Generative AI Leader Business Strategies for Generative AI Solutions Practice Test 7 — 15 Questions

Question 1

A financial institution wants to deploy a generative AI solution for contract analysis. They need to ensure compliance with regulations. Which approach is best?

Accepted Answer

Fine-tune a model on a curated dataset of past contracts and implement human-in-the-loop review. Best because fine-tuning on a curated dataset of past contracts ensures the model learns domain-specific language and compliance patterns, while human-in-the-loop review provides a critical safety net for regulatory adherence. This combination directly addresses the need for accuracy and accountability in contract analysis, where errors can have legal consequences.

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Deploy a large open-source model fine-tuned on public legal documents

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Use a general-purpose pre-trained model with no modifications to minimize risk

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Implement retrieval-augmented generation (RAG) with the company's legal document database

Question 2

A startup is deciding between using a pre-trained model via API vs. hosting their own open-source model. Which factor is most critical for their decision?

Accepted Answer

Total cost of ownership including infrastructure and expertise. Total cost of ownership (TCO) is the most critical factor because it encompasses not only the direct costs of infrastructure (compute, storage, networking) but also the hidden costs of expertise (MLOps engineers, security hardening, ongoing maintenance) and opportunity costs. A pre-trained API may have higher per-token costs but lower upfront investment, while self-hosting an open-source model requires significant capital expenditure on GPUs, cooling, and power, plus the operational burden of scaling inference under variable load. This decision directly impacts the startup's burn rate and runway, making TCO the primary driver for a resource-constrained organization.

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The accuracy on a benchmark dataset

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The number of parameters in the model

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The level of community support for the open-source model

Question 3

A company's generative AI model is producing biased outputs. What is the most effective mitigation strategy?

Accepted Answer

Fine-tune the model using a balanced, representative dataset and implement output filtering. Fine-tuning on a balanced, representative dataset directly addresses the root cause of biased outputs by correcting the model's learned associations, while output filtering provides a safety net to catch residual bias. This combination is more effective than superficial fixes because it modifies the model's internal weights rather than just masking outputs.

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Use a larger model with more parameters to improve overall accuracy

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Use prompt engineering to instruct the model to avoid biased language

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Increase the diversity of input samples by random sampling

Question 4

A company is evaluating the ROI of a generative AI project. Which metric is most appropriate?

Accepted Answer

Reduction in time to complete tasks using the generative AI tool. The primary business justification for a generative AI project is operational efficiency, measured directly by the reduction in time to complete tasks. Unlike technical metrics such as model error rate, this metric ties the AI's output to tangible productivity gains, which is the core of ROI analysis in a business context. Generative AI tools are designed to augment human workflows, so time savings translate into cost savings and increased throughput, making it the most appropriate metric for evaluating return on investment.

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Reduction in model error rate on a test set

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Increase in user satisfaction scores

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Cost per inference compared to historical average

Question 5

A healthcare organization wants to use generative AI for medical report summaries. What is the primary concern?

Accepted Answer

Ensuring HIPAA compliance and data security when using cloud AI services. The primary concern for a healthcare organization using generative AI for medical report summaries is ensuring HIPAA compliance and data security when using cloud AI services. Medical data is protected health information (PHI), and any cloud-based AI service must have a Business Associate Agreement (BAA) in place and enforce encryption at rest and in transit to avoid regulatory penalties and data breaches.

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The model's ability to generate fluent and coherent summaries

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Minimizing the cost of each API call to stay within budget

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Latency of responses for real-time use cases

Question 6

A company has a generative AI model that is too slow for real-time inference. What architectural change would help?

Accepted Answer

Apply model quantization and deploy on TPUs. Model quantization reduces the precision of the model's weights (e.g., from FP32 to INT8), which significantly decreases memory footprint and computation time, enabling faster inference. Deploying on TPUs (Tensor Processing Units) further accelerates matrix operations through specialized hardware, making this combination ideal for real-time latency requirements.

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Switch to a larger, more accurate model

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Deploy the model on more powerful CPUs

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Use distributed training across multiple GPUs

Question 7

A company is choosing a generative AI model for code generation. Which TWO considerations are most important?

Accepted Answer

Whether the model's training data includes the target programming languages. A generative AI model for code generation must have been trained on the target programming languages to produce syntactically and semantically correct code. Without such training data, the model cannot understand language-specific syntax, libraries, or idioms, leading to irrelevant or erroneous outputs.

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The total number of model parameters

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The open-source license of the model

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The latency of the model's inference endpoint

Question 8

What are THREE best practices for responsible generative AI deployment?

Accepted Answer

Monitor model performance and data drift over time. Continuous monitoring of model performance and data drift is essential for maintaining the reliability and safety of generative AI systems. Data drift occurs when the statistical properties of input data change over time, which can degrade model accuracy and introduce unintended biases. Regular monitoring allows teams to detect these shifts early and retrain or adjust the model to sustain responsible behavior.

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Maximize model size for best accuracy

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Avoid fine-tuning the model to preserve original capabilities

Question 9

A company is considering whether to use Vertex AI's Generative AI Studio. Which TWO are benefits?

Accepted Answer

It integrates seamlessly with Vertex AI Pipelines for MLOps. Vertex AI Generative AI Studio is designed to work natively with Vertex AI Pipelines, enabling users to incorporate generative models into end-to-end MLOps workflows for automation, monitoring, and retraining. This integration allows seamless orchestration of prompt tuning, model evaluation, and deployment within the same managed environment, reducing operational overhead.

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It is always cheaper than using third-party APIs

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It generates outputs that are always more accurate than custom models

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It requires no coding or machine learning expertise to use

Question 10

A company wants to use GenAI to automate customer support. They have a large knowledge base. Which approach maximizes ROI in the first 6 months?

Accepted Answer

Use a pre-built conversational AI platform with Retrieval-Augmented Generation (RAG). Maximizes ROI in the first 6 months because it leverages a pre-built conversational AI platform integrated with Retrieval-Augmented Generation (RAG). RAG allows the model to dynamically retrieve relevant information from the existing knowledge base at inference time, providing accurate, context-aware responses without the need for costly retraining or custom model development. This approach balances rapid deployment, low upfront investment, and high accuracy, making it the most cost-effective solution for automating customer support quickly.

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Deploy a general-purpose chatbot without customization

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Build a custom LLM from scratch using their data

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Fine-tune a foundation model on historical support tickets

Question 11

A financial services firm is developing a GenAI application for investment advice. They need to ensure regulatory compliance. Which business strategy should they prioritize?

Accepted Answer

Implement strict human-in-the-loop review for all investment recommendations. In regulated industries like financial services, GenAI applications must prioritize compliance over speed. Option B is correct because a human-in-the-loop (HITL) review ensures that every investment recommendation is auditable and meets regulatory standards (e.g., SEC or FINRA rules), mitigating risks of hallucinated or non-compliant outputs. This strategy directly addresses the need for accountability and transparency in high-stakes decision-making.

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Rapidly deploy an MVP and iterate based on user feedback

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Open-source the model to gain community trust

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Partner with a cloud provider that offers indemnification for model outputs

Question 12

Which of the following is a key consideration when selecting a GenAI model for a cost-sensitive application?

Accepted Answer

Latency and throughput requirements. For cost-sensitive applications, latency and throughput requirements directly impact infrastructure costs, as lower latency often requires more expensive compute resources (e.g., higher GPU memory, faster inference hardware) and higher throughput may necessitate scaling out instances. Model size in parameters is a secondary factor that influences latency and throughput, but the primary cost driver is the operational performance needed to meet service-level agreements (SLAs).

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Model size in parameters

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Number of training epochs

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The model's training data source

Question 13

A retail company wants to use GenAI to generate product descriptions. They have a small team of data scientists. What is the most efficient approach?

Accepted Answer

Use a foundation model API with prompt engineering and few-shot examples. Using a foundation model API with prompt engineering and few-shot examples is the most efficient approach for a small team. It leverages pre-trained models (e.g., GPT-4, Claude) via API calls, requiring no infrastructure or training data, while prompt engineering and few-shot examples allow the model to adapt to the company's product catalog with minimal effort and cost.

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Collect more data for several months before starting

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Train a model from scratch using their product data

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Buy a proprietary model from a startup

Question 14

A healthcare startup is exploring GenAI for clinical note summarization. They have concerns about patient data privacy. Which Google Cloud approach best addresses privacy while still using powerful models?

Accepted Answer

Use Vertex AI with data residency controls and no external data sharing. Vertex AI with data residency controls and no external data sharing ensures that patient data remains within specified geographic boundaries and is not used for model training or improvement, directly addressing healthcare privacy regulations like HIPAA. This approach leverages Google Cloud's powerful models while maintaining strict data governance, unlike options that risk data exposure or lack enterprise-grade controls.

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Deploy open-source models on-premises

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Use a third-party API with anonymization of patient data

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Use Vertex AI with model customization (fine-tuning)

Question 15

A company wants to measure the business impact of a GenAI content generation tool. Which metric is most appropriate?

Accepted Answer

Reduction in content production time. The primary business impact of a GenAI content generation tool is operational efficiency, measured by the reduction in content production time. This metric directly correlates to cost savings and faster time-to-market, which are key business outcomes. Unlike technical metrics, it reflects real-world value delivery.

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Number of model parameters

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Model accuracy on a test set

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Training loss