Generative AI Leader Timed Practice Test 2 — 10 Questions

Question 1

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.

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Number of model parameters

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Model accuracy on a test set

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Training loss

Question 2

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.

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Partner with a generative AI vendor for a custom solution

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

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

Question 3

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.

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Pause all non-critical use cases immediately

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Switch to a cheaper model provider

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Increase the budget by 50% to $15,000

Question 4

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.

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Free with advertising.

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One-time license fee for the model.

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Selling user data collected from interactions.

Question 5

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.

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The model is too small to generate accurate responses

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The chatbot is too verbose

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The system is overfitting to the training data

Question 6

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).

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Store all patient data in Cloud Storage with object versioning.

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Enable encryption at rest for all resources.

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Use a publicly accessible endpoint for faster response times.

Question 7

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.

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Use a lower temperature to make the output more deterministic.

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

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

Question 8

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.

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The user is not authorized to use Vertex AI at all

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

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The user needs the roles/aiplatform.admin role

Question 9

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.

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To improve training speed

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To reduce model size for faster inference

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To enable multi-modal inputs

Question 10

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.

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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