Generative AI Leader Practice Test 33 — 15 Questions

Question 1

A healthcare organization wants to deploy a GenAI chatbot for patient intake. They must ensure that the model does not generate harmful advice and that patient data remains confidential. Which set of controls should they implement on Vertex AI?

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

Accepted Answer

Enable safety filters, use VPC Service Controls, and enforce Private Google Access. Safety filters block harmful content; VPC Service Controls keep data within the customer's VPC; Private Google Access prevents data exfiltration. The other options lack these combined controls.

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Use model tuning with only positive examples

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Disable logging and restrict API keys

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Use a public model endpoint and rely on prompt engineering

Question 2

Refer to the exhibit. What is the most likely cause of this error?

Accepted Answer

The user does not have the required IAM role. The error shown in the exhibit is an HTTP 403 Forbidden response, which indicates that the server understood the request but refuses to authorize it. In Google Cloud, this is most commonly caused by the user's identity lacking the necessary IAM role or permission to call the specific API or access the resource. Even if the project ID is correct and the network is functional, a missing IAM role (e.g., `aiplatform.user` or `roles/aiplatform.user`) will result in this exact error.

Answer

The model is too large

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The network is down

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The project ID is incorrect

Question 3

A company wants to use generative AI to create short product videos from text descriptions. Which Google Cloud service should they consider?

Accepted Answer

Veo. Veo is Google's video generation model that creates videos from text. Imagen creates images, Chirp creates audio, and Gemini is multimodal but not specialized for video generation.

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Chirp

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Imagen

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Gemini

Question 4

A business leader is developing a gen AI strategy. Which three key components should be included in the strategy?

Accepted Answer

Plan for responsible AI. Responsible AI is a foundational component of any generative AI strategy, ensuring ethical use, bias mitigation, and compliance with emerging regulations. Without a plan for responsible AI, the organization risks reputational damage, legal liability, and deployment failures due to lack of trust. This goes beyond simple fairness checklists to include continuous monitoring of model outputs for toxicity, hallucination, and privacy violations.

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Focus solely on technology

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Involve stakeholders across departments

Question 5

A generative AI model is trained on a dataset containing biased text. The team wants to debias the model without significantly sacrificing performance on the original task. Which approach is most appropriate?

Accepted Answer

Curate a smaller, balanced dataset that is representative of fair outcomes and fine-tune the model using a combination of the original data and this dataset with a regularization penalty on bias metrics.. It directly addresses bias in the training data by combining the original dataset with a curated, balanced dataset and applying a regularization penalty on bias metrics. This approach allows the model to retain performance on the original task while explicitly penalizing biased representations during fine-tuning, which is a standard technique in fairness-aware machine learning. The regularization term acts as a constraint that guides the optimization away from biased decision boundaries without requiring full retraining or architectural changes.

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Train an adversarial classifier to predict protected attributes from the model's hidden representations and minimize that prediction accuracy.

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Filter the original training dataset to remove all sentences containing biased terms or stereotypes.

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After training, apply a separate classifier on the model's output logits to adjust the final predictions for fairness.

Question 6

A startup wants to build a generative AI application for customer support. Their main concern is cost control while maintaining low latency. Which Google Cloud service is most suitable for deploying their custom model?

Accepted Answer

Vertex AI Prediction. Vertex AI Prediction is the correct choice because it provides a fully managed, serverless endpoint for deploying custom models with autoscaling to zero, which directly addresses the startup's need for cost control by only charging for compute resources when the endpoint serves predictions. It also supports low latency through optimized prediction containers and can leverage GPUs or TPUs for inference, making it ideal for real-time customer support applications.

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BigQuery ML

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Cloud Run

Answer

Vertex AI Workbench

Question 7

An organization needs to build an AI system that can take actions on behalf of users, such as booking appointments or sending emails. They want to use Google Cloud's capabilities for this. Which approach aligns with Google's future direction?

Accepted Answer

Use Gemini with function calling to execute tasks. Agentic AI refers to systems that can autonomously perform tasks and take actions, which is a key area of investment.

Answer

Deploy a static knowledge base chatbot

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Use AutoML Tables to predict actions

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Fine-tune Gemini for classification

Question 8

A research organization is building a generative AI model to assist in drug discovery by generating molecular structures. They have a large dataset of proprietary chemical compounds and want to train a model from scratch. They have extensive ML expertise but limited GPU resources. The organization must comply with strict data privacy regulations that prohibit data from leaving their on-premises environment. Which strategy enables them to train the model efficiently while meeting compliance?

Accepted Answer

Use Google Cloud Confidential VMs with attached GPUs for secure training. Google Cloud Confidential VMs with attached GPUs provide hardware-based memory encryption (using AMD SEV or Intel TDX) that protects data in use, enabling secure training on sensitive proprietary chemical data in the cloud. This allows the organization to leverage scalable GPU resources for efficient model training while maintaining compliance with strict data privacy regulations that prohibit data from leaving their on-premises environment.

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Train the model entirely on-premises using existing servers

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Partner with a cloud provider to train the model on their infrastructure

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Transfer the data to Google Cloud and use standard GPU instances

Question 9

A developer is using the Gemini API to generate creative product taglines. The taglines are often bland and uncreative. The developer wants more variety and novelty in the outputs. Which parameter adjustment would most effectively increase the diversity of the generated taglines?

Accepted Answer

Increase temperature from 0.2 to 0.9.. Increasing temperature from 0.2 to 0.9 raises the randomness of token sampling, which directly increases the diversity and novelty of generated text. A low temperature (e.g., 0.2) makes the model highly deterministic, always picking the most probable next token, leading to bland outputs. A higher temperature (e.g., 0.9) allows less probable tokens to be selected more often, producing more creative and varied taglines.

Answer

Decrease top_p from 1.0 to 0.5.

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Set frequency_penalty to 2.0.

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Decrease temperature from 0.7 to 0.2.

Question 10

A company deploys a Gemini model on Vertex AI for a healthcare application. They need to ensure that the model does not generate medical advice and that responses are grounded in trusted medical sources. Which combination of safety measures should they implement?

Accepted Answer

Enable safety filters and use Vertex AI Grounding with a labeled medical dataset. It combines two essential safety layers: safety filters block harmful content (including medical advice), and Vertex AI Grounding anchors responses to a labeled medical dataset, ensuring factual accuracy and compliance with healthcare regulations. This dual approach prevents the model from generating unverified or dangerous medical information while maintaining relevance to trusted sources.

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Use Vertex AI Grounding with a public dataset and disable safety filters

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Enable safety filters only, without grounding

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Fine-tune the model on a curated medical dataset and disable safety filters for faster responses

Question 11

A company wants to build a chatbot using Vertex AI that can answer customer questions based on their internal knowledge base. Which Google Cloud service should they use to store and retrieve the knowledge base efficiently?

Accepted Answer

Vertex AI Vector Search. Vertex AI Vector Search is the correct choice because it is purpose-built for semantic similarity search over embeddings, enabling the chatbot to retrieve relevant chunks from the knowledge base based on meaning rather than exact keyword matches. It integrates natively with Vertex AI and supports high-dimensional vector indexing, making it efficient for large-scale retrieval-augmented generation (RAG) workflows.

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Cloud Storage

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BigQuery

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Vertex AI Matching Engine

Question 12

A company is using Vertex AI to generate marketing copy. They notice that the output sometimes contains factual inaccuracies. Which parameter adjustment is most likely to improve factual accuracy?

Accepted Answer

Decrease the temperature parameter.. Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and less likely to generate creative but factually incorrect content. Lower temperature (e.g., 0.1) forces the model to choose higher-probability tokens, which aligns with more factual and consistent responses, especially in tasks like marketing copy where accuracy is critical.

Answer

Increase the max_output_tokens parameter.

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Increase the top_p parameter.

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Add a post-processing step to verify facts using a database.

Question 13

A company using Vertex AI for model training wants to reduce costs by automatically stopping training when the model's performance stops improving. Which Vertex AI feature should they configure?

Accepted Answer

Early stopping (via Vertex AI Training). Early stopping in Vertex AI Training stops training runs when performance plateaus. Hyperparameter tuning optimizes parameters, not cost. Workbench is for notebooks. AutoML handles this automatically but is for specific model types.

Answer

AutoML

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Hyperparameter tuning

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Vertex AI Workbench

Question 14

A developer is using Gemini 1.5 Flash for a real-time chat application and notices that responses are sometimes too slow. Which model parameter or configuration change would MOST likely reduce latency without significantly harming quality?

Accepted Answer

Decrease the max output tokens setting. Lowering max output tokens reduces the number of tokens generated, directly decreasing response time. Temperature and top-p affect creativity, not latency. Reducing top-k may slightly speed up sampling but has a minimal effect compared to output length.

Answer

Decrease the top-k value to 10

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Increase the temperature to 1.5

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Increase the top-p value to 1.0

Question 15

A financial institution wants to deploy a generative AI system for automated report generation. They require that the model does NOT expose sensitive information from its training data and that outputs are factually accurate. Which THREE techniques should they combine?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) to ground outputs in verified documents. RAG grounds outputs in verified sources, RLHF can reduce hallucination and harmful outputs, and differential privacy during training prevents memorization of sensitive data.

Answer

Use a larger model with more parameters

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Increase the temperature to 2.0 for creativity