Generative AI Leader Google Cloud's Generative AI Offerings Practice Test 4 — 15 Questions

Question 1

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?

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

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 2

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 3

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. 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 4

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. 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.

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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 5

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. 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 6

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 Model Garden models are first-class Vertex AI resources, so they plug directly into Vertex AI Evaluation and Vertex AI Model Monitoring (including skew/drift detection) without custom glue code. Option D is correct because Model Garden provides one-click or API-driven deployment to Vertex AI Endpoints, which handles autoscaling, traffic splitting, and serving infrastructure that you would otherwise build yourself for an externally sourced model. Option A is not inherently true — Model Garden includes both open and partner/proprietary models, and pricing varies, so it is not a guaranteed cost benefit. Option B is not unique to Model Garden, since raw pre-trained models from external sources can also be fine-tuned. Option E is not guaranteed, as data privacy depends on the specific model's terms and your configuration, not on Model Garden itself.

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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 7

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. 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 8

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

Accepted Answer

Side-by-side comparison of model outputs. 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

Question 9

A company is building a generative AI chatbot for customer support using Vertex AI. They want to ground the model responses with their internal knowledge base stored in Cloud Storage and BigQuery. Which feature should they use to ensure the model only answers from the provided data and avoids hallucination?

Accepted Answer

Vertex AI Grounding with Vertex AI Search. Vertex AI Grounding with Vertex AI Search is the correct feature because it allows the model to retrieve and cite information from a specified data source (such as Cloud Storage and BigQuery) to generate responses. This process, known as grounding, ensures the model's output is based solely on the provided authoritative data, effectively reducing hallucinations by constraining the model to factual, retrieved content rather than relying on its internal parametric knowledge.

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Vertex AI Prediction

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Vertex AI Pipelines

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Cloud Functions

Question 10

A developer is using Vertex AI Studio to prototype a chat application. They want to provide the model with a system instruction to set the tone and style. How should they configure this in the Vertex AI Studio interface?

Accepted Answer

Use the 'System Instruction' field in the model configuration. Vertex AI Studio provides a dedicated 'System Instruction' field in the model configuration panel, which allows developers to set the tone, style, and behavioral guidelines for the model without mixing them into the user prompt. This field is specifically designed to hold system-level instructions that are prepended to the conversation context, ensuring consistent behavior across multiple turns.

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Add the instruction as part of the prompt text

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Set the temperature parameter to a high value

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Add the instruction in the 'Context' parameter

Question 11

An organization is using Vertex AI Agent Builder to create a customer service agent. They want the agent to be able to hand off to a human agent when it cannot answer a question. What should they configure in the agent's design?

Accepted Answer

Use a 'Fallback' intent to route to a human. In Vertex AI Agent Builder (Dialogflow CX), when the agent cannot answer or match a user's request, the conversation triggers a fallback/no-match path. To hand off to a human, you configure that fallback path to route to a live agent via fulfillment, webhook, or live-agent handoff. There is no standard built-in 'Escalation' intent in the product; escalation is an outcome you implement, not a specific intent type.

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Configure 'Slot filling' to collect more info

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Implement a 'Confirmation' prompt for the user

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Add an 'Escalation' intent that triggers a human handoff

Question 12

A data scientist wants to fine-tune a foundation model from Vertex AI Model Garden on their custom dataset. They want to choose a cost-effective method that updates only a small subset of parameters. Which fine-tuning approach should they use?

Accepted Answer

Parameter-Efficient Fine-Tuning (PEFT) like LoRA. Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA (Low-Rank Adaptation) are specifically designed to update only a small subset of parameters (e.g., low-rank matrices injected into transformer layers) while keeping the majority of the foundation model frozen. This drastically reduces memory and compute costs compared to full fine-tuning, making it the most cost-effective choice for customizing a model from Vertex AI Model Garden on a custom dataset.

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Full fine-tuning

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Prompt tuning

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Reinforcement Learning from Human Feedback (RLHF)

Question 13

Which TWO features are available in Vertex AI Agent Builder to enhance the conversational abilities of an agent? (Choose TWO.)

Accepted Answer

Slot filling. Slot filling is correct because it allows the agent to collect required parameters (slots) from the user during a conversation, enabling multi-turn interactions to fulfill complex requests. In Vertex AI Agent Builder, slot filling is a core feature for conversational agents, as it systematically prompts for missing information (e.g., date, location) until all necessary slots are filled, enhancing the agent's ability to handle dynamic user inputs.

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Sentiment analysis

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Code execution

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Knowledge base integration

Question 14

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.

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Number of model versions

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Number of model instances

Question 15

A developer receives the above JSON response from a Vertex AI language model. The output content is correct, but the developer expected the model to not answer geography questions. What should the developer do to prevent the model from responding to geography queries?

Accepted Answer

Add a system instruction to not answer geography questions. Vertex AI does not have a predefined safety filter category for 'Geography'. To prevent the model from answering geography questions, the developer should use a system instruction that explicitly tells the model not to respond to geography queries. System instructions are the appropriate mechanism for custom content restrictions, while safety filters are limited to predefined categories such as toxic, harassment, etc.

Answer

Adjust the safety filter thresholds for the 'Toxic' category

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Enable Vertex AI Grounding with a geography knowledge base

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Configure a safety filter for the 'Geography' category