Generative AI Leader Business Strategies for Generative AI Solutions Practice Test 8 — 15 Questions

Question 1

A team built a GenAI chatbot that uses a vector database to retrieve context. Users report irrelevant responses. What is the most likely business strategy issue?

Accepted Answer

The embedding model is not aligned with the domain vocabulary. Irrelevant responses in a RAG (Retrieval-Augmented Generation) chatbot most often stem from the embedding model failing to capture domain-specific semantics. If the embedding model was trained on general text (e.g., Wikipedia) but the chatbot operates in a specialized field like legal or medical, the vector similarity search will retrieve context that is semantically distant from the user's query, leading to irrelevant answers. This is a business strategy issue because the team chose an embedding model that does not align with their domain vocabulary, undermining the entire retrieval pipeline.

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The model is too small to generate accurate responses

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The chatbot is too verbose

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The system is overfitting to the training data

Question 2

An enterprise wants to adopt GenAI across departments but faces resistance from legal and compliance. Which strategy should the AI leader prioritize?

Accepted Answer

Build a comprehensive governance framework covering data use, review, and monitoring. Legal and compliance resistance stems from concerns about data privacy, regulatory adherence, and model accountability. A comprehensive governance framework directly addresses these by defining data usage policies, implementing review mechanisms for model outputs, and establishing continuous monitoring to detect drift or bias, which is essential for enterprise-grade GenAI deployment.

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Outsource the entire initiative to a consulting firm

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Deploy a single pilot in a low-risk department to demonstrate value

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Mandate use of GenAI through executive order

Question 3

A company is choosing between Google's Gemini API and an open-source model. Which factor is most important for a business with limited ML expertise?

Accepted Answer

Ease of integration and availability of support. For a business with limited ML expertise, ease of integration and availability of support are paramount because they reduce the need for in-house machine learning engineering talent. Google's Gemini API offers managed infrastructure, pre-built SDKs, and enterprise-grade support (e.g., SLA-backed uptime, dedicated account management), which directly lowers the barrier to entry and operational risk. In contrast, open-source models require significant expertise for deployment, scaling, and troubleshooting, making them unsuitable for teams without deep ML skills.

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Model parameter count

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Cost per token

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Community size

Question 4

Which TWO factors are most critical when deciding to build a custom GenAI model vs. using a pre-built API? (Select two.)

Accepted Answer

Availability of in-house ML talent. Building a custom GenAI model requires specialized machine learning expertise, including proficiency in frameworks like PyTorch or TensorFlow, experience with distributed training (e.g., using Horovod or DeepSpeed), and the ability to fine-tune architectures like transformers. Without in-house ML talent, the organization cannot effectively manage data curation, hyperparameter tuning, or model evaluation, making a pre-built API the more viable choice. This factor directly determines whether the organization has the technical capacity to undertake custom development.

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Number of layers in the model

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Brand reputation of the model provider

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Volume of expected inference requests

Question 5

An organization is developing a GenAI strategy for multiple business units. Which THREE steps should they take to ensure alignment? (Select three.)

Accepted Answer

Establish common data governance policies. Establishing common data governance policies (C) ensures that all business units adhere to consistent standards for data quality, privacy, and security, which is critical for training and deploying reliable GenAI models. Without unified governance, disparate data practices can lead to model bias, compliance violations, and integration failures across the organization.

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Implement a chargeback model for usage costs

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Allow each business unit to independently choose models

Question 6

Which THREE are essential components of a responsible AI strategy for GenAI? (Select three.)

Accepted Answer

Human oversight for critical decisions. Human oversight for critical decisions (C) is essential because GenAI models can produce plausible but incorrect or harmful outputs. A responsible AI strategy mandates that a human-in-the-loop reviews high-stakes outputs, such as medical diagnoses or financial approvals, to prevent automated errors from causing real-world harm. This aligns with the principle of human accountability in AI governance frameworks like the NIST AI Risk Management Framework.

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Use of only open-source models

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Maximum model size

Question 7

A team set a budget alert for their GenAI API usage at $10,000. They received the alert with current spend of $12,500. Which business action is most appropriate as a first step?

Accepted Answer

Review usage patterns and optimize prompt lengths and frequencies. The first step in responding to a budget overrun should be to analyze usage patterns and optimize prompt lengths and frequencies. This approach identifies inefficiencies (e.g., unnecessarily verbose prompts, excessive retries) that directly reduce token consumption and cost without disrupting critical operations. It aligns with the principle of cost optimization before making architectural or policy changes.

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Pause all non-critical use cases immediately

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Switch to a cheaper model provider

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Increase the budget by 50% to $15,000

Question 8

A startup with $500k in seed funding wants to integrate GenAI into their SaaS product for automated report generation. They have 2 ML engineers and expect 10,000 monthly users initially. They estimate that using a foundation model API (e.g., Gemini) will cost $0.10 per 1K tokens, and each report uses about 5K tokens. Alternatively, they could fine-tune an open-source model on their domain data, estimated at $50k for compute and $20k for engineering time, with inference cost of $0.02 per 1K tokens on a dedicated endpoint. Which approach is more cost-effective over the first 12 months assuming 50,000 reports per month?

Accepted Answer

Fine-tune the open-source model because it has lower per-report cost. The total cost of fine-tuning over 12 months is $70,000 upfront plus ($0.02 per 1K tokens × 5K tokens per report × 50,000 reports per month × 12 months) = $70,000 + $60,000 = $130,000. The API approach costs $0.10 per 1K tokens × 5K tokens × 50,000 reports × 12 = $300,000. Fine-tuning is significantly cheaper at scale despite the upfront investment.

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Use the foundation model API because it has lower upfront cost

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Use a combination of both depending on report complexity

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Build a custom model from scratch

Question 9

A retail company wants to integrate generative AI into its customer service chatbot to handle routine inquiries. They have a limited budget and want to launch quickly. Which strategy is most appropriate?

Accepted Answer

Use pre-trained models via Google Cloud's Generative AI Studio API. Using pre-trained models via Google Cloud's Generative AI Studio API allows the company to leverage existing, powerful models without the high cost and time investment of custom development or fine-tuning. This approach enables rapid deployment on a limited budget by simply integrating the API into their chatbot, handling routine inquiries effectively without requiring extensive machine learning expertise or infrastructure.

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Partner with a generative AI vendor for a custom solution

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Fine-tune an open-source model on their customer service logs

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Build a custom LLM from scratch using the company's own data

Question 10

A company deployed a generative AI chatbot using Vertex AI PaLM API for customer support. Users report high latency (average 5 seconds per response). They need to reduce latency without significantly affecting response quality. Which design change should they prioritize?

Accepted Answer

Apply model quantization to the deployed model. Model quantization reduces the precision of the model's weights (e.g., from FP32 to INT8), which decreases the computational load and memory footprint during inference. This directly lowers latency per request on the Vertex AI PaLM API while preserving most of the model's accuracy, making it the most effective single change for reducing response time without significantly degrading quality.

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Migrate the chatbot to run on edge devices

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Increase the batch size of inference requests

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Switch to a larger, more powerful foundation model

Question 11

A healthcare provider wants to use generative AI to automatically draft clinical notes from doctor-patient conversations. They must comply with HIPAA and ensure patient data privacy. Which strategy best meets their requirements?

Accepted Answer

Use Google Cloud Healthcare API integrated with Vertex AI. Google Cloud Healthcare API with Vertex AI provides a HIPAA-compliant, managed environment that integrates generative AI capabilities directly with healthcare data. The Healthcare API enforces data residency, access controls, and audit logging, while Vertex AI allows fine-tuning or using foundation models without exposing PHI to public endpoints. This combination ensures patient data privacy and regulatory compliance without requiring on-premises infrastructure.

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Outsource note generation to a third-party HIPAA-compliant vendor

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Deploy a custom model on-premises with strict access controls

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Use a public LLM with a data anonymization pipeline

Question 12

Which TWO are key business considerations when adopting generative AI solutions?

Accepted Answer

Data privacy and compliance requirements. Data privacy and compliance requirements (Option C) are a key business consideration because generative AI models often process sensitive or proprietary data, and regulations like GDPR, HIPAA, or CCPA mandate strict controls on data handling, storage, and model training. Failure to address these can result in legal penalties, reputational damage, and loss of customer trust, making it a top priority for enterprise adoption.

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Training duration on public datasets

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Number of model parameters

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Model accuracy on benchmarks

Question 13

Which THREE are best practices for responsible deployment of generative AI in a customer-facing application?

Accepted Answer

Implement human-in-the-loop review for sensitive outputs. Human-in-the-loop (HITL) review ensures that sensitive outputs—such as those involving protected health information (PHI), personally identifiable information (PII), or high-stakes decisions—are vetted by a human before reaching the customer. This mitigates the risk of harmful or biased generations that automated guardrails might miss, aligning with responsible AI principles like accountability and safety.

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Train the model on all available data to maximize coverage

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Use only small models to reduce risk

Question 14

Which TWO strategies can effectively reduce the operational costs of a generative AI model in production without significantly degrading user experience?

Accepted Answer

Cache frequent prompt completions. Caching frequent prompt completions reduces operational costs by eliminating redundant inference calls for identical or similar user requests. This directly lowers compute usage and latency without degrading user experience, as cached responses are served instantly. It is a common optimization in production LLM deployments, especially for high-traffic applications with repetitive queries.

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Use larger batch sizes for inference

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Increase the frequency of model retraining to improve efficiency

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Deploy multiple models and route requests by complexity

Question 15

A small marketing agency with 10 employees is exploring generative AI to create personalized ad copy for their clients. They have a limited budget of $5,000 per month and no in-house machine learning expertise. The CEO wants to have a working prototype within two weeks to show to a potential client. The agency's data is sensitive and cannot be shared with unauthorized third parties. Which strategy should they pursue?

Accepted Answer

Use Google's Generative AI Studio with pre-trained models via API. Google's Generative AI Studio provides pre-trained models via API, allowing the agency to quickly prototype personalized ad copy without needing in-house ML expertise. This approach respects the $5,000 budget (API usage is cost-effective for small-scale prototyping), meets the two-week timeline (no training required), and ensures data privacy by using Google Cloud's data governance controls (data is not shared with unauthorized third parties).

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Hire a team of data scientists to fine-tune an open-source model

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Use a third-party platform that requires on-premise deployment

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Build a custom foundation model from scratch using their client data