Generative AI Leader Practice Test 43 — 15 Questions

Question 1

A startup is building a generative AI tool that helps users write code. They want to launch quickly but need to ensure the generated code is secure and does not introduce vulnerabilities. They have a small team of developers with some ML experience. The tool should be cloud-hosted. Which approach balances speed, security, and cost?

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

Accepted Answer

Use a pre-trained code model (e.g., Codey) and add a security filtering layer. Using a pre-trained code model like Codey with a security filtering layer offers the best balance: rapid deployment via an existing model, built-in safety checks to mitigate vulnerabilities, and cost-effectiveness by avoiding custom training. Option A (manual review) is unscalable and insecure. Option B (training from scratch) is too slow and expensive. Option D (restricting outputs) limits utility.

Answer

Deploy the tool without any security checks and rely on manual review

Answer

Train a custom code generation model from scratch on a large dataset

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Use a smaller model and restrict outputs to only simple code patterns

Question 2

A company is evaluating the ROI of implementing GenAI for code generation. Which metric BEST captures the productivity improvement of developers?

Accepted Answer

Time saved per development task (e.g., from 2 hours to 30 minutes). Time saved per task directly measures productivity. Code quality is an improvement metric, but time saved is the primary productivity indicator.

Answer

Percentage of code that passes unit tests on the first attempt

Answer

Number of lines of code generated per day

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Number of bugs found in production after code review

Question 3

A company uses a text generation model for customer support but notices it occasionally provides outdated information. Which technique should they implement to improve output accuracy?

Accepted Answer

Implement retrieval-augmented generation (RAG). Retrieval-augmented generation (RAG) is the correct technique because it grounds the model's output in real-time, external knowledge sources (e.g., a vector database or document index) rather than relying solely on static training data. This directly addresses the problem of outdated information by allowing the model to retrieve and synthesize current facts at inference time, ensuring accuracy without requiring retraining.

Answer

Increase max output tokens

Answer

Fine-tune the model with more historical support data

Answer

Increase model temperature to 1.0

Question 4

A company wants to create a GenAI application that can answer questions about their internal policy documents, but they require that the model's responses are grounded in the actual documents to avoid hallucination. Which Vertex AI feature should they use?

Accepted Answer

Grounding with Google Search (or enterprise data sources). Grounding with Google Search or enterprise data sources ensures responses are based on verified sources. RAG Engine is a broader tool for building RAG applications. Vertex AI Studio is for prompt design. Agent Builder is for conversational agents but may not provide grounding directly.

Answer

Vertex AI Studio

Answer

Vertex AI Agent Builder

Answer

Model Garden

Question 5

A company is using a fine-tuned LLM for generating technical support responses. After deployment, they notice that the model sometimes produces incorrect but plausible-sounding answers (hallucinations). They have a large repository of verified technical manuals. Which technique would BEST reduce hallucinations while minimizing the need for additional training?

Accepted Answer

Implement RAG by indexing the verified manuals and retrieving relevant sections during inference. RAG with the verified manuals as the knowledge base allows the model to ground its responses in authoritative sources without retraining. Fine-tuning might still hallucinate if the data is insufficient. Prompt engineering alone cannot eliminate hallucinations. Using a larger context window may help but is not as reliable as RAG.

Answer

Increase the temperature parameter to make the model more conservative

Answer

Use a larger base model with a longer context window

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Fine-tune the model again with a larger dataset of verified responses

Question 6

Which Google Cloud AI service is specifically designed for extracting structured data from scanned documents, such as invoices and receipts?

Accepted Answer

Document AI. Document AI is the correct answer because it is purpose-built for understanding and extracting structured data from unstructured documents like invoices, receipts, and forms. It uses specialized processors (e.g., the Invoice Parser or Expense Parser) that combine optical character recognition (OCR) with natural language understanding and machine learning models trained on document layouts, enabling it to output structured fields such as vendor name, total amount, and line items.

Answer

Natural Language AI

Answer

Translation AI

Answer

Vision AI

Question 7

A fintech company is deploying a generative AI system that offers investment advice. To comply with regulations and Google's AI Principles, they need to ensure appropriate human oversight and transparency. Which two actions should they take? (Choose two.)

Accepted Answer

Require a human advisor to review and approve all AI-generated recommendations. Human review for high-stakes decisions (C) ensures accountability, and providing model cards (D) ensures transparency about capabilities and limitations. Publishing a Model Card is a key practice to comply with Google's AI Principles and regulatory requirements for transparency.

Answer

Use only synthetic data for training to avoid privacy issues

Answer

Allow the AI to execute trades automatically without human intervention

Answer

Disable logging of all interactions to maximize user privacy

Question 8

A research team at a university is exploring protein folding predictions. They need access to state-of-the-art models and large-scale computational resources at no cost for academic use. Which Google Cloud offering should they use?

Accepted Answer

Colab notebooks with AlphaFold. Google DeepMind's AlphaFold provides cutting-edge protein structure predictions. Colab offers free GPU notebooks, and AlphaFold is available as a Colab notebook for academic use.

Answer

Google Cloud TPU v5e pods via Cloud TPU API

Answer

Vertex AI Model Registry with AlphaFold as a pre-trained model

Answer

BigQuery ML with a custom protein folding model

Question 9

A company is planning an iterative rollout of a GenAI code generation tool for developers. They want to ensure adoption and minimize resistance. Which THREE change management practices are most effective?

Accepted Answer

Start with a small pilot group and gather feedback before expanding. Identifying AI champions, providing hands-on training, and starting with a small pilot group are proven strategies for AI adoption.

Answer

Immediately replace existing code review processes with AI-generated reviews

Answer

Monitor only the number of prompts submitted as the success metric

Question 10

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 11

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 12

A startup wants to build a text-to-speech application for generating audiobooks. Which Google Cloud generative AI service is best suited for this task?

Accepted Answer

Chirp. Chirp is Google Cloud's text-to-speech AI model. Imagen generates images, Codey generates code, and Gemini is a multimodal model not specialized for high-quality speech synthesis.

Answer

Imagen

Answer

Codey

Answer

Gemini

Question 13

What is the primary advantage of using embeddings and vector search for semantic search over traditional keyword search?

Accepted Answer

Ability to find documents with similar meaning even without exact keyword matches. Embeddings and vector search capture semantic meaning by converting text into high-dimensional vectors, enabling retrieval of documents with similar meaning even when they lack exact keyword matches. This is the primary advantage over traditional keyword search, which relies on literal term matching and fails with synonyms, paraphrases, or conceptual similarity.

Answer

Faster retrieval speed

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Lower storage requirements

Answer

No need for indexing

Question 14

A company is deploying a Gemini-based application and needs to ensure low latency for real-time user interactions. They also want to reduce cost. Which THREE strategies should they consider? (Select 3)

Accepted Answer

Use Gemini 1.5 Flash instead of Pro. Gemini 1.5 Flash is a lighter, distilled version of the Pro model, designed for lower latency and reduced computational cost while still maintaining strong performance for real-time interactions. Flash models use fewer parameters and optimized inference paths, making them ideal for latency-sensitive applications where cost efficiency is critical.

Answer

Increase the model's max output tokens to ensure comprehensive answers

Answer

Use full fine-tuning to make the model faster

Question 15

A company uses Vertex AI to deploy a generative model for customer support. They need to ensure that the model does not produce harmful content when responding to user queries. Which of the following is the MOST effective approach?

Accepted Answer

Use Vertex AI's safety filters and adjust safety settings per category. Vertex AI's safety filters and adjustable safety settings are the built-in, most effective way to block harmful content. The other options are either insufficient or place undue burden on users.

Answer

Instruct customers to report harmful responses for future model updates

Answer

Prompt the model to 'be safe' and rely on its training to avoid harmful content

Answer

Review all responses manually before sending to customers