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

Question 1

A media company wants to build a multi-modal generative app that accepts text, image, and video inputs and produces summaries. The app must handle variable-length videos up to 10 minutes. Which architecture is most scalable and cost-effective?

Accepted Answer

Use a pipeline to split videos into short clips, extract key frames, and process with Gemini 1.5 Pro (with context caching) to generate summaries.. Option A is correct because splitting videos into short clips and extracting key frames reduces the computational load and token usage, while Gemini 1.5 Pro's context caching efficiently handles variable-length videos up to 10 minutes by reusing processed context across requests. This approach balances scalability (by avoiding processing entire videos at once) and cost-effectiveness (by minimizing API calls and storage), making it ideal for a multi-modal summarization app.

Answer

Use Video Intelligence API to generate video captions, then feed captions to a text model.

Answer

Convert all inputs to text descriptions and use a text-only model.

Answer

Deploy a single Vertex AI endpoint with a model that can ingest multi-modal data directly.

Question 2

A startup wants to generate concise summaries of long news articles using an LLM on Vertex AI. They prioritize low latency and cost. Which model choice is most appropriate?

Accepted Answer

Use Gemini 1.5 Flash, which is designed for high throughput and low cost.. Gemini 1.5 Flash is optimized for high-throughput, low-latency, and cost-efficient summarization tasks, making it the ideal choice for a startup that needs to process long news articles quickly without incurring high costs. It balances performance and economy, whereas Gemini 1.5 Pro prioritizes accuracy at higher latency and cost, and PaLM 2 Bison is less efficient for this use case.

Answer

Use Gemini 1.5 Pro for the highest accuracy.

Answer

Use PaLM 2 Bison, as it is the most economical.

Answer

Use Vertex AI Text Embeddings, since embeddings can generate summaries.

Question 3

A bank wants to use LLMs to generate responses for customer support chat. All conversations must be logged, and any PII must be masked. The solution must comply with financial regulations. Which combination of Vertex AI services should be used?

Accepted Answer

Use Vertex AI Agent Builder with Data Governance, which can automatically mask PII and log interactions.. Option D is correct because Vertex AI Agent Builder integrates with Data Governance to automatically mask PII and log interactions, meeting both the logging and compliance requirements without custom development. This managed service ensures adherence to financial regulations by providing built-in data loss prevention (DLP) capabilities and audit trails, unlike the other options which require manual or less integrated approaches.

Answer

Deploy a custom model on Cloud Run and write a Cloud Function to mask PII.

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Use Vertex AI Prediction with a custom container that masks PII before inference.

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Use the Gemini API directly with a custom logging solution in Cloud Logging.

Question 4

A company has been using an on-premises ML infrastructure for generative AI and wants to migrate to Google Cloud. They have a pipeline that fine-tunes a large language model weekly using a proprietary dataset. The migration must minimize downtime and data transfer costs. Which approach best addresses these requirements?

Accepted Answer

Use Vertex AI Pipelines to orchestrate the fine-tuning process, and use Vertex AI Managed Datasets to incrementally sync new data with BigQuery as the source.. Option C is correct because Vertex AI Pipelines with Managed Datasets allows incremental data transfer and automates fine-tuning in the cloud, minimizing downtime. Option A is wrong because Cloud Storage Transfer Service is for one-time bulk transfer, causing longer downtime. Option B is wrong because a custom solution on GKE is complex and may not reduce costs. Option D is wrong because AutoML does not support fine-tuning custom large language models.

Answer

Use AutoML to train a new model directly from the dataset without fine-tuning.

Answer

Deploy the existing pipeline on a Google Kubernetes Engine cluster and use Google Cloud Filestore for shared storage.

Answer

Use Cloud Storage Transfer Service to move all data to Cloud Storage, then set up a Vertex AI custom training job to run the fine-tuning.

Question 5

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.. Option B is correct because 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.

Answer

Increase the min_replica_count to keep more instances always warm.

Answer

Use machine types with GPUs.

Answer

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

Question 6

A financial institution is deploying a generative AI solution that generates investment advice. They must ensure fairness, avoid toxic outputs, and comply with regulations like GDPR. Which TWO strategies should they implement? (Choose two.)

Accepted Answer

Use Vertex AI Safety Attributes to filter harmful content in both input and output.. Options B and D are correct because using safety attributes to filter harm and implementing a human-in-the-loop for high-risk outputs are direct measures. Option A is wrong because disabling logging is against compliance. Option C is wrong because training only on compliant data is insufficient for every scenario. Option E is wrong because decreasing temperature does not guarantee fairness.

Answer

Set the model temperature to 0 to eliminate creativity and reduce bias.

Answer

Fine-tune the model exclusively on compliant financial documents.

Answer

Disable request logging to avoid storing sensitive data.

Question 7

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.

Answer

Model parameter count (billions of parameters).

Answer

Training data provenance and licensing.

Question 8

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. Option D is correct because 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.

Answer

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

Answer

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

Answer

Use the largest available foundation model via API for highest accuracy

Question 9

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. Option D is correct because 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.

Answer

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

Answer

Use the PaLM API directly with anonymized customer data

Answer

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

Question 10

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. Option C is 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.

Answer

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

Answer

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

Answer

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

Question 11

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.

Answer

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 12

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.

Answer

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 13

A business wants to build a generative AI application but has limited data science resources. What is the recommended path?

Accepted Answer

Use Vertex AI's AutoML and pre-built APIs to accelerate development. Option C is correct because Vertex AI's AutoML and pre-built APIs lower the barrier to entry for teams without deep machine learning expertise. Option A (building from scratch) requires extensive expertise. Option B (hiring a team) is costly and time-consuming. Option D (buying a SaaS product) may not offer customization.

Answer

Hire a team of ML engineers to develop an in-house solution

Answer

Purchase a third-party generative AI SaaS product off-the-shelf

Answer

Build a custom model from scratch using TensorFlow

Question 14

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. Option A is correct because 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.

Answer

Reduction in model error rate on a test set

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Increase in user satisfaction scores

Answer

Cost per inference compared to historical average

Question 15

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.

Answer

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