Generative AI Leader Practice Test 38 — 15 Questions

Question 1

A data scientist wants to fine-tune a foundation model from Vertex AI Model Garden on their custom dataset. They want to choose a cost-effective method that updates only a small subset of parameters. Which fine-tuning approach should they use?

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

Accepted Answer

Parameter-Efficient Fine-Tuning (PEFT) like LoRA. Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA (Low-Rank Adaptation) are specifically designed to update only a small subset of parameters (e.g., low-rank matrices injected into transformer layers) while keeping the majority of the foundation model frozen. This drastically reduces memory and compute costs compared to full fine-tuning, making it the most cost-effective choice for customizing a model from Vertex AI Model Garden on a custom dataset.

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Full fine-tuning

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Prompt tuning

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Reinforcement Learning from Human Feedback (RLHF)

Question 2

A company is using a generative AI model to automatically screen job applications. They want to ensure the model does not discriminate based on gender or ethnicity. Which of the following actions should they take as part of responsible AI?

Accepted Answer

Evaluate the model's hiring decisions for disparate impact across gender and ethnicity. Evaluating the model's decisions for disparate impact using protected attributes is a key step in detecting and mitigating unfair bias.

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Use a more powerful model to improve accuracy, as bias decreases with better performance

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Blindly apply a fairness algorithm without understanding the context

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Remove all demographic data from the training set to prevent the model from learning biases

Question 3

A company is using Vertex AI Agent Builder to create a travel booking agent. They want the agent to book flights and hotels dynamically. What action type should they use?

Accepted Answer

Webhook. Vertex AI Agent Builder uses webhooks to integrate with external systems for dynamic, real-time operations like booking flights and hotels. A webhook allows the agent to make HTTP calls to external APIs (e.g., a travel booking service) to fetch or update data during a conversation, enabling dynamic booking actions. Static or notification actions cannot handle the two-way, real-time data exchange required for live reservations.

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Dynamic call

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Static call

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Notification

Question 4

A company is using Vertex AI to deploy a text generation model for a chatbot. They want to reduce the response latency. Which configuration change is most effective?

Accepted Answer

Use a smaller model variant. Using a smaller model variant directly reduces the number of parameters and computational operations required per inference, which lowers latency. In Vertex AI, smaller models like `text-bison@002` have fewer layers and attention heads than larger counterparts, resulting in faster token generation without requiring hardware changes.

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Enable model quantization

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Increase the number of GPUs

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Use a larger batch size

Question 5

A data analyst needs to run a simple regression model directly on data stored in BigQuery without moving data to another platform. Which service should they use?

Accepted Answer

BigQuery ML. BigQuery ML (B) is correct because it allows users to create and execute machine learning models using standard SQL syntax directly on data stored in BigQuery, without needing to export data to a separate platform. This service is specifically designed for running regression, classification, and other models natively within BigQuery, leveraging its serverless architecture and built-in ML capabilities.

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TensorFlow on Compute Engine

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Vertex AI Training

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Google Colab

Question 6

A financial services firm uses a fine-tuned model for contract analysis. They observe that the model's performance degrades after a few months because contract language evolves. The team wants to maintain accuracy without full retraining. What is the MOST cost-effective approach?

Accepted Answer

Perform incremental fine-tuning with a small representative sample of new contracts. Fine-tuning the existing model with a small amount of new data (incremental fine-tuning) is the most cost-effective way to adapt to language evolution without full retraining. The other options are either too costly or do not adapt the model.

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Switch to a larger base model and use zero-shot prompting

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Retrain the model from scratch every quarter with all historical data

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Use Vertex AI Model Monitoring to detect drift and alert, then manually adjust prompts

Question 7

A data scientist observes that a text generation model consistently produces outputs that stereotype certain genders. According to Google's AI Principles, what is the BEST first step?

Accepted Answer

Evaluate the model's bias using a diverse test set across genders. Google's AI Principles emphasize that the first step in addressing bias is to evaluate and measure it using appropriate tools and diverse datasets. This aligns with Principle #2: 'Avoid creating or reinforcing unfair bias,' which requires testing models across relevant demographic groups before taking corrective action. Without evaluation, any subsequent mitigation steps would lack a baseline and could be ineffective or counterproductive.

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Immediately stop using the model and delete it

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Fine-tune the model on a gender-balanced dataset

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Add a disclaimer that the model may exhibit bias

Question 8

A healthcare organization needs to process sensitive patient data (PHI) using Google Cloud's Natural Language API for entity extraction. They require HIPAA compliance and data isolation within Google's infrastructure. What is the minimum configuration they must enable?

Accepted Answer

Sign a HIPAA Business Associate Agreement (BAA) with Google Cloud and enable VPC Service Controls. To use Cloud AI APIs with HIPAA compliance and data isolation, the customer must sign a BAA and use VPC Service Controls to prevent data exfiltration.

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Use the Healthcare API instead of Natural Language API

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Enable CMEK for the Natural Language API and use a private IP address

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Deploy the Natural Language API in a Google-managed project and use a shared VPC

Question 9

A research team wants to run a large-scale training job for a custom transformer model. They need access to Google's custom AI accelerators with high-speed interconnects for distributed training. Which infrastructure should they use?

Accepted Answer

Cloud TPU v3-32 pod. Loud TPU v3-32 pod because it provides Google's custom AI accelerators (TPUs) with high-speed interconnects (e.g., a 2D toroidal mesh network) specifically designed for large-scale distributed training of transformer models. This pod offers 32 TPU v3 chips interconnected at 100 Gbps per chip, enabling efficient model parallelism and data parallelism for custom transformer architectures, which is not achievable with standard GPUs or Kubernetes setups.

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Vertex AI Training with default GPU

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Compute Engine with NVIDIA H100 GPUs

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Google Kubernetes Engine with GPU nodes

Question 10

A data scientist wants to run large-scale distributed training of a custom deep learning model using Google's custom AI accelerators. Which infrastructure should they choose to minimize cost while leveraging Google's proprietary chips?

Accepted Answer

Cloud TPU v5e. Cloud TPU v5e is the correct choice because it is Google's proprietary custom AI accelerator designed specifically for large-scale distributed training of deep learning models, offering superior cost-efficiency compared to GPUs for many workloads. TPU v5e provides a balanced price-performance ratio for medium-to-large training tasks, and Google's TPU architecture is optimized for TensorFlow and JAX, enabling efficient scaling across multiple TPU pods. This minimizes cost while leveraging Google's custom chips, as opposed to using NVIDIA GPUs which are not Google's proprietary hardware.

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Compute Engine with NVIDIA A100 GPUs

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Vertex AI Workbench with custom machines

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Google Colab Pro with TPU runtime

Question 11

What is the primary purpose of Google's Content Safety filters in Vertex AI?

Accepted Answer

To block generated content that contains hate speech, violence, or sexually explicit material. Google's Content Safety filters in Vertex AI are designed to block generated content that violates safety policies, specifically targeting hate speech, violence, and sexually explicit material. This is a core component of responsible AI deployment, ensuring that model outputs adhere to ethical guidelines and legal requirements. The filters operate by analyzing the generated text or images against predefined safety categories, not by assessing data quality or source curation.

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To filter out low-quality training data

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To ensure the model only generates content from a curated set of sources

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To improve the model's accuracy on safe content

Question 12

A financial services firm is deploying a generative AI model to assist in loan approval decisions. To comply with regulatory requirements for fairness and explainability, which THREE actions should they take? (Choose 3)

Accepted Answer

Evaluate the model for bias using diverse test sets. Evaluating the model for bias using diverse test sets is a fundamental step in ensuring fairness in AI-driven loan approvals. This involves testing the model across demographic groups (e.g., race, gender, age) to detect disparate impact, which is required by regulations like the Equal Credit Opportunity Act (ECOA) and Fair Housing Act. Without this evaluation, the model could inadvertently discriminate, leading to legal and ethical violations.

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Add SynthID watermarks to all model outputs

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Increase the model size to improve accuracy

Question 13

Refer to the exhibit. A data scientist runs the gcloud command and sees the model listed. However, when they try to deploy the model to an endpoint, they get an error: 'Model is not deployable'. What is the most likely reason?

Accepted Answer

The model was imported from a custom container but without a serving specification or artifact.. A model imported from a custom container must include a serving specification (e.g., a `predict` route) and an artifact (e.g., a saved model file) to be deployable. Without these, Vertex AI cannot determine how to serve predictions, resulting in the 'Model is not deployable' error. The `gcloud` command listing the model only confirms its registration, not its readiness for deployment.

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The model is still in training and not yet ready.

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The model does not have the correct IAM permissions assigned to the deployment service account.

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The region for the endpoint is different from the model's region.

Question 14

A global e-commerce company wants to translate product descriptions into 50 languages with high accuracy. They need to handle domain-specific terms (e.g., 'size chart', 'return policy'). Which approach should they use?

Accepted Answer

Use Vertex AI Translation with custom glossaries. Vertex AI Translation with custom glossaries is specifically designed for high-accuracy, domain-specific translations. Custom glossaries allow you to define precise translations for terms like 'size chart' and 'return policy', ensuring consistency across 50 languages. This approach leverages Google's neural machine translation models while overriding generic translations with your business-specific terminology.

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Use the Gemini API with a prompt like 'Translate to French'

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Build a custom agent with Vertex AI Agent Builder

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Use Imagen to generate translated images

Question 15

A company is evaluating the ROI of a generative AI project. Which metric is most appropriate?

Accepted Answer

Reduction in time to complete tasks using the generative AI tool. The primary business justification for a generative AI project is operational efficiency, measured directly by the reduction in time to complete tasks. Unlike technical metrics such as model error rate, this metric ties the AI's output to tangible productivity gains, which is the core of ROI analysis in a business context. Generative AI tools are designed to augment human workflows, so time savings translate into cost savings and increased throughput, making it the most appropriate metric for evaluating return on investment.

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Reduction in model error rate on a test set

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Increase in user satisfaction scores

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Cost per inference compared to historical average