Generative AI Leader Timed Practice Test 5 — 10 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.

Generative AI Leader Timed Practice Test 5 — 10 Questions

Exhibit

Generative AI Leader Timed Practice Test 5 — 10 Questions

Exhibit