Generative AI Leader Practice Test 47 — 15 Questions

Question 1

A company is deploying Gemini in an enterprise application and needs to choose between Gemini Pro and Gemini Flash for cost optimization. The application has high throughput and can tolerate lower latency. Which TWO considerations should guide the choice? (Choose 2)

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

Accepted Answer

Gemini Pro provides higher quality outputs at a higher cost. Gemini Flash is cost-optimized for high throughput and lower cost, while Gemini Pro offers higher quality but at higher cost. For high volume, cost-efficient inference, Flash is better. If quality is critical, Pro is warranted. On-device models (Nano) are not relevant here.

Answer

Gemini Flash runs on-device for latency-sensitive applications

Answer

Gemini Nano is the best choice for high-throughput server workloads

Answer

Gemini Pro is only available in Google AI Studio

Question 2

A company is comparing Google Cloud's AI offerings with AWS Bedrock and Azure OpenAI. They need to select a platform that provides: 1) native multimodal capabilities (image, video, text in one model), 2) integration with enterprise productivity tools, and 3) custom TPU hardware for training. Which THREE differentiators are unique to Google Cloud?

Accepted Answer

Integration with Google Workspace (Gmail, Docs, etc.). Gemini's native multimodality, Workspace integration, and TPU infrastructure are unique to Google Cloud. AWS and Azure do not offer the same combination.

Answer

Azure OpenAI Service with GPT-4

Answer

Amazon Bedrock with Titan models

Question 3

Which Google Cloud service allows you to run machine learning models directly using SQL queries on data in BigQuery?

Accepted Answer

BigQuery ML. BigQuery ML enables users to create, train, and deploy ML models using standard SQL, eliminating the need to move data to a separate environment.

Answer

Cloud Functions

Answer

Vertex AI

Answer

Dataflow

Question 4

A company is deploying a chatbot using Vertex AI and wants to ensure that the model's responses are grounded in Google Search results to reduce hallucinations. Which feature should they enable?

Accepted Answer

Vertex AI Search for grounding. Google Search grounding in Vertex AI allows the model to retrieve real-time information from Google Search to ground answers. RAG uses private data, not web search. Vertex AI Agent Builder includes grounding but is for building agents, not specifically the grounding feature itself.

Answer

Vertex AI Agent Builder

Answer

Retrieval-Augmented Generation (RAG) with internal documents

Answer

Gemini API with custom fine-tuning

Question 5

A healthcare organization needs to run ML models on patient data stored in BigQuery while ensuring data never leaves the database. Which service allows them to create and execute ML models directly in BigQuery SQL?

Accepted Answer

BigQuery ML. BigQuery ML enables SQL-based ML model creation and execution directly on data in BigQuery, meeting the requirement without data movement.

Answer

Cloud Functions

Answer

Vertex AI Prediction

Answer

Vertex AI Workbench

Question 6

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.. 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 7

A social media company uses a generative AI to moderate user comments. They need to filter hate speech, violence, and sexual content. What is the most efficient way to implement content safety in Vertex AI?

Accepted Answer

Use Google's pre-built safety filters provided with Vertex AI. Google's pre-built safety filters in Vertex AI are specifically designed for content moderation tasks like hate speech, violence, and sexual content detection. They are immediately available, require no custom training, and integrate directly with Vertex AI's generative AI workflows, making them the most efficient choice for a social media company needing rapid deployment.

Answer

Hire human moderators to manually review all comments

Answer

Use a third-party API for content moderation

Answer

Train a custom content classifier from scratch using Vertex AI AutoML

Question 8

A company is deploying a generative AI application using Vertex AI. They need to minimize latency for real‑time inference while maintaining high quality. Which TWO actions are most effective?

Accepted Answer

Use a smaller model like Gemini Flash. Using a smaller model (e.g., Gemini Flash) reduces latency, and reducing the max output tokens limits generation time. Increasing temperature does not affect latency; using a larger model increases latency; batching is for throughput, not single‑request latency.

Answer

Batch multiple inference requests together

Answer

Use Gemini Pro instead of Gemini Flash to ensure quality

Answer

Increase the temperature to 1.0 for more creative outputs

Question 9

A retail company plans to use Vertex AI's generative AI to create product descriptions. They need to ensure descriptions are factually accurate and do not misrepresent products. Which strategy should they prioritize?

Accepted Answer

Implement human-in-the-loop review. Human-in-the-loop (HITL) review is the correct strategy because it directly addresses the need for factual accuracy and prevention of misrepresentation. While generative AI can produce fluent text, it lacks a reliable grounding mechanism for product-specific facts, making human oversight essential to catch hallucinations, verify claims, and ensure compliance with advertising standards. This approach aligns with responsible AI practices and is a core recommendation for high-stakes content generation.

Answer

Use prompt engineering

Answer

Use a larger model

Answer

Increase temperature parameter

Question 10

A company is building a conversational AI using the Gemini API on Vertex AI. They want to reduce the chance of generating toxic content while still allowing creative and engaging responses for their gaming community. Which TWO safety settings should they adjust in the safety_settings parameter?

Accepted Answer

Enable the 'harm_category' filter for 'DANGEROUS_CONTENT' with threshold BLOCK_ONLY_HIGH.. Setting the 'DANGEROUS_CONTENT' category to BLOCK_ONLY_HIGH allows the model to generate creative and engaging responses for a gaming community while still blocking the most severe dangerous content. This balances safety with creative freedom, as the gaming context may involve simulated conflict or action that is not genuinely harmful.

Answer

Set the threshold for 'SEXUALLY_EXPLICIT' category to BLOCK_NONE.

Answer

Set the threshold for 'SEXUALLY_EXPLICIT' category to BLOCK_LOW_AND_ABOVE.

Answer

Set the threshold for 'HARASSMENT' category to BLOCK_LOW_AND_ABOVE.

Question 11

A machine learning engineer is using a generative AI model that occasionally outputs biased content. Which TWO actions should they take to address this issue?

Accepted Answer

Adjust the training data to be more representative of the target population. Evaluating bias with diverse test sets helps identify issues, and adjusting training data representativeness helps mitigate bias at the source.

Answer

Use a single test set for simplicity

Answer

Remove all safety filters to allow more freedom in outputs

Answer

Increase the model's size to improve generalization

Question 12

A data science team is fine-tuning a large language model using Vertex AI to generate marketing copy. They notice that the generated text is often repetitive and lacks creativity. Which technique should they apply to improve output diversity?

Accepted Answer

Increase the temperature parameter to 0.9.. Increasing the temperature parameter to 0.9 raises the randomness of the probability distribution over tokens, allowing less likely tokens to be selected. This directly counteracts repetitive output by encouraging the model to explore more diverse word choices, which is a standard technique for improving creativity in text generation.

Answer

Decrease the beam search width to 1.

Answer

Decrease the top-k sampling threshold.

Answer

Add more examples of repetitive text to the training dataset.

Question 13

An MLOps engineer wants to implement continuous evaluation of a generative model in production. Which Vertex AI component should they use?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the correct component because it provides continuous evaluation of model performance in production, including detecting prediction drift, data drift, and feature attribution drift. For generative models, it can monitor output quality and safety metrics over time, alerting engineers to degradation or shifts in model behavior without requiring manual intervention.

Answer

Vertex AI Feature Store

Answer

Vertex AI Prediction

Answer

Vertex AI Pipelines

Question 14

What is the most likely cause of the error?

Accepted Answer

The predict schema must be stored in the same bucket as the model artifacts and referenced without the full gs:// URI. The error occurs because the Vertex AI Predict schema must be stored in the same Cloud Storage bucket as the model artifacts, and when referenced in the model upload request, it should use a relative path (without the full `gs://` URI). Using the full URI causes a parsing failure, as Vertex AI expects the schema to be co-located with the model artifacts for validation and deployment.

Answer

The display name contains a hyphen which is not allowed

Answer

The container image URI is incorrect

Answer

The region us-central1 does not support TensorFlow models

Question 15

A graphic design company wants to generate high-quality synthetic images for product mockups. Which Google Cloud generative AI service is most suitable?

Accepted Answer

Imagen on Vertex AI. Imagen on Vertex AI is the correct choice because it is Google Cloud's state-of-the-art text-to-image diffusion model specifically designed to generate high-quality, photorealistic synthetic images from natural language prompts. This directly meets the requirement for creating product mockups, as Imagen can produce custom visuals with fine-grained control over style and composition, and it integrates seamlessly with Vertex AI for deployment and management.

Answer

AutoML Vision

Answer

Codey APIs for code generation

Answer

Natural Language API