Generative AI Leader Practice Test 5 — 25 Questions

Question 1

A multinational corporation is using Vertex AI to generate multilingual customer support responses. They have fine-tuned the Gemini model on support tickets in English and now want to extend to 10 additional languages. The fine-tuning dataset for new languages is small (1000 tickets each). During evaluation, the model performs well for common languages (Spanish, French) but poorly for languages like Finnish and Thai. The team needs to improve performance for low-resource languages. They have budget constraints and cannot collect more data quickly. Which approach should they take?

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

Accepted Answer

Use a multilingual foundation model and fine-tune with cross-lingual transfer learning techniques.. Using a multilingual foundation model (like Gemini's multilingual variant) with cross-lingual transfer learning leverages the model's pre-trained knowledge across languages, allowing it to generalize from high-resource languages (Spanish, French) to low-resource ones (Finnish, Thai) even with small fine-tuning datasets. This approach is budget-friendly as it avoids separate models or costly data collection, and it directly addresses the performance gap by sharing linguistic patterns across languages.

Answer

Switch to Vertex AI Codey API for generating responses in all languages.

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Deploy separate fine-tuned models for each language.

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Collect more training data for low-resource languages via crowdsourcing.

Question 2

A company wants to build a chatbot that can answer questions about its internal knowledge base using natural language. Which Google Cloud Generative AI offering should they use to quickly prototype and deploy this chatbot with minimal coding?

Accepted Answer

Generative AI Studio. Generative AI Studio provides a no-code/low-code environment to prototype and deploy chatbots with foundation models.

Answer

Vertex AI Endpoints

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Cloud Natural Language API

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Vertex AI Model Garden

Question 3

A company wants to use Generative AI for customer support chatbots. They are concerned about cost and latency. Which deployment option best balances these concerns?

Accepted Answer

Use a fine-tuned version of a smaller model on Vertex AI with response caching. Using a fine-tuned smaller model on Vertex AI with response caching reduces both cost and latency. Smaller models require fewer computational resources, and caching avoids redundant inference calls, directly addressing the company's concerns without sacrificing accuracy for the specific task.

Answer

Deploy an open-source model on-premise to avoid cloud costs

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Rely on a third-party chatbot API that abstracts the model

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Use the largest available foundation model via API for highest accuracy

Question 4

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.

Answer

Vertex AI Model Garden with fine-tuning

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Vertex AI Agent Builder with web search

Answer

Vertex AI Codey APIs

Question 5

A company wants to use GenAI to create a knowledge base assistant that can answer questions from internal documentation. They need the assistant to always have access to the latest documents without retraining. Which TWO services should they combine? (Choose 2)

Accepted Answer

Vertex AI Agent Builder. Vertex AI Agent Builder provides the conversational agent framework, and Vertex AI Vector Search enables retrieval from the latest documents using embeddings.

Answer

Vertex AI Pipelines

Answer

Vertex AI Model Garden

Answer

Duet AI in Google Docs

Question 6

A team wants to build a GenAI application that can interact with external APIs (e.g., to check inventory or place orders). Which Vertex AI component provides this capability?

Accepted Answer

Vertex AI Extensions. Extensions allow agents to call external APIs and perform actions beyond text generation. Agent Builder is the platform but Extensions provide the specific API integration. Model Garden and Grounding do not support API calls.

Answer

Grounding with Google Search

Answer

Model Garden

Answer

Vertex AI Agent Builder

Question 7

A product manager wants to add a feature that drafts meeting summaries automatically in Google Meet. Which Gemini for Google Workspace capability should they use?

Accepted Answer

Gemini for Workspace in Google Meet. Gemini for Workspace in Google Meet provides meeting summaries. Duet AI for Slides does not generate meeting summaries. Vertex AI Agent Builder and Model Garden are not directly integrated into Meet.

Answer

Vertex AI Model Garden

Answer

Vertex AI Agent Builder

Answer

Duet AI in Google Slides

Question 8

A healthcare organization wants to use generative AI for medical report summaries. What is the primary concern?

Accepted Answer

Ensuring HIPAA compliance and data security when using cloud AI services. The primary concern for a healthcare organization using generative AI for medical report summaries is ensuring HIPAA compliance and data security when using cloud AI services. Medical data is protected health information (PHI), and any cloud-based AI service must have a Business Associate Agreement (BAA) in place and enforce encryption at rest and in transit to avoid regulatory penalties and data breaches.

Answer

The model's ability to generate fluent and coherent summaries

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Minimizing the cost of each API call to stay within budget

Answer

Latency of responses for real-time use cases

Question 9

A marketing agency wants to use Vertex AI to automatically generate social media posts for clients. They plan to use the Gemini API with few-shot prompting. The agency's developers have limited experience with generative AI and want the fastest way to prototype and iterate on prompts. They are already using Google Cloud for other services. Which approach should they take to quickly develop and test prompts?

Accepted Answer

Use Vertex AI Studio (Gen AI Studio) to design and test prompts interactively.. Vertex AI Studio (Gen AI Studio) is the correct choice because it provides a no-code, interactive environment specifically designed for rapid prompt engineering and iteration with Gemini models. It allows developers with limited generative AI experience to test few-shot prompts, adjust parameters, and see results immediately without writing code, making it the fastest path from concept to working prototype within the Google Cloud ecosystem.

Answer

Use a third-party platform like OpenAI Playground and migrate later.

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Use Google Cloud Shell to invoke the model via curl commands.

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Write Python scripts using the Vertex AI SDK and run them in Airflow.

Question 10

A company is considering using gen AI for customer support. Which two business strategies are most important for success?

Accepted Answer

Measure customer satisfaction metrics. Measuring customer satisfaction metrics (A) is critical because it provides quantitative feedback on the generative AI system's performance, enabling iterative improvements to the model's responses and alignment with business goals. Without metrics like CSAT or NPS, the company cannot validate whether the AI is reducing resolution time or improving user experience, which are key ROI indicators for gen AI deployments.

Answer

Ignore data privacy

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Deploy without testing

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Use the cheapest model

Question 11

A developer wants to use Google Cloud generative AI to build a multimodal application that can answer questions about images and text. Which TWO services are most appropriate?

Accepted Answer

Vertex AI. Vertex AI is correct because it provides a unified platform for building, deploying, and managing multimodal generative AI models, including support for both image and text inputs. It offers access to foundational models like Gemini Pro Vision, enabling developers to create applications that answer questions about images and text through a single API endpoint.

Answer

Chirp

Answer

Imagen

Answer

Codey

Question 12

A healthcare organization deploys a generative AI model to assist in diagnosing rare diseases from medical images. To comply with the EU AI Act's requirements for high-risk AI systems, what is the MOST critical control they must implement?

Accepted Answer

Implement a human-in-the-loop review process with the ability to override AI recommendations. The EU AI Act requires human oversight for high-risk AI systems, including the ability for humans to override or stop the system's decisions.

Answer

Use a model with a high F1 score to minimize the need for human review

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Ensure the model is trained on synthetic data only to avoid privacy concerns

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Deploy the model in a sandbox environment without real patient data

Question 13

A company wants to generate images from text descriptions. Which model in Vertex AI Model Garden should they use?

Accepted Answer

Imagen. Imagen is the correct choice because it is a text-to-image model in Vertex AI Model Garden specifically designed to generate high-fidelity images from natural language descriptions. Unlike the other options, Imagen uses a diffusion-based architecture to create photorealistic visuals, making it the only option that directly addresses the requirement of generating images from text.

Answer

Chirp

Answer

Codey

Answer

PaLM 2

Question 14

An enterprise deploys a large language model (LLM) for internal document summarization. Users complain that summaries sometimes include statements not present in the original document. Which mitigation strategy should the team prioritize to address this hallucination issue?

Accepted Answer

Implement retrieval-augmented generation (RAG) to ground the model in the original documents and require citations.. Retrieval-Augmented Generation (RAG) is the most direct and effective mitigation for hallucination in document summarization because it forces the LLM to base its output on retrieved chunks of the original document. By requiring citations, the model must reference specific passages, making it verifiable and reducing the likelihood of fabricating content. This grounds the generation in the source material, addressing the root cause of hallucination—lack of factual grounding—rather than relying on post-hoc correction or output tuning.

Answer

Train a discriminator model to detect hallucinations and perform adversarial training.

Answer

Apply reinforcement learning from human feedback (RLHF) using a reward model that penalizes hallucinations.

Answer

Reduce the model's temperature parameter to 0 to make outputs deterministic.

Question 15

A company wants to start using Gemini for Google Workspace to improve productivity. Which TWO applications can provide meeting summarization features? (Choose 2)

Accepted Answer

Google Chat. Google Meet and Google Chat both offer meeting summarization features powered by Gemini. In Google Meet, Gemini can automatically generate meeting notes and summaries after a recorded meeting. In Google Chat, Gemini can summarize conversations and meetings that occurred within a space, providing a concise recap of key points and action items.

Answer

Google Docs

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Google Sheets

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Gmail

Question 16

A company wants to measure the business impact of a GenAI content generation tool. Which metric is most appropriate?

Accepted Answer

Reduction in content production time. The primary business impact of a GenAI content generation tool is operational efficiency, measured by the reduction in content production time. This metric directly correlates to cost savings and faster time-to-market, which are key business outcomes. Unlike technical metrics, it reflects real-world value delivery.

Answer

Number of model parameters

Answer

Model accuracy on a test set

Answer

Training loss

Question 17

A researcher wants to quickly prototype an AI model using a free, hosted Jupyter notebook environment with GPU support. Which Google product is BEST suited for this?

Accepted Answer

Google Colab. Google Colab provides free hosted Jupyter notebooks with GPU support. Kaggle is for datasets and competitions but not primarily for notebooks. Vertex AI Workbench and AI Platform are enterprise tools with costs.

Answer

AI Platform Notebooks

Answer

Kaggle Notebooks

Answer

Vertex AI Workbench

Question 18

A developer is using the Vertex AI Gemini API to generate product descriptions. They get a 400 error 'INVALID_ARGUMENT: The model's maximum input token limit is 8192.' What is the most likely issue?

Accepted Answer

The prompt is too long. The 400 error 'INVALID_ARGUMENT: The model's maximum input token limit is 8192' explicitly indicates that the combined token count of the prompt (system instructions, user input, and any conversation history) exceeds the 8192-token context window of the Gemini model being used. This is a hard limit enforced by the Vertex AI Gemini API, and the error is triggered before any generation begins. Therefore, the most likely issue is that the prompt is too long.

Answer

The API key is invalid

Answer

The output tokens are too high

Answer

The model is not available in the region

Question 19

A healthcare provider plans to implement gen AI for clinical note summarization. They have limited AI expertise. Which Google Cloud approach best aligns with their business strategy?

Accepted Answer

Use Vertex AI Agent Builder with pre-built templates. Vertex AI Agent Builder provides pre-built templates and a low-code interface specifically designed for organizations with limited AI expertise. It enables rapid deployment of generative AI solutions like clinical note summarization without requiring deep data science skills, directly aligning with the healthcare provider's business strategy of minimizing technical overhead while leveraging AI.

Answer

Hire a team of data scientists

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Deploy an open-source model on Compute Engine

Answer

Build a custom model from scratch

Question 20

A company is evaluating whether to build a custom fine-tuned model for code generation or use a pre-built API like Gemini API. The code generation needs to follow the company's internal coding standards. Which consideration is MOST important in deciding to build vs buy?

Accepted Answer

Pre-built APIs may not generate code that adheres to internal standards without extensive prompting. Fine-tuning allows the model to learn company-specific coding standards, which pre-built APIs cannot guarantee. Cost and latency are secondary; data privacy is important but can be addressed with both options.

Answer

Pre-built APIs are always cheaper than fine-tuned models

Answer

Fine-tuned models have lower latency than pre-built APIs

Answer

Data privacy is easier to achieve with pre-built APIs