Generative AI Leader Practice Test 3 — 25 Questions

Question 1

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. Option D is correct because 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

Question 2

A startup is building a customer support chatbot using Vertex AI and wants to ground responses in their product documentation to reduce hallucinations. Which approach should they use?

Accepted Answer

Enable Vertex AI Grounding with a custom enterprise data store containing the documentation.. Vertex AI Grounding with a custom enterprise data store is the correct approach because it allows the chatbot to retrieve and cite specific chunks from the product documentation in real time, directly reducing hallucinations by constraining responses to verified content. This method uses the underlying grounding service to query a vector-based data store (powered by Vertex AI Search) and append source references to the model's output, ensuring factual accuracy without retraining.

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Use the Codey API for text generation.

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Use the base model without any grounding to maximize flexibility.

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Fine-tune the model on the documentation and deploy.

Question 3

A machine learning engineer is tuning a large language model on Vertex AI for question answering. They want to evaluate the model's performance before deployment. Which THREE metrics should they consider?

Accepted Answer

F1 score. The F1 score is a key metric for evaluating question-answering models because it measures the harmonic mean of precision and recall, capturing how well the model's predicted answer overlaps with the ground truth answer. In the context of Vertex AI and large language models, F1 score is particularly useful when answers are not exact matches but contain partial correct information, making it a standard evaluation metric for extractive and generative QA tasks.

Answer

Cost per training epoch

Answer

Training time per epoch

Question 4

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. Option B is correct because 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 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

Refer to the exhibit. This JSON describes a Vertex AI endpoint with a deployed model. Which statement about scaling is true?

Accepted Answer

The endpoint uses only dedicated resources, no automatic scaling. Option A is correct because the JSON shows that the endpoint is configured with `dedicatedResources` and no `autoscalingMetricSpecs` or `minReplicaCount`/`maxReplicaCount` fields. In Vertex AI, when you specify only `machineSpec` and a fixed `minReplicaCount` (here implicitly 1) without a `maxReplicaCount` or autoscaling metrics, the endpoint uses dedicated resources with no automatic scaling — the model will always run on exactly the number of replicas you define, regardless of load.

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The endpoint will automatically scale based on GPU utilization

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The endpoint will scale from 1 to 3 replicas based on load using automatic scaling

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The endpoint can scale to zero when not in use

Question 7

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 8

A healthcare provider plans to implement gen AI for clinical note summarization. They have limited AI expertise. Which Google Cloud approach best aligns with their business strategy?

Accepted Answer

Use Vertex AI Agent Builder with pre-built templates. Vertex AI Agent Builder provides pre-built templates and a low-code interface specifically designed for organizations with limited AI expertise. It enables rapid deployment of generative AI solutions like clinical note summarization without requiring deep data science skills, directly aligning with the healthcare provider's business strategy of minimizing technical overhead while leveraging AI.

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Hire a team of data scientists

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Deploy an open-source model on Compute Engine

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Build a custom model from scratch

Question 9

A developer is tuning a text-generation model for creative writing. They want the outputs to be more diverse and less repetitive. Which THREE parameters/changes can help? (Choose three.)

Accepted Answer

Increase temperature to 0.9. Increasing temperature to 0.9 raises the randomness of the probability distribution over the vocabulary, making the model more likely to sample less probable tokens. This directly increases output diversity and reduces repetitiveness by flattening the softmax curve, which is a standard technique for creative generation.

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Reduce top-k to 10

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Reduce frequency penalty to 0.0

Question 10

A large enterprise runs a generative AI solution serving millions of daily inference requests. To reduce costs, they propose using serverless endpoints (Vertex AI Prediction) with a custom container, but they notice high latency during cold starts. Which strategy best addresses this problem while minimizing cost?

Accepted Answer

Set a minimum number of replicas to maintain a baseline of always-on instances.. Option A is correct because setting a minimum number of replicas ensures that a baseline of always-on instances is maintained, eliminating cold starts for the majority of requests. This directly addresses the latency spike caused by container initialization and model loading in serverless endpoints, while the cost impact is limited to the minimum replicas rather than scaling all instances.

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Upgrade to GPU-accelerated machines for all replicas.

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Implement client-side request batching to reduce the number of inference calls.

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Use prewarmed containers by setting an idle timeout to keep instances alive.

Question 11

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 12

A company is building a search application that requires grounding answers in their internal knowledge base. They want to use Vertex AI Search and Conversation with a custom datastore. Which configuration is essential to ensure the model only answers based on their documents?

Accepted Answer

Configure the answer generation to use grounding with the enterprise datastore as the source.. Option C is correct because Vertex AI Search and Conversation provides a built-in grounding capability that explicitly ties answer generation to a specified enterprise datastore. By configuring grounding with the custom datastore as the source, the model is constrained to retrieve and synthesize answers exclusively from the indexed documents, preventing reliance on its parametric knowledge or external sources.

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Enable streaming responses to get real-time answers.

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Fine-tune the model on the company's documents.

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Set the model's temperature to 0 to make responses deterministic.

Question 13

What are THREE best practices for responsible generative AI deployment?

Accepted Answer

Monitor model performance and data drift over time. Option A is correct because 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.

Answer

Maximize model size for best accuracy

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Avoid fine-tuning the model to preserve original capabilities

Question 14

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. Option C is correct because 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.

Answer

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 15

A real-time customer support chatbot using Gemini is experiencing high latency. The team must maintain response quality while improving speed. Which technique should they implement?

Accepted Answer

Use context caching for frequent queries. Context caching reduces latency by storing frequently accessed query responses or intermediate computations, allowing the chatbot to reuse precomputed results instead of reprocessing identical or similar requests through the full model pipeline. This directly addresses high latency in real-time systems while preserving response quality, as the cached outputs are identical to freshly generated ones for the same input.

Answer

Switch to a larger model

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Increase the batch size

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Decrease the temperature

Question 16

Which TWO are advantages of using Retrieval-Augmented Generation (RAG) over fine-tuning?

Accepted Answer

No need to retrain the base model. Option A is correct because RAG retrieves relevant external knowledge at inference time without modifying the base model's parameters, eliminating the need for retraining. This contrasts with fine-tuning, which requires updating model weights through additional training cycles. RAG thus preserves the original model while augmenting its output with up-to-date information.

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Requires less data preparation

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Lower inference latency

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More secure because model weights are not modified

Question 17

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

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Cost per inference compared to historical average

Question 18

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 19

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.

Answer

Use of only open-source models

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Maximum model size

Question 20

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. Option D is correct because 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 = $600,000, totaling $670,000. The API approach costs $0.10 per 1K tokens * 5K tokens * 50,000 reports * 12 = $3,000,000, making fine-tuning significantly cheaper at scale despite the upfront investment.

Answer

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