Generative AI Leader Business Strategies for Generative AI Solutions Practice Test 2 — 15 Questions

Question 1

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.

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Using an outdated model version

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Incorrect regional endpoint configuration

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Overfitting on training data

Question 2

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.

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Cloud Audit Logs

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Confidential VMs

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Cloud Data Loss Prevention (DLP) API

Question 3

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.

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Optimize the prompt to reduce output length

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Reduce the maximum number of replicas to limit resource usage

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Switch to a GPU-based machine type for faster inference

Question 4

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. Option A is correct because 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.

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Deploy the model in a single region to reduce network costs

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Store all model prediction logs indefinitely for auditing

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Always use GPU instances for inference

Question 5

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. Option A is correct because 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.

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Latency and throughput requirements

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Size of the model in terms of parameters

Question 6

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.

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The region in the request does not match the endpoint region

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The endpoint ID is incorrect

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The model ID is incorrect

Question 7

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 spec defines 'eval_dataset' as a componentInput, but it is not defined in the root's inputDefinitions (option B). The runtimeConfig has the value, but the pipeline spec does not declare the parameter. The component (A) may be fine. The constant (C) is for project. The runtimeConfig (D) is correct but the spec is missing the input definition.

Answer

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 8

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. Option B is correct because 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.

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

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

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

Question 9

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. Enabling global endpoint with automatic traffic splitting and increasing min replicas per region (option D) provides both failover and capacity. Simply increasing replicas in europe (A) doesn't help if traffic shifts. Global endpoint without min replicas (B) still risks cold starts. Using Cloud CDN (C) is for static content, not model inference.

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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 10

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.. Option B is correct because grounding the model with a knowledge base and using prompt engineering to restrict PII directly addresses hallucinations and compliance without high cost or latency. Option A is too complex and expensive for limited budget. Option C increases latency further due to multi-hop reasoning. Option D removes the RAG capability, increasing hallucination risk.

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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 11

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. Option C is correct because provisioned throughput provides fixed capacity with predictable monthly cost, ideal for managing cost uncertainty. Option A (pay-as-you-go) is variable. Option B (committed use discounts) requires commitment but still variable if usage exceeds. Option D (free tier) is too limited.

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Committed use discounts

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Free tier

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Pay-as-you-go

Question 12

A retail company plans to use Vertex AI's generative AI to create product descriptions. They need to ensure descriptions are factually accurate and do not misrepresent products. Which strategy should they prioritize?

Accepted Answer

Implement human-in-the-loop review. Human-in-the-loop (HITL) review is the correct strategy because it directly addresses the need for factual accuracy and prevention of misrepresentation. While generative AI can produce fluent text, it lacks a reliable grounding mechanism for product-specific facts, making human oversight essential to catch hallucinations, verify claims, and ensure compliance with advertising standards. This approach aligns with responsible AI practices and is a core recommendation for high-stakes content generation.

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Use prompt engineering

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Use a larger model

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Increase temperature parameter

Question 13

A financial institution wants to use generative AI to generate personalized investment advice. They face strict regulatory requirements on explainability and bias. Which approach should they take?

Accepted Answer

Use a RAG system with curated proprietary data. Option C is correct because a Retrieval-Augmented Generation (RAG) system allows the financial institution to ground generative AI outputs in curated, proprietary data sources (e.g., regulatory guidelines, client risk profiles, historical performance). This approach enhances explainability by enabling traceable citations back to specific documents, and reduces bias by controlling the data fed to the model, which is critical for meeting strict regulatory requirements like GDPR or SEC rules on algorithmic fairness.

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Use a foundation model with prompt engineering

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Use a custom model trained from scratch

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Use a closed-source model with vendor lock-in

Question 14

An e-commerce company uses a generative AI model to generate marketing copy. They notice that the model occasionally produces off-brand or inappropriate content. What is the best way to mitigate this?

Accepted Answer

Fine-tune the model on brand guidelines. Option B is correct because fine-tuning on brand guidelines directly addresses brand-specific content issues. Option A (safety filter) is too broad. Option C (reducing temperature) affects creativity but not brand adherence. Option D (increasing top-k sampling) increases diversity, not control.

Answer

Reduce the model's temperature

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Increase the model's top-k sampling

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Use a safety filter

Question 15

A startup wants to leverage Google Cloud's generative AI but has limited ML expertise. Which Google Cloud service allows them to build generative AI applications without deep ML knowledge?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio is a managed service that provides a low-code/no-code interface for building, testing, and deploying generative AI applications using pre-trained foundation models. It abstracts away the complexities of model training, infrastructure management, and ML pipeline orchestration, enabling teams with limited ML expertise to leverage generative AI capabilities through simple prompts and visual workflows.

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Cloud TPU

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TensorFlow

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Apigee