Generative AI Leader Business Strategies for Generative AI Solutions Practice Test 3 — 15 Questions

Question 1

A manufacturing company wants to use generative AI to create maintenance manuals from sensor data. The manuals must be accurate and reflect the latest equipment configurations. Which approach best ensures data freshness and consistency?

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

Accepted Answer

Use a retrieval-augmented generation (RAG) system that queries a live database of sensor configurations.. A retrieval-augmented generation (RAG) system retrieves the most current equipment configurations directly from a live database at inference time, ensuring the generated manual reflects real-time sensor data without requiring model retraining. This approach decouples the static knowledge in the LLM from the dynamic data source, guaranteeing both accuracy and freshness while avoiding the latency and cost of continuous retraining.

Answer

Train the model in real-time as sensor data streams in.

Answer

Periodically retrain the model with the latest sensor data.

Answer

Have human technicians review and update the manuals manually.

Question 2

A company is adopting generative AI for customer support. Which TWO strategies should they implement to manage risks related to brand reputation?

Accepted Answer

Establish a human-in-the-loop escalation process for sensitive interactions.. A human-in-the-loop escalation process ensures that sensitive or ambiguous customer interactions are reviewed by a human agent before an AI-generated response is sent. This directly mitigates brand reputation risk by preventing the AI from inadvertently making offensive, legally problematic, or factually incorrect statements that could go viral. The human reviewer acts as a safety net, catching edge cases that automated filters might miss, such as nuanced sarcasm or cultural insensitivity.

Answer

Publish a disclaimer that the AI may make mistakes.

Answer

Deploy the model without any content filters to maximize helpfulness.

Answer

Disable customer support AI entirely to avoid any risk.

Question 3

A healthcare provider is planning to deploy generative AI for clinical note summarization. Which THREE actions are essential for regulatory compliance (e.g., HIPAA)?

Accepted Answer

Implement role-based access controls to limit who can view AI-generated notes.. Role-based access controls (RBAC) are a core requirement under HIPAA's Security Rule (45 CFR § 164.312(a)(1)) to ensure that only authorized personnel can access electronic protected health information (ePHI). In the context of generative AI for clinical note summarization, RBAC prevents unauthorized viewing of AI-generated summaries that may contain sensitive patient data, thereby enforcing the minimum necessary standard.

Answer

Allow clinicians to share AI-generated summaries with anyone in the organization.

Answer

Store raw patient data in model training logs for auditing.

Question 4

An organization uses an IAM policy for Vertex AI as shown. A security audit reveals that engineer@example.com deployed a model that inadvertently exposed sensitive data. What is the most likely reason this happened?

Accepted Answer

The engineer had the aiplatform.user role, which includes permissions to deploy models without additional review.. The `aiplatform.user` role in Vertex AI includes the `aiplatform.model.deploy` permission, which allows any user with that role to deploy models without requiring additional approvals or administrative review. This lack of a secondary authorization step means the engineer could deploy a model that exposed sensitive data, even if the model had not been properly vetted for data leakage.

Answer

Audit logging is not enabled for DATA_WRITE events.

Answer

The admin user did not review the deployment.

Answer

The policy does not include a separation of duties between development and production.

Question 5

A team has developed a generative AI model for real-time translation. The evaluation metrics and business requirements are shown. Which business decision is most appropriate given the trade-offs?

Accepted Answer

Optimize the model for cost efficiency, even if accuracy drops slightly to 90%.. The business requirements prioritize cost efficiency as the primary constraint, and the model currently exceeds the cost target. A slight accuracy drop to 90% (still within acceptable limits) allows cost to be reduced, aligning with the core business goal. The trade-off is acceptable since latency and other metrics remain within bounds, and accuracy at 90% still meets the minimum threshold for real-time translation quality.

Answer

Accept the model as-is because all other metrics are within limits.

Answer

Prioritize latency reduction even if it increases cost.

Answer

Reduce accuracy to 85% to achieve both latency and cost targets.

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.

Answer

BigQuery ML

Answer

Cloud Run

Answer

Vertex AI Workbench

Question 7

A retail company is building a product description generator using a large language model on Vertex AI. They need to ensure the generated descriptions do not contain offensive language. Which strategy should they implement?

Accepted Answer

Implement a content moderation filter (e.g., Perspective API) as a post-processing step. Content moderation filters like Perspective API act as a post-processing safeguard that can catch offensive language the model might generate despite prompt engineering or fine-tuning. This approach provides a deterministic, rule-based or ML-based check that is independent of the model's training, ensuring compliance with content policies in production. It is a standard practice for deploying LLMs in customer-facing applications where safety is critical.

Answer

Fine-tune the model on a dataset of clean product descriptions

Answer

Use Vertex AI Model Monitoring to detect anomalies in model predictions

Answer

Include explicit instructions in the prompt to avoid offensive language

Question 8

A financial services firm is deploying a generative AI chatbot for customer inquiries. They have strict compliance requirements: all conversations must be auditable and the model must not use customer data for training. Which Google Cloud offering should they choose?

Accepted Answer

Vertex AI Agent Builder with data governance controls. Vertex AI Agent Builder is correct because it provides built-in data governance controls that prevent customer data from being used for model training, while also supporting full auditability through integration with Cloud Audit Logs and Cloud Logging. This directly addresses the firm's compliance requirements for auditable conversations and data privacy.

Answer

Private Google Access for on-premises connectivity

Answer

Dialogflow CX with Cloud Logging

Answer

Cloud AI Platform Pipelines

Question 9

An e-commerce company is using a generative AI model to recommend products. They notice that the recommendations are often irrelevant. What is the most likely cause?

Accepted Answer

Inadequate prompt engineering. Inadequate prompt engineering is the most likely cause because generative AI models rely heavily on the quality and specificity of the input prompt to produce relevant outputs. If the prompts used to generate product recommendations are vague, poorly structured, or lack context (e.g., not including user preferences or historical behavior), the model will return generic or irrelevant suggestions. This is a common failure point in recommendation systems where the prompt acts as the primary interface for steering model behavior.

Answer

Using an outdated model version

Answer

Incorrect regional endpoint configuration

Answer

Overfitting on training data

Question 10

A healthcare company wants to use generative AI to summarize patient records. They are concerned about data privacy and HIPAA compliance. Which Google Cloud feature should they use to protect patient data?

Accepted Answer

Customer-managed encryption keys (CMEK) with VPC Service Controls. D is correct because Customer-managed encryption keys (CMEK) with VPC Service Controls provide a defense-in-depth approach for HIPAA compliance. CMEK allows the healthcare company to control and manage the encryption keys used to protect patient data at rest, while VPC Service Controls prevent data exfiltration by restricting data movement outside a defined service perimeter. This combination ensures that even if an attacker gains access, they cannot decrypt the data or move it out of the controlled environment, directly addressing data privacy and HIPAA requirements.

Answer

Cloud Audit Logs

Answer

Confidential VMs

Answer

Cloud Data Loss Prevention (DLP) API

Question 11

A media company is using a generative AI model to create video captions. The model is deployed on Vertex AI with autoscaling. During peak hours, they observe high latency and request timeouts. Which action would most effectively address this issue?

Accepted Answer

Increase the minimum number of replicas in the autoscaling configuration. Increasing the minimum number of replicas ensures that during peak hours, the model already has a baseline of warm instances ready to handle requests, reducing cold-start latency and preventing timeouts. Autoscaling can take time to spin up new replicas, so a higher minimum replica count directly mitigates the latency spike by pre-provisioning capacity.

Answer

Optimize the prompt to reduce output length

Answer

Reduce the maximum number of replicas to limit resource usage

Answer

Switch to a GPU-based machine type for faster inference

Question 12

Which TWO actions are recommended best practices for cost optimization when deploying generative AI models on Vertex AI?

Accepted Answer

Use batch prediction for non-real-time workloads. Batch prediction processes predictions asynchronously in large batches, which is significantly more cost-effective than online (real-time) prediction for workloads that do not require immediate responses. Vertex AI batch prediction jobs automatically scale down to zero when not in use, eliminating idle compute costs, and you only pay for the resources consumed during the job execution.

Answer

Deploy the model in a single region to reduce network costs

Answer

Store all model prediction logs indefinitely for auditing

Answer

Always use GPU instances for inference

Question 13

Which THREE factors should be considered when selecting a foundation model for a generative AI application in a regulated industry?

Accepted Answer

Transparency of the model's training data and sources. In regulated industries (e.g., healthcare, finance), transparency of training data and sources is critical for compliance with regulations like GDPR or HIPAA. Without knowing the provenance and composition of the training data, an organization cannot audit for prohibited content, verify consent, or ensure the model does not inadvertently expose sensitive information. This transparency directly impacts the ability to perform due diligence and meet legal obligations for data usage.

Answer

Latency and throughput requirements

Answer

Size of the model in terms of parameters

Question 14

A data scientist is trying to get online predictions from a Vertex AI endpoint but receives the error shown. What is the most likely cause?

Accepted Answer

The model has not been deployed to the specified endpoint. The error indicates that the model is not deployed to the endpoint. In Vertex AI, an endpoint is a resource that hosts one or more deployed models. If a model has not been deployed to the endpoint, any prediction request to that endpoint will fail with a 'model not found' or similar error, even if the endpoint ID and region are correct.

Answer

The region in the request does not match the endpoint region

Answer

The endpoint ID is incorrect

Answer

The model ID is incorrect

Question 15

A large enterprise is deploying a generative AI-powered code assistant for their developers. The solution uses Vertex AI with a fine-tuned Codey model. The security team requires that all prompts and responses be logged for audit purposes, but the logs must not contain sensitive information such as API keys or passwords. The operations team is concerned about high latency during peak usage. You need to design a solution that meets security requirements without compromising performance. Which approach should you take?

Accepted Answer

Enable Vertex AI model monitoring with Cloud Logging, and configure a log sink with a custom exclusion filter to redact sensitive patterns before storing. It uses Vertex AI model monitoring with Cloud Logging to capture prompts and responses, then applies a custom exclusion filter with a log sink to redact sensitive patterns (e.g., API keys, passwords) in real time before logs are stored. This meets the security requirement for audit logging without sensitive data while avoiding the latency overhead of post-processing or a custom proxy, thus satisfying the operations team's performance concern.

Answer

Use Cloud Audit Logs to capture all API calls to Vertex AI, but do not log the actual prompts and responses

Answer

Log all prompts and responses to Cloud Storage and use a Cloud DLP job to scan and redact sensitive data periodically

Answer

Implement a custom proxy that logs all requests after stripping sensitive data, then forward to the model