Generative AI Leader Business Strategies for Generative AI Solutions Practice Test 5 — 15 Questions

Question 1

A large enterprise is evaluating gen AI for internal knowledge management. They need to ensure accuracy and reduce hallucinations. Which strategy is most effective?

Accepted Answer

Use Retrieval-Augmented Generation (RAG). Retrieval-Augmented Generation (RAG) is the most effective strategy because it grounds the model's responses in an external, authoritative knowledge base, retrieving relevant documents at inference time to provide factual context. This directly reduces hallucinations by ensuring the generated output is based on retrieved evidence rather than relying solely on the model's parametric memory, which is critical for enterprise knowledge management where accuracy is paramount.

Answer

Fine-tune a model on domain-specific data

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Increase model temperature

Answer

Use a larger model without customization

Question 2

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. Multi-region deployment with Vertex AI (option B) is correct because it allows serving models in close proximity to users across different regions, reducing latency while complying with data sovereignty requirements. Option A (single global deployment with CDN) is incorrect because a single deployment may violate data residency laws and CDN does not address compute locality for model inference. Option C (third-party API) is incorrect because relying on external APIs may not provide the necessary control over data placement and may introduce additional compliance risks. Option D (on-premises only) is incorrect because it is costly, limits scalability, and may not be feasible for global reach.

Answer

Single global deployment with CDN

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Use a third-party API

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On-premises deployment only

Question 3

A company wants to estimate the total cost of ownership (TCO) for a gen AI solution on Google Cloud. Which factors are most important?

Accepted Answer

Compute, storage, and API call costs. The total cost of ownership (TCO) for a generative AI solution on Google Cloud encompasses all operational expenses, including compute (e.g., TPU/GPU instances for training and inference), storage (e.g., Cloud Storage for datasets and model artifacts), and API call costs (e.g., Vertex AI prediction requests). Focusing on a single cost component, such as training or inference alone, ignores the recurring expenses of serving the model and storing data, which often dominate long-term TCO.

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Only model training cost

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Only inference cost

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Only compute cost

Question 4

A marketing agency uses gen AI for content generation. They need to brand consistently. What is a key business consideration?

Accepted Answer

Implement content moderation and brand guidelines. Consistent branding requires enforcing predefined guidelines on tone, style, and terminology across all generated content. Without content moderation and brand guidelines, a generative AI model may produce off-brand, inconsistent, or even harmful outputs, undermining brand identity. This is a core business strategy for deploying gen AI at scale, ensuring alignment with marketing objectives.

Answer

Use only generated content

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Use the most creative model

Answer

Optimize for speed

Question 5

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. 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 6

A company is considering using gen AI for customer support. Which two business strategies are most important for success?

Accepted Answer

Measure customer satisfaction metrics. Measuring customer satisfaction metrics (A) is critical because it provides quantitative feedback on the generative AI system's performance, enabling iterative improvements to the model's responses and alignment with business goals. Without metrics like CSAT or NPS, the company cannot validate whether the AI is reducing resolution time or improving user experience, which are key ROI indicators for gen AI deployments.

Answer

Ignore data privacy

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Deploy without testing

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Use the cheapest model

Question 7

A business leader is developing a gen AI strategy. Which three key components should be included in the strategy?

Accepted Answer

Plan for responsible AI. Responsible AI is a foundational component of any generative AI strategy, ensuring ethical use, bias mitigation, and compliance with emerging regulations. Without a plan for responsible AI, the organization risks reputational damage, legal liability, and deployment failures due to lack of trust. This goes beyond simple fairness checklists to include continuous monitoring of model outputs for toxicity, hallucination, and privacy violations.

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Focus solely on technology

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Involve stakeholders across departments

Question 8

A financial services firm must comply with regulations when using gen AI. Which two measures are critical?

Accepted Answer

Implement audit trails. Audit trails are critical for compliance because they provide a tamper-evident, chronological record of all AI model inputs, outputs, and decisions. This enables firms to demonstrate regulatory adherence (e.g., under GDPR or SOX) by reconstructing the exact sequence of events that led to a specific AI-generated output, which is essential for accountability and forensic review.

Answer

Deploy without risk assessment

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Use a closed-source model

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Use only synthetic data

Question 9

An ML engineer sees the above deployment output. The business wants to reduce inference cost. Which action should they take?

Accepted Answer

Change to a lower-cost machine type. Switching to a lower-cost machine type directly reduces the per-request compute cost without altering the model architecture or inference logic. This is a common cost-optimization strategy in cloud-based ML deployments, where instance types (e.g., from GPU to CPU or from a larger to a smaller GPU) can be selected based on latency and throughput requirements, provided the model fits within the machine's memory and compute constraints.

Answer

Use a larger model

Answer

Deploy to multiple regions

Answer

Increase traffic split

Question 10

A developer is using Vertex AI with an API key and gets the above error. What is the likely cause?

Accepted Answer

Expired API key. The error indicates that the API key used to authenticate with Vertex AI is no longer valid. API keys can expire due to a configured expiration policy or if they have been revoked in the Google Cloud Console. Since the developer is using an API key directly (rather than a service account or OAuth token), an expired key is the most direct cause of an authentication failure.

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Wrong endpoint

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Excessive quota

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Insufficient permissions

Question 11

A retail company with a large FAQ database wants to build a generative AI customer service chatbot that can answer questions accurately with up-to-date information. Which business strategy should they prioritize?

Accepted Answer

Use retrieval-augmented generation (RAG) with vector search on the FAQ database.. Retrieval-augmented generation (RAG) with vector search allows the chatbot to dynamically retrieve the most relevant, up-to-date FAQ entries from a large database at inference time, grounding the generative model's responses in verified content without requiring retraining. This approach combines the flexibility of a pre-trained language model with the accuracy of real-time information retrieval, ensuring answers reflect the latest FAQ updates.

Answer

Train a new model from scratch using the FAQ data.

Answer

Fine-tune a foundational model on the entire FAQ dataset.

Answer

Use a general-purpose language model without any customization.

Question 12

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 13

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.. 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.

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Use a black-box large language model with monitoring.

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

Question 14

Refer to the exhibit. A sudden surge of traffic reaches 15,000 requests per second, but the endpoint can only handle 1,000 req/s per replica. What will happen to new requests?

Accepted Answer

They will receive HTTP 429 (Too Many Requests) errors.. When a surge of 15,000 requests per second hits an endpoint configured with a maxReplicaCount (e.g., 10 replicas at 1,000 req/s each = 10,000 req/s capacity), any excess requests beyond that capacity are rejected with an HTTP 429 (Too Many Requests) status code. This is standard behavior in autoscaling systems: once the replica count reaches its maximum limit, the service cannot scale further, and new requests are throttled to prevent overload.

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They will be processed, and replicas will exceed maxReplicaCount.

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They will be redirected to a different model.

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They will be queued until capacity becomes available.

Question 15

Refer to the exhibit. A developer receives this error when trying to call a model for prediction. What is the most likely cause?

Accepted Answer

The developer's service account lacks the required IAM role.. The error when calling a model for prediction most likely stems from the developer's service account lacking the required IAM role. In Google Cloud AI Platform, the 'aiplatform.user' or 'aiplatform.predictor' role is necessary to invoke prediction endpoints; without it, the API returns a permission-denied error. This is a common misconfiguration when service accounts are created without explicit roles attached.

Answer

The project has exceeded its prediction quota.

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The model version has been deprecated.

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The model is not deployed on an endpoint.