Generative AI Leader Google Cloud's Generative AI Offerings — Timed 40 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

Question 11

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.

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The display name contains a hyphen which is not allowed

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The container image URI is incorrect

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The region us-central1 does not support TensorFlow models

Question 12

Why is the model responding in English despite the prompt asking for French translation?

Accepted Answer

The system instruction to translate to French was not set; the user prompt alone is not sufficient. Option C is correct because in Google Cloud's Vertex AI and Generative AI offerings, the system instruction is a separate, persistent directive that sets the model's behavior, such as language output. The user prompt alone, even if it asks for a French translation, is not sufficient to override the default language of the model; the system instruction must explicitly specify the target language. Without this instruction, the model defaults to its training language (typically English), regardless of the user's request.

Answer

The model endpoint is configured for English only

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The temperature is too high, causing random outputs

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The maxOutputTokens is too low to complete the translation

Question 13

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 14

A financial services firm needs to deploy a large language model (LLM) for analyzing sensitive client documents. They require the model to run within their Virtual Private Cloud (VPC) with no internet access and must comply with data residency regulations. Which Google Cloud generative AI offering should they use?

Accepted Answer

Vertex AI Model Garden with private endpoints and VPC Service Controls. Option A is correct because Vertex AI Model Garden with private endpoints and VPC Service Controls allows the LLM to be deployed entirely within the customer's VPC, with no internet egress, and enforces data residency by restricting data movement to the configured VPC boundary. Private endpoints use Private Service Connect to route inference traffic through internal IPs, while VPC Service Controls prevent data exfiltration and ensure compliance with residency regulations.

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Vertex AI Search

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Cloud Run

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Vertex AI Workbench

Question 15

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. Option C is correct because 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.

Answer

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 16

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

Add an 'Escalation' intent that triggers a human handoff. Agent Builder supports 'Escalation' intent to hand off to human agents. Option B is wrong because fallback intent is for unrecognized input but not necessarily human handoff. Option C is wrong because confirmation is for confirming actions. Option D is wrong because slot filling is for collecting parameters.

Answer

Configure 'Slot filling' to collect more info

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Implement a 'Confirmation' prompt for the user

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Use a 'Fallback' intent to route to a human

Question 17

A company is using Vertex AI for multimodal generative AI to analyze images and text. They need to ensure that the model's outputs are auditable and can be traced back to the input data. Which feature should they enable?

Accepted Answer

Vertex AI Model Monitoring with Explainable AI. Option D is correct because Vertex AI Model Monitoring with Explainable AI provides feature attributions that map model predictions back to specific input features (e.g., pixels in images or tokens in text). This creates an auditable trail by quantifying how each input contributed to the output, enabling traceability for compliance and debugging. The other options lack the direct input-to-output attribution required for auditability.

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Vertex AI Feature Store

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Vertex AI Experiments

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Cloud Logging

Question 18

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. Option C is correct because 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 19

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 20

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