Generative AI Leader Google Cloud's Generative AI Offerings — Timed 10 Questions

Question 1

A healthcare company is building a chatbot to answer patient queries based on their medical documents stored in Cloud Storage. They want to minimize latency and ensure data residency in the EU. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Search with document grounding. Vertex AI Search with document grounding is correct because it allows the chatbot to ground responses in the customer's own medical documents stored in Cloud Storage, ensuring low latency through optimized indexing and retrieval, while supporting data residency controls to keep data within the EU. This service is specifically designed for enterprise search and Q&A over private document repositories, making it ideal for healthcare use cases requiring compliance and fast responses.

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Vertex AI Model Garden with fine-tuning

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Vertex AI Agent Builder with web search

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Vertex AI Codey APIs

Question 2

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?

Accepted Answer

Vertex AI Studio. Vertex AI Studio provides a no-code/low-code environment with pre-trained foundation models and prompt templates, enabling rapid generation of product descriptions from keywords without any ML expertise. It offers the fastest time-to-market because it eliminates the need for custom model training, infrastructure setup, or coding, directly leveraging Google's generative AI capabilities through a simple interface.

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Vertex AI Workbench with custom training

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Vertex AI Agent Builder

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Vertex AI Model Garden

Question 3

A financial services firm uses a fine-tuned Gemini model in Vertex AI for regulatory compliance checks. They notice that token usage is high, increasing costs. They want to reduce costs without sacrificing accuracy. Which approach should they take?

Accepted Answer

Set max output tokens to a lower value and use more precise prompts. Option C is correct because reducing max output tokens directly lowers the number of tokens generated per request, which is the primary cost driver in pay-per-token models like Gemini. Using more precise prompts further reduces token waste by guiding the model to produce concise, relevant outputs without sacrificing accuracy, as compliance checks often require specific, structured responses rather than verbose explanations.

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Switch to a smaller base model like PaLM 2 Bison

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Enable context caching to reuse previous responses

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Reduce temperature to 0.0

Question 4

A retail company wants to build a customer service chatbot that can handle returns, order status, and FAQs. They need to integrate with their existing backend systems. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Agent Builder. Vertex AI Agent Builder is the correct choice because it provides a low-code platform specifically designed for building conversational AI agents (chatbots) that can be integrated with enterprise backend systems via APIs, connectors, and custom tools. It supports grounding in enterprise data, multi-turn dialogue management, and seamless integration with existing systems for handling returns, order status, and FAQs, making it the most suitable service for this use case.

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Vertex AI Model Garden

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Vertex AI Search

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Vertex AI Codey API

Question 5

A media company uses Vertex AI to generate video captions. The generated captions sometimes contain factual errors about named entities (e.g., actor names). Which technique would most likely reduce these errors?

Accepted Answer

Use Vertex AI grounding with a knowledge base of verified entities. Option C is correct because Vertex AI grounding connects the model to a knowledge base of verified entities, allowing it to retrieve authoritative facts during generation. This reduces hallucinations about named entities by constraining outputs to validated data rather than relying solely on the model's parametric knowledge.

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Enable response caching

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Increase the temperature parameter

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Decrease top_p to 0.3

Question 6

A company is using Vertex AI Gemini API to analyze customer feedback. They notice that the model occasionally generates offensive content. They have already set safety settings to block high-probability harmful content. What additional step should they take to further reduce offensive outputs?

Accepted Answer

Adjust safety settings to block medium-probability harmful content. Option B is correct because the company has already blocked high-probability harmful content, but offensive outputs can still occur at lower probability thresholds. By adjusting safety settings to block medium-probability harmful content, they tighten the filter to catch more borderline cases without requiring model retraining or sacrificing output diversity. This leverages Vertex AI's configurable safety filters, which operate on likelihood categories (e.g., high, medium, low) rather than just binary blocking.

Answer

Set the temperature to 0.0

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Enable context caching

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Fine-tune the model on customer feedback data

Question 7

A global e-commerce company wants to translate product descriptions into 50 languages with high accuracy. They need to handle domain-specific terms (e.g., 'size chart', 'return policy'). Which approach should they use?

Accepted Answer

Use Vertex AI Translation with custom glossaries. Option C is correct because Vertex AI Translation with custom glossaries is specifically designed for high-accuracy, domain-specific translations. Custom glossaries allow you to define precise translations for terms like 'size chart' and 'return policy', ensuring consistency across 50 languages. This approach leverages Google's neural machine translation models while overriding generic translations with your business-specific terminology.

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Use the Gemini API with a prompt like 'Translate to French'

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Build a custom agent with Vertex AI Agent Builder

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Use Imagen to generate translated images

Question 8

Which TWO options are benefits of using Vertex AI Model Garden compared to using raw pre-trained models from external sources? (Choose two.)

Accepted Answer

Integration with Vertex AI tools like evaluation and monitoring. Option C is correct because Vertex AI Model Garden is deeply integrated with the Vertex AI ecosystem, providing native access to tools like Vertex AI Evaluation for model performance assessment and Vertex AI Monitoring for drift detection and observability. This integration eliminates the need for custom pipelines to connect external models with these managed services, streamlining the MLOps workflow.

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Lower cost compared to using generic APIs

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Ability to fine-tune models on custom data

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Guaranteed data privacy and no data sharing

Question 9

Which THREE factors should be considered when choosing between Gemini 1.5 Pro and Gemini 1.5 Flash for a customer-facing chatbot? (Choose three.)

Accepted Answer

Cost constraints: Flash is more cost-effective per token. Option A is correct because Gemini 1.5 Flash is designed as a cost-optimized model, offering significantly lower per-token pricing compared to Gemini 1.5 Pro. For a customer-facing chatbot with high query volumes, cost efficiency is a primary consideration, making Flash the more economical choice for routine interactions.

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Safety filters: Pro has stricter safety defaults

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Multimodal capability: Flash does not support image input

Question 10

Which TWO features are available in Vertex AI Studio for prompt engineering? (Choose two.)

Accepted Answer

Side-by-side comparison of model outputs. Option A is correct because Vertex AI Studio provides a side-by-side comparison feature that allows prompt engineers to evaluate outputs from multiple model configurations or parameter settings simultaneously. This enables direct visual comparison of responses, helping to identify the most effective prompt phrasing or parameter combination without manual switching.

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One-click deployment to a Vertex AI endpoint

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Fine-tuning models directly in the interface

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Building conversational agents with drag-and-drop