Generative AI Leader Practice Exam 1 — 20 Questions

Question 1

A data scientist is trying to get online predictions from a Vertex AI endpoint but receives the error shown. What is the most likely cause?

Accepted Answer

The model has not been deployed to the specified endpoint. The error indicates that the model is not deployed to the endpoint. In Vertex AI, an endpoint is a resource that hosts one or more deployed models. If a model has not been deployed to the endpoint, any prediction request to that endpoint will fail with a 'model not found' or similar error, even if the endpoint ID and region are correct.

Answer

The region in the request does not match the endpoint region

Answer

The endpoint ID is incorrect

Answer

The model ID is incorrect

Question 2

A data scientist notices that a text generation model deployed on Vertex AI returns repetitive outputs after a few turns in a chat application. What is the most likely cause and the best parameter adjustment?

Accepted Answer

The top_p value is too high; reduce top_p to limit token sampling.. Repetitive outputs in a chat application after a few turns are typically caused by the model getting stuck in a loop due to high cumulative probability from top-p sampling. Reducing top_p limits the set of tokens considered at each step, forcing the model to explore less likely tokens and breaking the repetition cycle. This directly addresses the issue without sacrificing coherence, unlike temperature adjustments which affect randomness globally.

Answer

The max_output_tokens is too low; increase it to allow more diverse output.

Answer

The model is overfitted; switch to a smaller model.

Answer

The temperature is too low; increase temperature to add randomness.

Question 3

A company is deploying a generative AI model for medical diagnosis support. Which THREE considerations are critical for responsible AI?

Accepted Answer

Ensure the training data is diverse and representative.. Option A is correct because diverse and representative training data is critical for responsible AI in medical diagnosis. If the data lacks diversity, the model may exhibit bias, leading to inaccurate or harmful diagnoses for underrepresented groups. This directly impacts fairness, safety, and regulatory compliance in healthcare AI.

Answer

Maximize model throughput to handle high volumes.

Answer

Use the cheapest model to reduce costs.

Question 4

Which THREE considerations are critical when deploying a generative AI model using Vertex AI Endpoints for a latency-sensitive application? (Choose THREE.)

Accepted Answer

Model size and architecture. Model size and architecture directly impact inference latency because larger models with more parameters require more computation per request. For latency-sensitive applications, choosing a smaller or distilled model (e.g., Gemma 2B vs. 27B) or using quantization can reduce response times. Vertex AI Endpoints serve the model as-is, so the model's inherent computational cost is the primary driver of per-request latency.

Answer

Number of model versions

Answer

Number of model instances

Question 5

A company is deploying a generative AI model for customer support. They want to reduce hallucinations while maintaining fluency. They have a large dataset of previous support conversations. Which strategy should they prioritize?

Accepted Answer

Implement retrieval-augmented generation (RAG) using the conversation dataset as a knowledge base.. Retrieval-augmented generation (RAG) directly addresses hallucinations by grounding the model's responses in factual, retrieved data from the conversation dataset. This approach allows the model to generate fluent, contextually relevant answers while reducing the risk of inventing information, as it retrieves actual support interactions as evidence before generating a response.

Answer

Increase the beam search width to 10.

Answer

Fine-tune the model on the conversation dataset.

Answer

Set the temperature to 0.1.

Question 6

Which TWO techniques are commonly used to control the style and tone of a generative model's output?

Accepted Answer

Fine-tuning on a dataset with desired style. Option C is correct because fine-tuning on a dataset that embodies the desired style directly adjusts the model's weights, making it consistently produce outputs with that specific tone and style. This is a fundamental technique for customizing generative models, as it teaches the model the exact patterns, vocabulary, and stylistic nuances present in the training data.

Answer

Adjusting the temperature

Answer

Modifying the top_k value

Answer

Changing the top_p value

Question 7

A startup is building a generative AI content creation tool. They want to minimize operational costs while maintaining low latency for end users. Which deployment strategy should they adopt?

Accepted Answer

Use a serverless inference endpoint that scales to zero when not in use.. Option C is correct because serverless inference endpoints, such as AWS Lambda with SageMaker or Google Cloud Run, automatically scale to zero when idle, eliminating costs during periods of no traffic. This directly addresses the startup's goal of minimizing operational costs while maintaining low latency through rapid cold-start optimizations and provisioned concurrency for burst handling.

Answer

Deploy the model on edge devices to reduce cloud dependency.

Answer

Build an on-premises infrastructure to avoid cloud egress fees.

Answer

Provision dedicated GPU instances for consistent performance.

Question 8

A manufacturing company wants to use generative AI to create maintenance manuals from sensor data. The manuals must be accurate and reflect the latest equipment configurations. Which approach best ensures data freshness and consistency?

Accepted Answer

Use a retrieval-augmented generation (RAG) system that queries a live database of sensor configurations.. Option D is correct because a retrieval-augmented generation (RAG) system retrieves the most current equipment configurations directly from a live database at inference time, ensuring the generated manual reflects real-time sensor data without requiring model retraining. This approach decouples the static knowledge in the LLM from the dynamic data source, guaranteeing both accuracy and freshness while avoiding the latency and cost of continuous retraining.

Answer

Train the model in real-time as sensor data streams in.

Answer

Periodically retrain the model with the latest sensor data.

Answer

Have human technicians review and update the manuals manually.

Question 9

A marketing agency wants to generate images using Imagen on Vertex AI. They need to ensure the images are unique and avoid copyright issues. Which parameter adjustment is most relevant?

Accepted Answer

Use negative prompts. Negative prompts allow the model to exclude specific concepts, styles, or elements from generated images, directly reducing the risk of replicating copyrighted or trademarked content. By explicitly telling Imagen what not to include, the agency can steer outputs away from protected works without needing to modify training data or safety filters.

Answer

Increase training steps

Answer

Increase seed variability

Answer

Set safety threshold

Question 10

A retail company with a large FAQ database wants to build a generative AI customer service chatbot that can answer questions accurately with up-to-date information. Which business strategy should they prioritize?

Accepted Answer

Use retrieval-augmented generation (RAG) with vector search on the FAQ database.. Option A is correct because retrieval-augmented generation (RAG) with vector search allows the chatbot to dynamically retrieve the most relevant, up-to-date FAQ entries from a large database at inference time, grounding the generative model's responses in verified content without requiring retraining. This approach combines the flexibility of a pre-trained language model with the accuracy of real-time information retrieval, ensuring answers reflect the latest FAQ updates.

Answer

Train a new model from scratch using the FAQ data.

Answer

Fine-tune a foundational model on the entire FAQ dataset.

Answer

Use a general-purpose language model without any customization.

Question 11

A company wants to measure the business impact of a GenAI content generation tool. Which metric is most appropriate?

Accepted Answer

Reduction in content production time. Option A is correct because the primary business impact of a GenAI content generation tool is operational efficiency, measured by the reduction in content production time. This metric directly correlates to cost savings and faster time-to-market, which are key business outcomes. Unlike technical metrics, it reflects real-world value delivery.

Answer

Number of model parameters

Answer

Model accuracy on a test set

Answer

Training loss

Question 12

A retail company wants to integrate generative AI into its customer service chatbot to handle routine inquiries. They have a limited budget and want to launch quickly. Which strategy is most appropriate?

Accepted Answer

Use pre-trained models via Google Cloud's Generative AI Studio API. Option B is correct because using pre-trained models via Google Cloud's Generative AI Studio API allows the company to leverage existing, powerful models without the high cost and time investment of custom development or fine-tuning. This approach enables rapid deployment on a limited budget by simply integrating the API into their chatbot, handling routine inquiries effectively without requiring extensive machine learning expertise or infrastructure.

Answer

Partner with a generative AI vendor for a custom solution

Answer

Fine-tune an open-source model on their customer service logs

Answer

Build a custom LLM from scratch using the company's own data

Question 13

A team set a budget alert for their GenAI API usage at $10,000. They received the alert with current spend of $12,500. Which business action is most appropriate as a first step?

Accepted Answer

Review usage patterns and optimize prompt lengths and frequencies. Option C is correct because the first step in responding to a budget overrun should be to analyze usage patterns and optimize prompt lengths and frequencies. This approach identifies inefficiencies (e.g., unnecessarily verbose prompts, excessive retries) that directly reduce token consumption and cost without disrupting critical operations. It aligns with the principle of cost optimization before making architectural or policy changes.

Answer

Pause all non-critical use cases immediately

Answer

Switch to a cheaper model provider

Answer

Increase the budget by 50% to $15,000

Question 14

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.. Option C is correct because 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.

Answer

Free with advertising.

Answer

One-time license fee for the model.

Answer

Selling user data collected from interactions.

Question 15

A team built a GenAI chatbot that uses a vector database to retrieve context. Users report irrelevant responses. What is the most likely business strategy issue?

Accepted Answer

The embedding model is not aligned with the domain vocabulary. Option D is correct because irrelevant responses in a RAG (Retrieval-Augmented Generation) chatbot most often stem from the embedding model failing to capture domain-specific semantics. If the embedding model was trained on general text (e.g., Wikipedia) but the chatbot operates in a specialized field like legal or medical, the vector similarity search will retrieve context that is semantically distant from the user's query, leading to irrelevant answers. This is a business strategy issue because the team chose an embedding model that does not align with their domain vocabulary, undermining the entire retrieval pipeline.

Answer

The model is too small to generate accurate responses

Answer

The chatbot is too verbose

Answer

The system is overfitting to the training data

Question 16

An organization uses a fine-tuned model for medical diagnosis and must comply with HIPAA. Which measure is essential when deploying the model on Vertex AI?

Accepted Answer

Use a private Google Cloud Access and disable external internet access for the endpoint.. Option D is correct because HIPAA requires that patient data be protected from unauthorized access during transmission and deployment. Using a private Google Cloud Access endpoint with external internet access disabled ensures that the model endpoint is only reachable within the organization's VPC network, preventing data exposure over the public internet and meeting HIPAA's security rule for safeguarding electronic protected health information (ePHI).

Answer

Store all patient data in Cloud Storage with object versioning.

Answer

Enable encryption at rest for all resources.

Answer

Use a publicly accessible endpoint for faster response times.

Question 17

A prompt engineer wants to improve the model's adherence to a specific output format (e.g., always start with a greeting). Which technique should they try first?

Accepted Answer

Include a system instruction at the beginning of the prompt that specifies the desired format.. Option C is correct because system instructions are the most direct and efficient method to enforce output formatting in large language models. By placing a clear directive at the beginning of the prompt (e.g., 'Always start your response with a greeting'), the model's attention mechanism is guided to prioritize this rule during generation, without requiring retraining or hyperparameter changes.

Answer

Use a lower temperature to make the output more deterministic.

Answer

Fine-tune the model on many examples of the desired format.

Answer

Modify the model's tokenizer to encode the format rules.

Question 18

Refer to the exhibit. A user with this IAM role tries to deploy a model to a Vertex AI Endpoint but fails. What is the most likely reason?

Accepted Answer

The user needs the roles/aiplatform.deployer role. The user has an IAM role but lacks the specific permission `aiplatform.deployments.create` required to deploy a model to a Vertex AI Endpoint. The `roles/aiplatform.deployer` role includes this permission, while the user's existing role does not, causing the deployment to fail. Even if the user can use other Vertex AI services, deploying a model to an endpoint is a distinct action that requires this specific role.

Answer

The user is not authorized to use Vertex AI at all

Answer

The model artifact is not in the same region as the endpoint

Answer

The user needs the roles/aiplatform.admin role

Question 19

What is the purpose of grounding in Vertex AI?

Accepted Answer

To connect model outputs to verifiable sources. Grounding in Vertex AI connects model outputs to verifiable, external sources of information (such as Google Search, enterprise data sources, or third-party databases) to reduce hallucinations and improve factual accuracy. By referencing grounded sources, the model can provide citations and allow users to verify claims, which is critical for enterprise applications requiring trust and compliance.

Answer

To improve training speed

Answer

To reduce model size for faster inference

Answer

To enable multi-modal inputs

Question 20

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

Answer

Use prompt engineering to instruct the model to avoid biased language

Answer

Increase the diversity of input samples by random sampling

Generative AI Leader Practice Exam 1 — 20 Questions

Exhibit

Generative AI Leader Practice Exam 1 — 20 Questions

Exhibit