Generative AI Leader Practice Test 9 — 10 Questions

Question 1

A global company deploys a generative AI chatbot in the European Union. They must ensure compliance with GDPR regarding user data used for fine-tuning the model. What is the MOST important requirement they must fulfill?

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

Accepted Answer

Obtain explicit consent from data subjects for using their data in fine-tuning. Under GDPR, using personal data for fine-tuning a generative AI model constitutes a new processing purpose that requires a lawful basis. Explicit consent (Article 7 and Article 9) is the most robust basis when relying on consent, as it must be freely given, specific, informed, and unambiguous. Without explicit consent, the company risks violating data minimization and purpose limitation principles, even if other anonymization or storage measures are applied.

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Use only synthetic data for fine-tuning to avoid GDPR issues

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Store all fine-tuned models only on US-based servers

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Anonymize all data before fine-tuning, regardless of consent

Question 2

A company wants to generate images from text descriptions using Google Cloud. Which service should they use?

Accepted Answer

Vertex AI Imagen. Vertex AI Imagen is Google Cloud's purpose-built service for generating high-fidelity images from text descriptions using diffusion models. It directly addresses the requirement of text-to-image generation, offering capabilities like image editing, upscaling, and style transfer, which are not available in other Vertex AI or Vision services.

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Vertex AI Gemini

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Cloud Vision API

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AutoML Vision

Question 3

A company wants to use a pre-trained language model for customer support summarization. They need to ensure responses are concise and accurate. Which prompt engineering technique is most effective?

Accepted Answer

Few-shot prompting with examples. Few-shot prompting (B) is most effective because it provides the model with a small set of example input-output pairs (e.g., a customer query and its concise summary), which guides the model to produce outputs that match the desired format, length, and accuracy. This technique is particularly useful for summarization tasks where consistency and adherence to a specific style are critical, as it reduces ambiguity without requiring fine-tuning.

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Zero-shot prompting

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Chain-of-thought prompting

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Negative prompting

Question 4

Which Google Cloud service provides a managed environment for prompt engineering and model evaluation?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio is the correct answer because it is a managed service within Vertex AI specifically designed for prompt engineering, model tuning, and evaluation of generative AI models. It provides a no-code interface for testing prompts, comparing model outputs, and iterating on prompt design, directly supporting the workflow described in the question.

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AI Platform Notebooks

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Dialogflow CX

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Cloud Composer

Question 5

During model evaluation, a team observes good performance on training data but poor on validation data. Which regularization technique is most appropriate to address this?

Accepted Answer

Apply dropout. The scenario describes overfitting, where the model memorizes training data but fails to generalize to unseen validation data. Dropout is a regularization technique that randomly deactivates a fraction of neurons during training, forcing the network to learn more robust features and reducing co-adaptation, which directly mitigates overfitting.

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Add more training data

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Increase the learning rate

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Use a larger batch size

Question 6

A startup develops a generative AI tool for legal document review. To ensure explainability, they want the model to cite specific clauses from source documents when making assertions. Which technique should they use?

Accepted Answer

Grounding using a retrieval system that provides source documents. Grounding in generative AI means the model cites verifiable sources for its outputs, which is essential for explainability in domains like law.

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Fine-tuning on legal documents with citation examples

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Chain-of-thought prompting

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Prompt engineering to ask for citations

Question 7

You are a generative AI lead at a healthcare startup developing a system to summarize patient medical records for quick review by doctors. The system uses a fine-tuned LLM. After deployment, doctors report that the summaries often miss critical details like medication dosages and allergy information. The current pipeline preprocesses patient records by extracting text from EHR, feeding it to the LLM, and outputting a summary. The team has limited time and budget. They cannot retrain the model because it is hosted as a managed API. Which action should you take to most effectively improve the summarization quality without changing the model?

Accepted Answer

Revise the prompt to explicitly ask for medication dosages and allergies, and format the input text by adding headings (e.g., '### Medications') to emphasize important sections.. Prompt engineering is the most effective and cost-efficient way to improve LLM output without retraining or changing the model. By explicitly instructing the model to include medication dosages and allergies, and by structuring the input with clear headings, you guide the model's attention to critical sections, directly addressing the missing details. This approach leverages the LLM's existing capabilities and requires no changes to the hosted API or additional infrastructure.

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Increase the maximum output token limit to force the model to include more details.

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Replace the LLM with a simpler extractive summarization model that selects sentences from the original document.

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Implement a retrieval-augmented generation (RAG) system that pulls supplementary data from external drug databases.

Question 8

A financial services firm needs to deploy a generative AI model that generates reports from structured and unstructured data. The solution must ensure that outputs never contain sensitive customer information. Which combination of Google Cloud services should they use?

Accepted Answer

Vertex AI Gemini API with DLP integration + IAM roles + VPC Service Controls. Vertex AI's DLP integration can inspect and redact PII from prompts and responses. Combined with IAM roles and VPC-SC, this provides comprehensive data protection. The other options either lack DLP integration or do not cover both prompt and response inspection.

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Vertex AI Gemini API + Cloud Key Management Service (KMS) + Secret Manager

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Vertex AI Gemini API + Cloud Data Loss Prevention (DLP) standalone + Cloud NAT

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Vertex AI Gemini API + Cloud DLP + Cloud Armor

Question 9

A company is fine-tuning a Gemma model using Vertex AI. They observe that the model overfits. Which TWO actions should they take to mitigate overfitting?

Accepted Answer

Use more diverse data. Introducing more diverse data helps the model generalize better by exposing it to a wider variety of patterns, reducing the risk of memorizing noise from a limited dataset. Option E is correct because dropout randomly deactivates a fraction of neurons during fine-tuning, which prevents co-adaptation and acts as a regularization technique to combat overfitting in transformer-based models like Gemma.

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Use a larger batch size

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Increase the number of training epochs

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Reduce the learning rate

Question 10

During a RAG pipeline implementation, the retrieval system frequently returns irrelevant documents, causing the generator to produce incorrect answers. Which change is most likely to improve the relevance of retrieved documents?

Accepted Answer

Add a re-ranking step using a cross-encoder model to refine the top results.. Re-ranking with a cross-encoder model evaluates query-document pairs more deeply, improving precision at the cost of latency. Increasing the number of documents may introduce more noise. Changing embedding dimension or chunk size may help but are less targeted.

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Increase the number of documents retrieved from the vector store.

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Use a different embedding model with higher vector dimension.

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Decrease the chunk size of documents to reduce noise.