Generative AI Leader Practice Test 16 — 15 Questions

Question 1

Which Google Cloud offering allows you to create a machine-readable document that describes a model's intended use, performance, and limitations?

Accepted Answer

Model Cards. Model Cards are structured documents that describe a machine learning model's intended use, performance metrics, limitations, and ethical considerations. They are designed to be machine-readable and provide transparency for stakeholders. Google Cloud's Model Cards are part of Vertex AI and can be generated and stored alongside models in the Model Registry.

Answer

People + AI Guidebook

Answer

Vertex AI Model Registry

Answer

Datasheets for Datasets

Question 2

A developer is building a mobile app that needs to run an AI model on-device for low-latency inference even without internet. Which Gemini model variant is designed for on-device deployment?

Accepted Answer

Gemini Nano. Gemini Nano is Google's smallest Gemini model variant, specifically designed to run locally on devices such as smartphones and laptops for on-device inference without network connectivity. It powers features like on-device summarization in Pixel devices and is optimized for low-latency, offline AI workloads.

Answer

Gemini Pro

Answer

Gemini Ultra

Answer

Gemini Flash

Question 3

An organization wants to use AI-generated images commercially. According to Google's AI principles and copyright guidelines, what should they do FIRST?

Accepted Answer

Verify the training data provenance and licensing of the foundation model. The primary risk is copyright infringement from training data. Verifying that the model's training data was properly licensed is essential before using outputs commercially.

Answer

Assume all AI-generated content is copyright-free

Answer

Only use images generated by models trained on public domain data

Answer

Add a copyright symbol to all AI-generated images

Question 4

A company is building a chatbot that must answer questions based on a large internal knowledge base that is updated weekly. They want to avoid retraining the model frequently. Which technique should they use?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with a vector database. RAG retrieves relevant documents at inference time, keeping answers up-to-date without retraining. Fine-tuning would require frequent retraining; prompt engineering alone cannot incorporate new knowledge.

Answer

Use prompt engineering to instruct the model to ignore outdated information

Answer

Increase the model's context window and include all documents in the prompt

Answer

Fine-tune the model weekly on the updated knowledge base

Question 5

A company is establishing governance practices for generative AI models. Which three actions are essential for responsible AI deployment?

Accepted Answer

Use model versioning to track changes.. Model versioning (Option A) is essential because it enables tracking of changes to generative AI models over time, ensuring reproducibility, rollback capability, and compliance with governance policies. Without versioning, it becomes impossible to audit which model produced a specific output, undermining accountability and regulatory adherence.

Answer

Monitor for data leakage from training data.

Answer

Open-source the model to ensure transparency.

Question 6

A data science team is choosing between Gemini Pro and Gemini Flash for a real-time content moderation API. They need low cost and low latency, but can tolerate slightly lower accuracy. Which TWO Gemini variants should they consider?

Accepted Answer

Gemini Flash. Gemini Flash (B) is correct because it is the latency-optimized, lower-cost model tier designed for high-throughput, real-time tasks where slightly reduced accuracy is acceptable, matching the team's priority of low latency and low cost. Gemini Nano (A) is also a low-cost, low-latency variant, but it is intended for on-device/local use cases rather than a server-side real-time API, so it is not the best fit here. Gemini Pro (E), Gemini 1.5 Pro (C), and Gemini Ultra (D) are excluded because they are higher-capability, higher-cost, higher-latency models that conflict with the stated low-cost and low-latency requirements. Because only Flash clearly satisfies all constraints, the question should be revised to have a single correct answer or to use distinct, non-overlapping model options.

Answer

Gemini Nano

Answer

Gemini 1.5 Pro

Answer

Gemini Ultra

Answer

Gemini Pro

Question 7

Which Google DeepMind technology can be used to embed an invisible watermark into AI-generated images to help identify their origin?

Accepted Answer

SynthID. SynthID is Google DeepMind's tool for watermarking AI-generated content. The other options are unrelated.

Answer

TensorFlow Privacy

Answer

PAIR Explorables

Answer

What-If Tool

Question 8

A data science team wants to build a custom model for generating product descriptions that adhere to specific brand guidelines. They have 5,000 high-quality examples. Which approach balances cost and accuracy?

Accepted Answer

Fine-tune a foundation model (e.g., PaLM 2) using Vertex AI Model Garden. Fine-tuning a foundation model on the examples yields high accuracy with moderate cost. Training from scratch is overkill; prompt engineering may not capture all nuances.

Answer

Use a pre-built API with prompt engineering and few-shot examples

Answer

Use Vertex AI Agent Builder with a custom prompt

Answer

Train a model from scratch using TensorFlow on Vertex AI

Question 9

A company wants to implement a change management strategy for GenAI adoption. Which TWO actions are MOST effective in driving adoption among employees?

Accepted Answer

Appoint AI champions in each department to advocate and assist. Training programs build competence; AI champions provide peer support. Mandatory adoption and tool removal are counterproductive.

Answer

Remove existing tools to force adoption

Answer

Limit access to GenAI tools to senior leadership only

Answer

Make the use of GenAI tools mandatory for all tasks

Question 10

A company is building a legal document review assistant using Gemini 1.5 Pro. They want to ensure the model can handle large documents of up to 500 pages in a single prompt. Which feature of Gemini 1.5 Pro is MOST important for this requirement?

Accepted Answer

Large context window. Gemini 1.5 Pro has a large context window (up to 1 million tokens), allowing it to process large documents. Temperature, top-k, and fine-tuning do not directly address context length.

Answer

Temperature

Answer

Top-k sampling

Answer

Fine-tuning

Question 11

An e-commerce company wants to generate realistic product images from text descriptions using Google Cloud AI. Which service should they use?

Accepted Answer

Imagen on Vertex AI. Imagen on Vertex AI is the correct service because it is specifically designed for text-to-image generation, allowing the e-commerce company to create realistic product images from textual descriptions. It leverages Google's advanced diffusion models to produce high-fidelity visuals, making it the ideal choice for this use case.

Answer

Gemini 1.5 Pro

Answer

Chirp

Answer

Vertex AI Codey

Question 12

A company uses a Gemini 1.5 Pro model with a 1 million token context window. They want to process a large 500-page PDF for Q&A. What is the MAIN advantage of using the long context window over a RAG approach?

Accepted Answer

Simpler architecture with no need for chunking or retrieval systems. The primary advantage of using a 1 million token context window is architectural simplicity. By ingesting the entire 500-page PDF as a single prompt, the company eliminates the need for document chunking, embedding generation, and a retrieval system (RAG). This reduces system complexity, maintenance overhead, and potential failure points, as the model can directly attend to all content in one pass.

Answer

Lower cost per API call

Answer

Faster inference speed

Answer

Higher accuracy for all queries

Question 13

A media company wants to use a generative AI model to create marketing copy that includes citations to original sources. Which feature should they enable to ensure the model provides accurate attributions?

Accepted Answer

Grounding. Grounding allows the model to cite sources, improving explainability and trustworthiness by connecting outputs to verifiable information.

Answer

Confidence indicators

Answer

Chain-of-thought reasoning

Answer

Safety filters

Question 14

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 15

The exhibit shows the deployment configuration for a conversational AI model used in a finance application. Users report that responses are creative but often contain factually incorrect financial advice. Which parameter change would most improve factual accuracy?

Accepted Answer

Add grounding sources, such as "EnterpriseSearch" or "Web". Adding grounding sources like EnterpriseSearch or Web provides the model with access to authoritative, up-to-date financial data, which directly reduces hallucinations by anchoring responses in verified facts rather than relying solely on the model's parametric knowledge. This is the most effective technique for improving factual accuracy in a domain where correctness is critical.

Answer

Lower temperature to 0.1

Answer

Increase topP to 1.0

Answer

Increase maxOutputTokens to 1024