Generative AI Leader Business Strategies for Generative AI Solutions — 25 Questions

Question 1

A healthcare organization is developing a generative AI system to assist doctors with clinical decision support. They are concerned about regulatory compliance (e.g., HIPAA) and potential liability. What is the most important business strategy to mitigate these risks?

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

Accepted Answer

Implement a human-in-the-loop review process with clear accountability for AI-generated recommendations.. A human-in-the-loop (HITL) review process ensures that all AI-generated recommendations are verified by a qualified clinician before action, directly addressing HIPAA accountability requirements and reducing liability by maintaining a clear chain of responsibility. This strategy aligns with regulatory frameworks that mandate human oversight for high-risk clinical decisions, as the AI system itself cannot be held liable under current laws.

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Limit the system to non-critical administrative tasks only.

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Use an open-source model to avoid vendor lock-in and reduce costs.

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Fully automate the system to reduce human error.

Question 2

A startup is building a generative AI content creation tool. They want to minimize operational costs while maintaining low latency for end users. Which deployment strategy should they adopt?

Accepted Answer

Use a serverless inference endpoint that scales to zero when not in use.. Serverless inference endpoints, such as Vertex AI endpoints with automatic scaling to zero, eliminate costs during periods of no traffic. This directly addresses the startup's goal of minimizing operational costs while maintaining low latency through provisioned concurrency and rapid scaling.

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Deploy the model on edge devices to reduce cloud dependency.

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Build an on-premises infrastructure to avoid cloud egress fees.

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Provision dedicated GPU instances for consistent performance.

Question 3

A global e-commerce company uses generative AI to generate product descriptions in multiple languages. They want to ensure consistency across markets while respecting cultural nuances. Which THREE strategies should they adopt?

Accepted Answer

Develop region-specific prompt templates that incorporate local cultural references and legal requirements.. Options B, C, and E are correct. Developing region-specific prompt templates (B) ensures that cultural nuances and legal requirements are incorporated. Engaging local marketing teams (C) provides human oversight to catch any cultural insensitivities. A/B testing (E) allows for data-driven optimization based on regional engagement metrics. Option A is incorrect because a neutral tone may still miss cultural nuances and can result in generic content. Option D is incorrect because relying on a single model with translation fails to address cultural context and may produce awkward or inappropriate phrasing.

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Standardize all descriptions to a neutral tone to avoid cultural issues.

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Use a single global model with a translation layer to convert English descriptions.

Question 4

A machine learning engineer is defining a Vertex AI pipeline for model evaluation using the JSON representation shown. The pipeline fails with an error that the 'eval_dataset' parameter is missing. What is the issue?

Accepted Answer

The pipeline spec does not declare 'eval_dataset' as a pipeline input parameter. The pipeline fails because the JSON representation of the Vertex AI pipeline does not include 'eval_dataset' in the `pipelineSpec.root.inputDefinitions.parameters` section. Without declaring it as a pipeline input parameter, the pipeline runtime cannot resolve the reference to `inputs.eval_dataset` in the component's arguments, causing the missing parameter error.

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The component 'comp-model-eval' does not accept 'eval_dataset' as input

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The 'project' parameter should be a pipeline input, not a constant

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The runtimeConfig parameter values must be strings, not references

Question 5

A global news agency is using a generative AI model to summarize breaking news articles in real-time. The model is deployed on Vertex AI across multiple regions (us-central1, europe-west4, asia-southeast1) for low latency worldwide. The agency has a Service Level Objective (SLO) of 99.9% availability and p99 latency under 2 seconds. Recently, during a major event, traffic spiked 10x, and the europe-west4 region experienced latency spikes over 5 seconds and some 503 errors. The team suspects the regional endpoint is under-provisioned. Which combination of actions should they take to meet the SLO consistently?

Accepted Answer

Enable the global endpoint feature in Vertex AI with automatic traffic splitting, and increase the minimum replicas for each regional endpoint. It enables the global endpoint feature with automatic traffic splitting, allowing traffic to be routed to healthy regions and providing failover. Additionally, increasing minimum replicas per region ensures each regional endpoint has baseline capacity to handle spikes, preventing under-provisioning. Option B only increases max replicas in europe-west4, which does not address traffic shifts, and reducing min replicas elsewhere risks capacity issues. Option C suggests Cloud CDN, which is for static content, not model inference. Option D configures a global load balancer with a single endpoint, which does not optimally use Vertex AI's regional endpoints and may not meet latency SLO.

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Increase the maximum replicas for the europe-west4 endpoint and reduce the min replicas in other regions

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Implement Cloud CDN caching for common summaries and reduce the number of regions to two

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Configure a global load balancer with a single Vertex AI endpoint and increase max replicas globally

Question 6

You are the Generative AI lead for a global retail company that is building a customer service chatbot using a large language model (LLM) on Vertex AI. The chatbot will handle order inquiries, returns, and product recommendations. The company has a multi-cloud strategy and uses Google Cloud for AI workloads, but customer data is stored in AWS DynamoDB and on-premises databases. The legal team mandates that no customer personally identifiable information (PII) is sent to the LLM for training or inference, and that the model's responses must comply with GDPR and CCPA. The engineering team has proposed using a fine-tuned version of Gemini with retrieval-augmented generation (RAG) from a vector database. During a pilot, the chatbot occasionally hallucinates and invents order details, and response latency is over 10 seconds for complex queries. The budget for this project is limited, and the team needs to balance cost, compliance, and performance. Which course of action should you recommend?

Accepted Answer

Ground the model with a curated knowledge base from DynamoDB and on-premises data, and use prompt engineering to explicitly instruct the model not to generate PII. Implement a PII detection and redaction layer before sending queries to the LLM.. The best course of action because it directly addresses both hallucinations and compliance without excessive cost or latency. Grounding the model with a curated knowledge base from DynamoDB and on-premises data (RAG) provides factual context, reducing hallucinations. Prompt engineering instructs the model not to generate PII, and a PII detection/redaction layer ensures no PII reaches the LLM, meeting GDPR/CCPA mandates. This approach balances cost, compliance, and performance better than the alternatives. Option A adds complexity and cost with two models and a router. Option B removes RAG, increasing hallucination risk despite fine-tuning. Option C uses a larger model and chain-of-thought, likely increasing latency beyond acceptable limits.

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Implement a two-model architecture: a smaller model for simple queries and a larger model for complex queries, with a router based on query complexity.

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Switch to a purely fine-tuned model without RAG, and rely on fine-tuning data that excludes PII to ensure compliance.

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Use a larger, more powerful LLM with chain-of-thought prompting to improve reasoning and reduce hallucinations, and cache frequent queries to reduce latency.

Question 7

A company wants to deploy a generative AI chatbot for customer service but is concerned about cost unpredictability due to variable usage. Which pricing model should they choose to best manage costs?

Accepted Answer

Provisioned throughput. Provisioned throughput provides fixed capacity with predictable monthly cost, ideal for managing cost uncertainty. Option A (committed use discounts) requires a commitment but costs can still vary if usage exceeds the committed amount. Option B (free tier) is too limited for a full-scale chatbot. Option C (pay-as-you-go) is variable and leads to cost unpredictability.

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Committed use discounts

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Free tier

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Pay-as-you-go

Question 8

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. 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.

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Use Vertex AI Endpoint with monitoring

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Implement a custom cost allocation using labels

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Use Cloud Billing budgets and alerts per model

Question 9

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. (Automatic static analysis) is correct because it directly scans code for security vulnerabilities, making it the most appropriate metric for this purpose. Option A (BLEU score) measures text similarity, not security. Option C (Human evaluation) is subjective and less scalable. Option D (Perplexity) measures language model confidence, not code security.

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BLEU score

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Human evaluation

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Perplexity

Question 10

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. Ensuring data privacy and compliance is the primary business consideration when handling customer data, especially with regulatory requirements like GDPR or CCPA. Option A is wrong because while latency matters, minimizing it at any cost is not the primary consideration and can lead to excessive expenses. Option B is wrong because using only open-source models may limit flexibility and can raise data privacy concerns if not properly vetted. Option C is wrong because choosing the most complex model often increases cost and latency without necessarily improving business outcomes.

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Minimizing latency at any cost

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Using open-source models only

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Choosing the most complex model

Question 11

A company wants to ensure only authorized users can deploy gen AI models. The current policy allows all users in the domain. What is the best practice to restrict deployment?

Accepted Answer

Add condition to restrict deployment. Adding a condition to restrict deployment (e.g., using IAM conditions in Google Cloud's attribute-based access control) allows you to limit model deployment to only authorized users based on attributes like user role, project, or resource tags. This is the best practice because it enforces fine-grained access control without removing existing permissions or adding unnecessary roles, directly addressing the requirement to restrict deployment while maintaining existing user access.

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Remove the binding

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Add more roles

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Use organizational policies

Question 12

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.. The IAM policy grants the 'Vertex AI User' role to developer@example.com, which includes permissions for using models for prediction (e.g., `aiplatform.predict`) and viewing metadata (e.g., `aiplatform.models.list`). This role does not include permissions for deploying or managing models, nor full control over all Vertex AI resources, making option A correct.

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No effective permissions because the role is incorrect.

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Ability to deploy and manage models.

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Full control over all Vertex AI resources.

Question 13

A retail company wants to build a chatbot that answers product questions and provides personalized recommendations. They have a small labeled dataset and limited ML expertise. Which approach should they take?

Accepted Answer

Use Vertex AI Agent Builder with a pre-built agent and integrate their product catalog via Search and Conversation.. Vertex AI Agent Builder provides a pre-built agent framework that integrates with Search and Conversation, allowing the company to quickly deploy a chatbot using their product catalog without needing extensive ML expertise. This approach leverages Google's foundation models and retrieval-augmented generation (RAG) to answer product questions and generate personalized recommendations, making it ideal for a small labeled dataset and limited ML resources.

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Fine-tune Gemini with their product data using Vertex AI Generative AI Studio.

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Build a custom transformer model using TensorFlow on Vertex AI Workbench.

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Use BigQuery ML to train a classification model on customer queries.

Question 14

A large enterprise runs a generative AI solution serving millions of daily inference requests. To reduce costs, they propose using serverless endpoints (Vertex AI Prediction) with a custom container, but they notice high latency during cold starts. Which strategy best addresses this problem while minimizing cost?

Accepted Answer

Set a minimum number of replicas to maintain a baseline of always-on instances.. Setting a minimum number of replicas ensures that a baseline of always-on instances is maintained, eliminating cold starts for the majority of requests. This directly addresses the latency spike caused by container initialization and model loading in serverless endpoints, while the cost impact is limited to the minimum replicas rather than scaling all instances.

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Upgrade to GPU-accelerated machines for all replicas.

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Implement client-side request batching to reduce the number of inference calls.

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Use prewarmed containers by setting an idle timeout to keep instances alive.

Question 15

A company is building a search application that requires grounding answers in their internal knowledge base. They want to use Vertex AI Search and Conversation with a custom datastore. Which configuration is essential to ensure the model only answers based on their documents?

Accepted Answer

Configure the answer generation to use grounding with the enterprise datastore as the source.. Vertex AI Search and Conversation provides a built-in grounding capability that explicitly ties answer generation to a specified enterprise datastore. By configuring grounding with the custom datastore as the source, the model is constrained to retrieve and synthesize answers exclusively from the indexed documents, preventing reliance on its parametric knowledge or external sources.

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Enable streaming responses to get real-time answers.

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Fine-tune the model on the company's documents.

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Set the model's temperature to 0 to make responses deterministic.

Question 16

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.. Splitting videos into short clips and extracting key frames reduces computational load and token usage, while Gemini 1.5 Pro's context caching efficiently handles variable-length videos by reusing processed context across requests. This balance of scalability (avoiding processing entire videos at once) and cost-effectiveness (minimizing API calls and storage) is ideal for multi-modal summarization. Option B is less scalable and cost-effective because it introduces an additional API (Video Intelligence) and loses visual context from captions. Option C loses multi-modal information by converting all inputs to text, reducing summary accuracy. Option D would directly ingest raw video, leading to prohibitive token costs and latency, making it impractical for variable-length inputs.

Answer

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

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

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

Question 17

A startup wants to generate concise summaries of long news articles using an LLM on Vertex AI. They prioritize low latency and cost. Which model choice is most appropriate?

Accepted Answer

Use Gemini 1.5 Flash, which is designed for high throughput and low cost.. Gemini 1.5 Flash is optimized for high-throughput, low-latency, and cost-efficient summarization tasks, making it the ideal choice for a startup that needs to process long news articles quickly without incurring high costs. It balances performance and economy, whereas Gemini 1.5 Pro prioritizes accuracy at higher latency and cost, and PaLM 2 Bison is less efficient for this use case.

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Use Gemini 1.5 Pro for the highest accuracy.

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Use PaLM 2 Bison, as it is the most economical.

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Use Vertex AI Text Embeddings, since embeddings can generate summaries.

Question 18

A bank wants to use LLMs to generate responses for customer support chat. All conversations must be logged, and any PII must be masked. The solution must comply with financial regulations. Which combination of Vertex AI services should be used?

Accepted Answer

Use Vertex AI Agent Builder with Data Governance, which can automatically mask PII and log interactions.. Vertex AI Agent Builder integrates with Data Governance to automatically mask PII and log interactions, meeting both the logging and compliance requirements without custom development. This managed service ensures adherence to financial regulations by providing built-in data loss prevention (DLP) capabilities and audit trails, unlike the other options which require manual or less integrated approaches.

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Deploy a custom model on Cloud Run and write a Cloud Function to mask PII.

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Use Vertex AI Prediction with a custom container that masks PII before inference.

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Use the Gemini API directly with a custom logging solution in Cloud Logging.

Question 19

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.. Vertex AI Pipelines provides a managed, serverless orchestration service that can run the weekly fine-tuning workflow with minimal operational overhead, while Vertex AI Managed Datasets can incrementally sync new data from BigQuery, reducing data transfer costs by avoiding full dataset copies. This combination minimizes downtime because the pipeline can be triggered on a schedule without manual intervention, and incremental syncs avoid re-transferring the entire proprietary dataset each week.

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

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

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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 20

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.. Vertex AI Safety Attributes provides built-in safety filters that can detect and block harmful content (e.g., hate speech, toxicity, financial misinformation) in both user prompts and model outputs. This directly addresses the need to avoid toxic outputs and comply with regulations like GDPR, which require protecting users from harmful or biased advice.

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

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Fine-tune the model exclusively on compliant financial documents.

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Disable request logging to avoid storing sensitive data.