Generative AI Leader Practice Test 22 — 15 Questions

Question 1

A data scientist is fine-tuning a foundation model for a specialized legal document summarization task. The labeled dataset is only 5,000 examples. Which fine-tuning technique would be MOST efficient to adapt the model without catastrophic forgetting and with minimal computational cost?

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

Accepted Answer

Low-Rank Adaptation (LoRA). LoRA (Low-Rank Adaptation) is an adapter-based method that trains only a small number of added parameters, making it efficient and less prone to catastrophic forgetting compared to full fine-tuning. Supervised fine-tuning full model is expensive; RLHF is for alignment after fine-tuning; in-context learning requires no training but may not suffice.

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Reinforcement Learning from Human Feedback (RLHF)

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Full supervised fine-tuning of all model parameters

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In-context learning with few-shot examples

Question 2

A multimodal generative AI system processes both image and text inputs to produce captions. During inference, the image encoder sometimes produces noisy or missing features. Which architectural design decision best handles such input degradation without retraining?

Accepted Answer

Introduce a gating mechanism that learns to weigh image features based on confidence scores from the encoder.. A gating mechanism dynamically adjusts the contribution of image features based on confidence scores from the encoder, allowing the model to gracefully handle noisy or missing features without retraining. This architectural design learns to suppress unreliable image inputs and rely more on text or other modalities, ensuring robust caption generation under input degradation.

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Train a separate variational autoencoder to produce a clean latent representation from the noisy image.

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Increase the image encoder’s capacity to better extract robust features.

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Apply standard image preprocessing (e.g., denoising) to all inputs before feeding to the encoder.

Question 3

A company is using a generative AI model to generate product descriptions. They notice the outputs often include factual inaccuracies about product specifications. Which technique would best address this issue without modifying the model's architecture?

Accepted Answer

Implement a Retrieval-Augmented Generation (RAG) pipeline that retrieves product specs from a database. Retrieval-Augmented Generation (RAG) is the correct technique because it grounds the model's output in factual, up-to-date product specifications retrieved from an external database. This directly addresses factual inaccuracies without modifying the model's architecture, as the model generates text based on retrieved context rather than relying solely on its parametric knowledge.

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Decrease the temperature parameter to 0.1

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Increase the max output tokens to 1024

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Use few-shot prompting with 5 examples of correct descriptions

Question 4

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.

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Increase max output tokens

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Fine-tune the model with more historical support data

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Increase model temperature to 1.0

Question 5

A research lab is fine-tuning a large language model on a small dataset of medical records. They observe that the model overfits, memorizing specific patient details and producing outputs that violate privacy regulations. Which technique should they apply to improve generalization and reduce memorization?

Accepted Answer

Apply differential privacy (DP-SGD) during fine-tuning. Differential privacy (DP-SGD) is the correct technique because it directly addresses memorization of sensitive patient data by adding calibrated noise to the gradient updates during fine-tuning. This bounds the model's ability to encode any single individual's information, improving generalization and ensuring compliance with privacy regulations like HIPAA.

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Increase the batch size to 64

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Increase the number of training epochs

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Use early stopping based on validation loss

Question 6

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 7

A company is deploying a GenAI contract analysis system that processes confidential legal documents. They need to ensure that the model does not retain or train on customer data. Which configuration is REQUIRED?

Accepted Answer

Opt out of model logging and data retention in the API settings. The primary requirement is to prevent the GenAI model from retaining or training on confidential legal documents. In Google Cloud's Vertex AI, customers can configure data governance settings to disable model logging and data retention, ensuring that prompts and responses are not stored or used for model improvement. This configuration directly addresses the compliance need for data confidentiality in contract analysis.

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Select a model with a context window large enough to hold the entire contract

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Use a public model endpoint with data encryption in transit

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Use a smaller model to reduce the risk of data memorization

Question 8

A startup is deciding between using a pre-trained model via API vs. hosting their own open-source model. Which factor is most critical for their decision?

Accepted Answer

Total cost of ownership including infrastructure and expertise. Total cost of ownership (TCO) is the most critical factor because it encompasses not only the direct costs of infrastructure (compute, storage, networking) but also the hidden costs of expertise (MLOps engineers, security hardening, ongoing maintenance) and opportunity costs. A pre-trained API may have higher per-token costs but lower upfront investment, while self-hosting an open-source model requires significant capital expenditure on GPUs, cooling, and power, plus the operational burden of scaling inference under variable load. This decision directly impacts the startup's burn rate and runway, making TCO the primary driver for a resource-constrained organization.

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The accuracy on a benchmark dataset

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The number of parameters in the model

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The level of community support for the open-source model

Question 9

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 is the correct answer because it is the native, integrated AI capability within Google Workspace that provides meeting summaries, note-taking, and action item extraction directly in Google Meet. This feature, often branded as 'Take notes for me' or 'Gemini notes,' automatically generates a summary after a meeting and shares it with attendees, requiring no custom development. It is purpose-built for exactly this use case, making it the most direct and appropriate choice for a product manager seeking an out-of-the-box solution.

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Vertex AI Model Garden

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Vertex AI Agent Builder

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Duet AI in Google Slides

Question 10

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.

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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 11

A data scientist wants to run ML models directly on their BigQuery data without moving data out. Which THREE statements about BigQuery ML are correct? (Choose 3)

Accepted Answer

BigQuery ML supports binary logistic regression models. BigQuery ML lets data scientists build and evaluate models directly on data in BigQuery using SQL, without exporting data. It supports binary logistic regression via CREATE MODEL with OPTIONS(model_type='LOGISTIC_REG') for classification, linear regression via OPTIONS(model_type='LINEAR_REG') for forecasting and regression, and recommendation systems via matrix factorization with OPTIONS(model_type='MATRIX_FACTORIZATION'). All three can be trained and evaluated in place using standard SQL.

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BigQuery ML supports deep neural network models like CNNs and RNNs

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BigQuery ML requires data to be exported to Cloud Storage before training

Question 12

What is the main advantage of using a model with a larger context window?

Accepted Answer

Ability to process longer documents or conversations without truncation. A larger context window allows the model to process and retain more tokens in a single pass, enabling it to handle longer documents, extended conversations, or large codebases without needing to truncate or chunk the input. This is critical for tasks like summarizing entire research papers, maintaining coherent multi-turn dialogues, or analyzing long legal contracts, where preserving full context directly impacts output quality and accuracy.

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Better performance on image generation tasks

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Lower cost per API call

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Faster inference speed

Question 13

A financial institution wants to deploy a gen AI model for fraud detection but must comply with strict regulations regarding explainability. What is the best strategy?

Accepted Answer

Implement a smaller interpretable model with acceptable accuracy. Regulatory compliance for fraud detection demands explainability, which complex black-box models cannot provide. A smaller interpretable model (e.g., logistic regression or decision tree) offers transparency into decision factors, satisfying regulations like GDPR's right to explanation while maintaining acceptable accuracy for the use case.

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Use Vertex AI Explainable AI with a complex model

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Deploy multiple models and ensemble

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Use a large black-box model and rely on external auditing

Question 14

A developer is building a customer support chatbot using a large language model. The chatbot frequently generates plausible-sounding but incorrect answers to product questions. Which technique should be applied to improve factual accuracy?

Accepted Answer

Enable Grounding with the company's product knowledge base.. Grounding with the company's product knowledge base (D) is the most effective technique to improve factual accuracy. Grounding involves retrieving relevant information from a trusted knowledge source and providing it to the model as context, so the model generates answers based on that data rather than relying solely on its training. This reduces hallucinations and ensures responses are accurate and up-to-date.

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Provide a few-shot example of correct answers in the prompt.

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Use a higher temperature setting to encourage more creative responses.

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Increase the model's context length to include more of the conversation history.

Question 15

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.

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Increase the temperature parameter to make the model more conservative

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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