Generative AI Leader Business Strategies for Generative AI Solutions Practice Test 6 — 15 Questions

Question 1

A startup with limited budget wants to quickly test a generative AI use case for personalized email marketing. Which approach minimizes time-to-market and cost?

Accepted Answer

Use a managed API like the PaLM API with prompt engineering.. Using a managed API like the PaLM API with prompt engineering eliminates the need for infrastructure setup, model training, and data preparation. This approach leverages a pre-trained model via a simple REST API call, allowing the startup to iterate on prompts and achieve personalized email content in hours rather than weeks, minimizing both time-to-market and cost.

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Hire a team of AI researchers to build a solution.

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Develop a custom model from scratch.

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Fine-tune a large open-source model on internal data.

Question 2

A company wants to offer a generative AI feature where the output must follow a very specific tone and style as per the brand guidelines. Which strategy is most reliable?

Accepted Answer

Fine-tune a model on a dataset of branded content.. Fine-tuning a model on a dataset of branded content is the most reliable strategy because it adjusts the model's internal weights to consistently produce outputs that match the specific tone and style of the brand. Unlike prompt-based methods, fine-tuning embeds the stylistic constraints directly into the model's parameters, ensuring adherence even for complex or nuanced brand guidelines.

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Post-process the output with a style transfer algorithm.

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Use a general-purpose model with a system prompt describing the style.

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Use a different model for each content type.

Question 3

A global bank wants to deploy a generative AI assistant for employees across multiple European countries, each with strict data residency laws. Which deployment strategy is most compliant?

Accepted Answer

Deploy separate model instances in each country's cloud region.. Deploying separate model instances in each country's cloud region ensures that data never crosses national borders, directly complying with strict data residency laws like the GDPR's data localization requirements. This strategy uses regional cloud infrastructure (e.g., AWS eu-central-1, Azure westeurope) to keep both training and inference data within the specific jurisdiction, avoiding any cross-border data transfer.

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Use a federated learning approach where data stays on-premises.

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Deploy a single model in a US region and use data masking.

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Use a third-party API that processes data outside Europe.

Question 4

A company is using Vertex AI generative models for a high-volume text summarization service. Which two strategies can reduce operational costs?

Accepted Answer

Use batch prediction instead of online prediction.. Batch prediction reduces costs by processing multiple requests in a single batch job, which avoids the per-request overhead and idle compute time associated with online prediction. This is especially cost-effective for high-volume, non-real-time workloads like text summarization, as you pay only for the compute time used during the batch job rather than for each individual inference.

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Increase the model's max output tokens to 2048.

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Implement retry logic with exponential backoff.

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Lower the temperature parameter to 0.

Question 5

A company is establishing governance practices for generative AI models. Which three actions are essential for responsible AI deployment?

Accepted Answer

Use model versioning to track changes.. Model versioning (Option A) is essential because it enables tracking of changes to generative AI models over time, ensuring reproducibility, rollback capability, and compliance with governance policies. Without versioning, it becomes impossible to audit which model produced a specific output, undermining accountability and regulatory adherence.

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Monitor for data leakage from training data.

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Open-source the model to ensure transparency.

Question 6

A company is considering monetizing a generative AI-powered product. Which two business models are most common and viable?

Accepted Answer

Pay-per-use based on tokens consumed.. Pay-per-use based on tokens consumed aligns directly with the operational cost structure of generative AI models, where each inference incurs compute and memory costs proportional to the number of tokens processed. This model allows customers to pay only for what they use, making it viable for variable workloads and avoiding upfront commitment, while providers can scale revenue with usage. It is the most common monetization strategy for API-based generative AI services, such as OpenAI's GPT-4 or Anthropic's Claude, where pricing is explicitly tied to token counts.

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Free with advertising.

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One-time license fee for the model.

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Selling user data collected from interactions.

Question 7

A company is using a generative AI model for internal report generation. They notice costs are high because each request processes large amounts of text. Which business strategy would most effectively reduce costs while maintaining quality?

Accepted Answer

Fine-tune a smaller model on a specialized dataset.. Fine-tuning a smaller model on a specialized dataset reduces computational cost per inference because smaller models have fewer parameters and require less memory and processing power. By tailoring the model to the company's specific domain (e.g., internal reports), it can maintain output quality comparable to a larger general-purpose model, directly addressing the cost-per-request issue without sacrificing accuracy.

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Use a more powerful model to reduce retries.

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Implement caching for repeated requests.

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Increase the batch size for online predictions.

Question 8

A company wants to use generative AI for creative content generation (e.g., marketing copy). They need to ensure the content is original and does not plagiarize existing materials. Which combination of strategies is most effective?

Accepted Answer

Use a retrieval-augmented generation system that explicitly avoids copying.. Retrieval-augmented generation (RAG) systems explicitly retrieve relevant, non-copyrighted or licensed content from a curated knowledge base and generate outputs grounded in that retrieved data, which inherently reduces the risk of verbatim copying. Unlike simple plagiarism checkers or temperature adjustments, RAG combines retrieval with generation to ensure originality by design, making it the most effective strategy for avoiding plagiarism in creative content generation.

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Use a model with a high temperature setting and post-process with plagiarism checker.

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Fine-tune the model on a dataset of already-created content to learn style.

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Limit the model to generate only short snippets.

Question 9

A company wants to scale their generative AI application globally with low latency. Which infrastructure configuration is most suitable?

Accepted Answer

Multiple regional endpoints with traffic routing to the nearest region.. Deploying multiple regional endpoints with traffic routing to the nearest region minimizes latency by directing user requests to the geographically closest inference endpoint. This architecture leverages global load balancing (e.g., using Anycast DNS or HTTP(S) load balancers with backend services in multiple regions) to reduce round-trip time (RTT) and meet latency SLAs for real-time generative AI applications.

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Use a CDN to cache responses.

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On-premises deployment for all regions.

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Single endpoint in us-central1 with high max replicas.

Question 10

A company deployed a Gemini model on Vertex AI for real-time inference. After a week, they notice that some requests return 500 Internal Server Error, and the endpoint is occasionally unreachable. The endpoint is configured with minReplicaCount=1 and maxReplicaCount=2. What is the most likely cause?

Accepted Answer

The endpoint is under-provisioned: minReplicaCount=1 is too low for peak load, causing the single replica to become saturated.. With minReplicaCount=1 and maxReplicaCount=2, the endpoint starts with a single replica. Under peak load, that single replica can become saturated (CPU/memory exhaustion), causing 500 errors and unreachability. Autoscaling can add a second replica, but if the traffic spike is sudden or the scaling metric takes time to trigger, the single replica is overwhelmed before the second instance is provisioned.

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Autoscaling is disabled, so the endpoint cannot handle traffic spikes.

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The model was updated while the endpoint was serving requests.

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The project has reached its Vertex AI endpoint quota.

Question 11

A healthcare company wants to use Gemini to analyze patient records and summarize findings. Which data privacy practice is most critical when using the Gemini API on Vertex AI?

Accepted Answer

Enable Vertex AI Data Governance to mask or redact PII before sending to the API.. Vertex AI Data Governance allows you to configure data masking or redaction of personally identifiable information (PII) before the data is sent to the Gemini API, ensuring compliance with healthcare regulations like HIPAA. This is the most critical practice because it prevents PHI from being exposed to the model or stored in logs, directly addressing the core privacy requirement. Disabling logging alone (Option B) does not prevent PHI from being processed by the model, and fine-tuning with PHI (Option A) introduces significant compliance risks.

Answer

Fine-tune Gemini using PHI to improve accuracy.

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Disable request-response logging in Vertex AI to ensure data is not stored.

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Use the text-davinci-003 model instead of Gemini, as it is more private.

Question 12

A company has a generative AI chatbot on Vertex AI that shows high response latency. They want to reduce latency without significantly increasing cost. Which TWO actions should they take? (Choose two.)

Accepted Answer

Enable streaming responses using server-sent events.. Enabling streaming responses using server-sent events (SSE) allows the chatbot to send tokens incrementally as they are generated, rather than waiting for the full response. This reduces the perceived latency for the end user, as the first token appears much sooner, even though the total generation time may remain similar. This approach directly addresses high response latency without increasing compute cost, as it does not require additional infrastructure or model changes.

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Increase the min_replica_count to keep more instances always warm.

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Use machine types with GPUs.

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Switch to a larger model like Gemini 1.5 Pro for better accuracy.

Question 13

A team is selecting a foundation model for a text summarization use case. They need to consider factors that affect both model performance and production deployment. Which THREE factors are most critical? (Choose three.)

Accepted Answer

Inference latency and throughput capabilities.. Inference latency and throughput are critical for production deployment because they directly determine the user experience and operational cost. A model with high latency may be unsuitable for real-time summarization, while low throughput limits the number of concurrent requests the system can handle, affecting scalability and cost-efficiency.

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Model parameter count (billions of parameters).

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Training data provenance and licensing.

Question 14

A company wants to use Generative AI for customer support chatbots. They are concerned about cost and latency. Which deployment option best balances these concerns?

Accepted Answer

Use a fine-tuned version of a smaller model on Vertex AI with response caching. Using a fine-tuned smaller model on Vertex AI with response caching reduces both cost and latency. Smaller models require fewer computational resources, and caching avoids redundant inference calls, directly addressing the company's concerns without sacrificing accuracy for the specific task.

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Deploy an open-source model on-premise to avoid cloud costs

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Rely on a third-party chatbot API that abstracts the model

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Use the largest available foundation model via API for highest accuracy

Question 15

A retailer wants to generate personalized product descriptions using PaLM API. They have concerns about data privacy. What is the best practice to mitigate these concerns?

Accepted Answer

Enable data residency and use prompt engineering to avoid including personally identifiable information. Data residency ensures customer data is processed and stored within a specific geographic region, addressing regulatory compliance, while prompt engineering allows the retailer to avoid sending PII to the PaLM API entirely. This combination mitigates privacy risks without requiring custom model training or relying solely on encryption, which does not prevent the API from processing sensitive data.

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Train a custom model from scratch on proprietary data stored on-premise

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Use the PaLM API directly with anonymized customer data

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Encrypt all data in transit and at rest using customer-managed encryption keys