Generative AI Leader Practice Test 13 — 15 Questions

Question 1

A large enterprise wants to deploy multiple generative AI models across different business units while ensuring cost governance and usage tracking. Which Google Cloud solution is best suited?

Accepted Answer

Deploy each model in a separate project with IAM policies. Option B is correct because deploying each model in a separate Google Cloud project with IAM policies provides the strongest isolation for cost governance and usage tracking. This approach ensures that each business unit's model usage is billed to its own project, enabling granular cost allocation and independent monitoring without cross-project interference. It also allows per-project budget alerts and usage quotas, directly addressing the enterprise's need for decentralized cost control.

Answer

Use Vertex AI Endpoint with monitoring

Answer

Implement a custom cost allocation using labels

Answer

Use Cloud Billing budgets and alerts per model

Question 2

A company is using generative AI for code generation and wants to evaluate the quality of generated code for security vulnerabilities. Which metric is most appropriate?

Accepted Answer

Automatic static analysis. Option C is correct because automatic static analysis can scan code for security issues efficiently. Option A (BLEU score) measures text similarity, not security. Option B (human evaluation) is subjective and expensive. Option D (perplexity) measures language model confidence, not code security.

Answer

BLEU score

Answer

Human evaluation

Answer

Perplexity

Question 3

A global company deploying gen AI across multiple regions needs to minimize latency and comply with data sovereignty. What architecture should they adopt?

Accepted Answer

Multi-region deployment with Vertex AI. Option D is correct because multi-region deployment with Vertex AI allows serving models close to users (low latency) while adhering to data residency requirements. Option A is wrong because a single global deployment may violate data sovereignty and increase latency. Option B is wrong because on-premises deployment is costly and limits scalability. Option C is wrong because third-party APIs may not offer multi-region data control.

Answer

Single global deployment with CDN

Answer

Use a third-party API

Answer

On-premises deployment only

Question 4

A company has been using an on-premises ML infrastructure for generative AI and wants to migrate to Google Cloud. They have a pipeline that fine-tunes a large language model weekly using a proprietary dataset. The migration must minimize downtime and data transfer costs. Which approach best addresses these requirements?

Accepted Answer

Use Vertex AI Pipelines to orchestrate the fine-tuning process, and use Vertex AI Managed Datasets to incrementally sync new data with BigQuery as the source.. Option C is correct because Vertex AI Pipelines with Managed Datasets allows incremental data transfer and automates fine-tuning in the cloud, minimizing downtime. Option A is wrong because Cloud Storage Transfer Service is for one-time bulk transfer, causing longer downtime. Option B is wrong because a custom solution on GKE is complex and may not reduce costs. Option D is wrong because AutoML does not support fine-tuning custom large language models.

Answer

Use AutoML to train a new model directly from the dataset without fine-tuning.

Answer

Deploy the existing pipeline on a Google Kubernetes Engine cluster and use Google Cloud Filestore for shared storage.

Answer

Use Cloud Storage Transfer Service to move all data to Cloud Storage, then set up a Vertex AI custom training job to run the fine-tuning.

Question 5

A healthcare startup wants to use generative AI to provide clinical decision support. They must minimize the risk of harmful hallucinations. Which business strategy is most appropriate?

Accepted Answer

Implement retrieval-augmented generation with meticulously curated medical literature.. Retrieval-augmented generation (RAG) grounds the model's output in a trusted, external knowledge base—here, curated medical literature—which directly reduces the risk of hallucination by forcing the model to cite or derive answers from verified sources. This is the most effective strategy for clinical decision support because it combines generative flexibility with factual accuracy, unlike methods that only limit output or rely on post-hoc filtering.

Answer

Limit the model's output length to reduce hallucination risk.

Answer

Deploy a large general-purpose model and rely on post-processing filters.

Answer

Use a custom fine-tuned model on a proprietary medical dataset.

Question 6

A financial services firm wants to deploy generative AI for automated investment advice. They are subject to strict regulatory oversight requiring explainability and audit trails. Which strategy best meets these requirements?

Accepted Answer

Implement human-in-the-loop with full logging of model inputs, outputs, and human decisions.. Option D is correct because it directly addresses the regulatory requirements for explainability and audit trails by incorporating human oversight and comprehensive logging. The human-in-the-loop (HITL) mechanism ensures that critical investment decisions are reviewed by qualified professionals, while full logging of model inputs, outputs, and human decisions creates a transparent, auditable record. This approach satisfies financial regulations like MiFID II or SEC rules that mandate explainability and accountability in automated advice systems.

Answer

Fine-tune a model on historical trading data without human review.

Answer

Use a black-box large language model with monitoring.

Answer

Deploy a rule-based system augmented with generative AI for content generation.

Question 7

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?

Accepted Answer

Use a pre-trained code model (e.g., Codey) and add a security filtering layer. Option B is correct because using a pre-trained code model with a security filtering layer provides a good balance: quick start, built-in safety checks, and manageable cost. Option A (building from scratch) is too slow. Option C (manual review) doesn't scale. Option D (restricting outputs) may reduce usefulness.

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

Answer

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

Question 8

A company with limited AI expertise wants to adopt gen AI. They need a solution that integrates with existing data and applications. Which Google Cloud offering is best?

Accepted Answer

Vertex AI Agent Builder. Option D is correct because Vertex AI Agent Builder is designed for building conversational AI agents with easy integration to enterprise data sources. Option A is wrong because Colab Enterprise is a notebook environment, not a full solution. Option B is wrong because Apigee is an API management platform. Option C is wrong because BigQuery ML is for SQL-based ML, not gen AI agents.

Answer

Apigee

Answer

Colab Enterprise

Answer

BigQuery ML

Question 9

Refer to the exhibit. What access does the IAM policy grant to developer@example.com?

Accepted Answer

Ability to use Vertex AI models for prediction and view metadata.. Option B is correct because roles/aiplatform.user allows using models for predictions and viewing metadata, but not deploying or deleting models. Options A and C require higher roles.

Answer

No effective permissions because the role is incorrect.

Answer

Ability to deploy and manage models.

Answer

Full control over all Vertex AI resources.

Question 10

Refer to the exhibit. A user receives this error when trying to get predictions from a Vertex AI endpoint. What is the most likely cause?

Accepted Answer

The user lacks necessary IAM permissions. Option B is correct because the error message explicitly says PERMISSION_DENIED, indicating lack of IAM permissions. Option A (endpoint does not exist) would give NOT_FOUND error. Option C (model not deployed) would give a different error. Option D (different region) would also give a different error.

Answer

The endpoint does not exist

Answer

The endpoint is in a different region

Answer

The model is not deployed

Question 11

A company is establishing governance practices for generative AI models. Which three actions are essential for responsible AI deployment?

Accepted Answer

Use model versioning to track changes.. Options A, C, and D are correct. Regular bias audits ensure fairness; model versioning provides traceability; human review processes catch critical errors. Data leakage monitoring is important but not always considered a core governance pillar; open-sourcing is voluntary and not essential.

Answer

Monitor for data leakage from training data.

Answer

Open-source the model to ensure transparency.

Question 12

A media company wants to build a multi-modal generative app that accepts text, image, and video inputs and produces summaries. The app must handle variable-length videos up to 10 minutes. Which architecture is most scalable and cost-effective?

Accepted Answer

Use a pipeline to split videos into short clips, extract key frames, and process with Gemini 1.5 Pro (with context caching) to generate summaries.. Option A is correct because splitting videos into short clips and extracting key frames reduces the computational load and token usage, while Gemini 1.5 Pro's context caching efficiently handles variable-length videos up to 10 minutes by reusing processed context across requests. This approach balances scalability (by avoiding processing entire videos at once) and cost-effectiveness (by minimizing API calls and storage), making it ideal for a multi-modal summarization app.

Answer

Use Video Intelligence API to generate video captions, then feed captions to a text model.

Answer

Convert all inputs to text descriptions and use a text-only model.

Answer

Deploy a single Vertex AI endpoint with a model that can ingest multi-modal data directly.

Question 13

A financial institution is deploying a generative AI solution that generates investment advice. They must ensure fairness, avoid toxic outputs, and comply with regulations like GDPR. Which TWO strategies should they implement? (Choose two.)

Accepted Answer

Use Vertex AI Safety Attributes to filter harmful content in both input and output.. Options B and D are correct because using safety attributes to filter harm and implementing a human-in-the-loop for high-risk outputs are direct measures. Option A is wrong because disabling logging is against compliance. Option C is wrong because training only on compliant data is insufficient for every scenario. Option E is wrong because decreasing temperature does not guarantee fairness.

Answer

Set the model temperature to 0 to eliminate creativity and reduce bias.

Answer

Fine-tune the model exclusively on compliant financial documents.

Answer

Disable request logging to avoid storing sensitive data.

Question 14

A retail company wants to use gen AI for customer service chatbots. They have a large volume of customer interactions. What is the primary business consideration for deploying a gen AI solution?

Accepted Answer

Ensuring data privacy and compliance. Option D is correct because ensuring data privacy and compliance is critical when handling customer data. Option A is wrong because complexity doesn't guarantee success. Option B is wrong because minimizing latency at any cost can be too expensive. Option C is wrong because open-source models may not meet all requirements.

Answer

Minimizing latency at any cost

Answer

Using open-source models only

Answer

Choosing the most complex model

Question 15

A government agency is deploying a generative AI chatbot to answer citizen questions about public services. The chatbot must provide accurate and consistent information, scale to handle peak loads during tax season, and comply with strict data sovereignty laws that require all data to stay within the country. The agency has a moderate budget and in-house IT team but limited AI expertise. Which deployment architecture should they choose?

Accepted Answer

Deploy a pre-trained model on Vertex AI in the required region with auto-scaling. Option A is correct because using Vertex AI within a single region ensures data sovereignty, and autoscaling handles peak loads. Option B (multi-region) violates data sovereignty. Option C (on-premises) lacks scalability and AI expertise. Option D (managed service from a third-party) may not meet sovereignty or budget.

Answer

Build and host the model on-premises using open-source tools

Answer

Deploy the model on Vertex AI across multiple regions for availability

Answer

Use a third-party managed generative AI service that guarantees data residency