and ensure consistency. The Chief AI Officer proposes several solutions. Which course of action best balances control with innovation?

A company has been using an on-premises ML infrastructure for generative AI and wants to migrate to Google Cloud. They have a pipeline that fine-tunes a large language model weekly using a proprietary dataset. The migration must minimize downtime and data transfer costs. Which approach best addresses these requirements?

A retail company wants to use GenAI to generate product descriptions. They have a small team of data scientists. What is the most efficient approach?

A company is building a document summarization tool using Vertex AI Gemini API. They notice that the model sometimes returns incomplete summaries that miss key points. Which approach is most likely to improve summary quality without increasing token usage significantly?

A developer wants to use Gemini 1.5 Pro to analyze hour-long video content and generate a summary. Which feature of Gemini 1.5 Pro is most suitable for this task?

An organization is using Vertex AI to fine-tune a large language model. They notice training is taking longer than expected and cost is increasing. Which action is most likely to reduce training time and cost without significantly impacting model quality?

A security team wants to prevent prompt injection attacks on their generative AI application hosted on Vertex AI. Which best practice should they implement?

A data scientist is using the Vertex AI PaLM API for text generation. They notice that the model occasionally generates toxic content. Which parameter should they adjust to reduce the likelihood of toxic outputs?

A company deploys a Gemini model on Vertex AI for a healthcare application. They need to ensure that the model does not generate medical advice and that responses are grounded in trusted medical sources. Which combination of safety measures should they implement?

A startup wants to generate product descriptions from a few keywords using a large language model. They have no prior ML experience and need the fastest time-to-market. Which Google Cloud service should they use?

A user reports that the model's response to the same prompt varies significantly across different calls. Which parameter change would most likely reduce variability?

A company uses a generative model to produce product descriptions. The descriptions are factually inconsistent with the product specs. Which technique would best ensure factual accuracy?

Refer to the exhibit. The team changed the generation parameters to reduce output variability. However, summaries now often repeat the same phrases. Which parameter change is most likely causing the repetition?

A company uses a text-to-image model to generate marketing visuals. The results often misinterpret the prompt, e.g., 'a red car' generates a blue car. Which technique should they try first to align the output with the prompt?

A legal team wants to use GenAI to review contracts and highlight risky clauses. They need the AI to consistently follow a specific classification taxonomy. The team has a small set of labeled examples (500 contracts). Which approach yields the BEST accuracy for this use case?