Generative AI Leader Practice Exam 3 — 20 Questions

Question 1

Refer to the exhibit. An administrator creates this IAM policy for a Vertex AI project. What is the effect of this policy?

Accepted Answer

Alice can deploy pre-trained models; Bob can create and manage custom model code. Option B is correct because the IAM policy grants Alice the `aiplatform.models.get` permission (allowing her to view and deploy pre-trained models) and grants Bob the `aiplatform.models.create` and `aiplatform.models.update` permissions (allowing him to create and manage custom model code). The policy uses separate bindings for each user, with specific roles that align with these actions.

Answer

Alice can view models; Bob can delete models

Answer

Both have full access to all Vertex AI resources

Answer

Alice can train models; Bob can deploy models

Question 2

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).

Answer

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

Answer

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

Answer

Build a custom foundation model from scratch using their client data

Question 3

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.

Answer

Use the Codey API for text generation.

Answer

Use the base model without any grounding to maximize flexibility.

Answer

Fine-tune the model on the documentation and deploy.

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.

Answer

Only model training cost

Answer

Only inference cost

Answer

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.

Answer

The model's ability to generate fluent and coherent summaries

Answer

Minimizing the cost of each API call to stay within budget

Answer

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.

Answer

The endpoint will automatically scale based on GPU utilization

Answer

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

Answer

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.

Answer

Increase the model's max output tokens to 2048.

Answer

Implement retry logic with exponential backoff.

Answer

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.

Answer

Hire a team of data scientists

Answer

Deploy an open-source model on Compute Engine

Answer

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.

Answer

Reduce top-k to 10

Answer

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.

Answer

Upgrade to GPU-accelerated machines for all replicas.

Answer

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

Answer

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.

Answer

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

Answer

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.

Answer

Enable streaming responses to get real-time answers.

Answer

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

Answer

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

Answer

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

Answer

Train a model from scratch using their product data

Answer

Buy a proprietary model from a startup

Question 15

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

Answer

Increase in user satisfaction scores

Answer

Cost per inference compared to historical average

Question 16

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.

Answer

Rapidly deploy an MVP and iterate based on user feedback

Answer

Open-source the model to gain community trust

Answer

Partner with a cloud provider that offers indemnification for model outputs

Question 17

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

Answer

Maximum model size

Question 18

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

Answer

Use a combination of both depending on report complexity

Answer

Build a custom model from scratch

Question 19

A company is deploying a Gemini 1.0 Ultra model for a code generation assistant. They have set up Vertex AI Model Evaluation with a custom evaluation dataset to measure pass@1 accuracy. The initial evaluation shows 65% pass@1. They want to improve to 80% without collecting more training data. They have already attempted basic prompt engineering (e.g., 'write correct code') with limited improvement. Which approach is most likely to achieve the desired improvement?

Accepted Answer

Use chain-of-thought prompting with few-shot examples of correct code generation.. Chain-of-thought prompting with few-shot examples is the most effective approach because it guides the model through step-by-step reasoning, which is critical for complex code generation tasks. This technique leverages the model's in-context learning ability to improve accuracy without additional training data, directly addressing the need to boost pass@1 from 65% to 80%.

Answer

Reduce the temperature to 0 and set top_p to 1.

Answer

Increase the number of output tokens and enable beam search with width 4.

Answer

Apply reinforcement learning from human feedback (RLHF) using a reward model trained on the existing evaluation dataset.

Question 20

A global e-commerce company is using Vertex AI to build a generative AI chatbot for customer support. The chatbot is powered by the Gemini 1.5 Pro model and uses a vector search index for retrieval-augmented generation (RAG) over product documentation. The company has deployed the application in four regions (us-central1, europe-west4, asia-east1, and australia-southeast1) using a multi-region deployment with a global endpoint. The application is critical and requires high availability with a target latency of under 500ms for the RAG pipeline. Recently, users in Australia are experiencing inconsistent latency spikes, with response times exceeding 2 seconds during peak hours. The team suspects that the issue is related to the vector search index's replication and serving configuration. The index has 10 million embeddings with a dimension of 768. It is stored in a single regional bucket in us-central1, and the vector search index endpoint is deployed in all four regions with the same deployed index ID. The team is using the default configuration for index updates and serving. Which action should the team take to resolve the latency issue for Australian users?

Accepted Answer

Move the vector search index to a multi-regional Cloud Storage bucket (e.g., 'us') to reduce latency for index updates.. The latency spike for Australian users is caused by the vector search index being stored in a single regional bucket in us-central1. When the index is updated, the new embeddings must be rebuilt and streamed from us-central1 to the australia-southeast1 endpoint, introducing significant cross-region latency. Moving the index to a multi-regional Cloud Storage bucket (e.g., 'us') allows the index to be served from a location closer to all regions, reducing the update propagation time and improving consistency for Australian users.

Answer

Create a new regional bucket in australia-southeast1 and store a copy of the index there, then redeploy the vector search index endpoint to use the local bucket.

Answer

Deploy a separate vector search index endpoint for each region with its own index copy stored in a regional bucket in that region.

Answer

Increase the number of replicas for the vector search index in all regions to improve throughput and reduce latency.

Generative AI Leader Practice Exam 3 — 20 Questions

Exhibit

Generative AI Leader Practice Exam 3 — 20 Questions

Exhibit