Generative AI Leader Practice Test 16 — 15 Questions

Question 1

A company wants to use GenAI to automate customer support. They have a large knowledge base. Which approach maximizes ROI in the first 6 months?

Accepted Answer

Use a pre-built conversational AI platform with Retrieval-Augmented Generation (RAG). Maximizes ROI in the first 6 months because it leverages a pre-built conversational AI platform integrated with Retrieval-Augmented Generation (RAG). RAG allows the model to dynamically retrieve relevant information from the existing knowledge base at inference time, providing accurate, context-aware responses without the need for costly retraining or custom model development. This approach balances rapid deployment, low upfront investment, and high accuracy, making it the most cost-effective solution for automating customer support quickly.

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Deploy a general-purpose chatbot without customization

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Build a custom LLM from scratch using their data

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Fine-tune a foundation model on historical support tickets

Question 2

A research lab is planning to train a massive protein folding model similar to AlphaFold. They want to use Google Cloud infrastructure and tools. Which THREE components are most relevant?

Accepted Answer

Cloud TPU pods. Cloud TPU pods are specifically designed for large-scale machine learning workloads like protein folding, offering high-throughput matrix operations essential for training models similar to AlphaFold. They provide the massive parallel compute power needed for training deep neural networks on protein structure prediction tasks, which require processing large datasets and complex 3D spatial relationships.

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Cloud Vision API

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Google AI Studio

Question 3

Which of the following is a key principle in Google's AI Principles that directly addresses the need to avoid creating or reinforcing unfair bias?

Accepted Answer

Avoid creating or reinforcing unfair bias. Google's AI Principles explicitly state 'Avoid creating or reinforcing unfair bias' as a standalone principle. This principle directly mandates that AI systems must be designed and tested to mitigate biases in training data, model outputs, and deployment contexts, ensuring fairness across demographic groups. It is the most direct response to the question's focus on avoiding unfair bias.

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Uphold high standards of scientific excellence

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Be socially beneficial

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Be accountable to people

Question 4

A product manager wants to communicate the limitations of a new generative AI feature to stakeholders. According to Google's People + AI Guidebook, what is the BEST approach?

Accepted Answer

Provide clear examples of the AI's capabilities and failure modes. The People + AI Guidebook emphasizes setting appropriate expectations by clearly communicating what the AI can and cannot do.

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Highlight only the successful use cases to build excitement

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Promise that future versions will overcome all limitations

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Share a technical paper detailing the model architecture

Question 5

A company is using Vertex AI Model Garden to deploy a foundation model for document summarization. They notice that the model sometimes generates summaries that include factual errors. They want to reduce hallucinations without sacrificing latency. Which approach should they try first?

Accepted Answer

Enable Vertex AI Grounding with a curated database of documents. Vertex AI Grounding connects the model to a curated database of documents, allowing it to retrieve and cite factual information in real-time. This directly reduces hallucinations by grounding responses in verified sources without adding significant latency, as the retrieval step is optimized for speed. Other approaches either increase latency (fine-tuning), reduce output quality (temperature increase), or do not address factual accuracy (safety filters).

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Increase the temperature parameter to make the model more confident

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Add more safety filters to block uncertain responses

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Fine-tune the model on a high-quality dataset of correct summaries

Question 6

A financial institution wants to use generative AI to generate personalized investment advice. They face strict regulatory requirements on explainability and bias. Which approach should they take?

Accepted Answer

Use a RAG system with curated proprietary data. A Retrieval-Augmented Generation (RAG) system allows the financial institution to ground generative AI outputs in curated, proprietary data sources (e.g., regulatory guidelines, client risk profiles, historical performance). This approach enhances explainability by enabling traceable citations back to specific documents, and reduces bias by controlling the data fed to the model, which is critical for meeting strict regulatory requirements like GDPR or SEC rules on algorithmic fairness.

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Use a foundation model with prompt engineering

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Use a custom model trained from scratch

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Use a closed-source model with vendor lock-in

Question 7

A company wants to use generative AI for creative content generation (e.g., marketing copy). They need to ensure the content is original and does not plagiarize existing materials. Which combination of strategies is most effective?

Accepted Answer

Use a retrieval-augmented generation system that explicitly avoids copying.. Retrieval-augmented generation (RAG) systems explicitly retrieve relevant, non-copyrighted or licensed content from a curated knowledge base and generate outputs grounded in that retrieved data, which inherently reduces the risk of verbatim copying. Unlike simple plagiarism checkers or temperature adjustments, RAG combines retrieval with generation to ensure originality by design, making it the most effective strategy for avoiding plagiarism in creative content generation.

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Use a model with a high temperature setting and post-process with plagiarism checker.

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Fine-tune the model on a dataset of already-created content to learn style.

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Limit the model to generate only short snippets.

Question 8

A company is moving a GenAI proof-of-concept to production. The pilot showed promising results, but the production environment must handle higher traffic, ensure low latency, and manage costs. Which THREE actions should they take?

Accepted Answer

Implement caching for common or repetitive queries. A/B testing helps compare versions. Choosing a smaller model balances cost and latency. Caching reduces repeated work. Fine-tuning is expensive and not necessary. A single endpoint without testing is risky.

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Deploy a single model endpoint without A/B testing to simplify architecture

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Fine-tune the model on the entire dataset for better performance

Question 9

Which TWO are key business considerations when adopting generative AI solutions?

Accepted Answer

Data privacy and compliance requirements. Data privacy and compliance requirements (Option C) are a key business consideration because generative AI models often process sensitive or proprietary data, and regulations like GDPR, HIPAA, or CCPA mandate strict controls on data handling, storage, and model training. Failure to address these can result in legal penalties, reputational damage, and loss of customer trust, making it a top priority for enterprise adoption.

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Training duration on public datasets

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Number of model parameters

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Model accuracy on benchmarks

Question 10

A team is evaluating generative AI models for a content moderation system. Which THREE metrics are most important to assess?

Accepted Answer

Percentage of outputs flagged by safety filters.. Safety filters are a primary mechanism for detecting and blocking harmful or policy-violating content in generative AI outputs. In a content moderation system, the percentage of outputs flagged by these filters directly measures the model's tendency to produce unsafe content, which is critical for maintaining platform safety and compliance.

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Cost per million tokens.

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BLEU score against human-written moderation guidelines.

Question 11

A company fine-tunes a text model on internal HR policies. After deployment, the model sometimes outputs sensitive employee information. What is the most likely cause?

Accepted Answer

The fine-tuning dataset contained personally identifiable information that was not removed.. The most likely cause is that the fine-tuning dataset contained personally identifiable information (PII) that was not properly scrubbed. During fine-tuning, the model learns patterns and memorizes specific sequences from the training data. If the dataset includes sensitive employee records, the model can reproduce that information verbatim when prompted, leading to data leakage. This is a well-known risk in fine-tuning, as models can overfit to rare or unique examples in the training set.

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The model was not trained with reinforcement learning from human feedback (RLHF).

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The model has insufficient parameters to generalize properly.

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The prompt engineering was too verbose and included misleading instructions.

Question 12

A financial institution wants to deploy a generative AI solution for contract analysis. They need to ensure compliance with regulations. Which approach is best?

Accepted Answer

Fine-tune a model on a curated dataset of past contracts and implement human-in-the-loop review. Best because fine-tuning on a curated dataset of past contracts ensures the model learns domain-specific language and compliance patterns, while human-in-the-loop review provides a critical safety net for regulatory adherence. This combination directly addresses the need for accuracy and accountability in contract analysis, where errors can have legal consequences.

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Deploy a large open-source model fine-tuned on public legal documents

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Use a general-purpose pre-trained model with no modifications to minimize risk

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Implement retrieval-augmented generation (RAG) with the company's legal document database

Question 13

A data scientist is documenting a new dataset for a generative AI project. According to the Responsible AI toolkit, which TWO elements should they include in a Datasheet for Datasets?

Accepted Answer

The demographic composition of the data subjects. The Responsible AI toolkit's Datasheet for Datasets framework requires documenting the demographic composition of data subjects to identify potential biases and ensure fairness. This transparency allows downstream users to assess whether the dataset may lead to discriminatory outcomes in generative AI models, aligning with responsible AI principles.

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The model architecture used to collect the data

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The hyperparameters of the model that will process the data

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The cost of acquiring the dataset

Question 14

A large insurance company is using generative AI to automate claims processing. They have deployed a custom fine-tuned model on Vertex AI that reads claim documents and extracts key information. Recently, they noticed that the model’s performance degrades over time for certain claim types, leading to incorrect payouts. The team needs to detect and address model drift with minimal manual intervention. They have a data pipeline that captures incoming claims and user feedback on predictions. Which approach should they take?

Accepted Answer

Set up continuous evaluation with automated retraining pipelines based on performance metrics. It establishes a closed-loop MLOps pipeline where continuous evaluation of performance metrics (e.g., precision, recall, or F1-score on streaming data) triggers automated retraining when drift is detected. This minimizes manual intervention while ensuring the model adapts to distribution shifts in claim types, which is critical for maintaining accurate payouts in production.

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Implement a human review process for all claims the model processes

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Switch to a simpler rule-based system to avoid drift

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Manually retrain the model monthly using a snapshot of recent claims

Question 15

A team is building a medical diagnosis assistant using a foundation model. To comply with regulations, they need to ensure the model does not make up facts. What is the best approach?

Accepted Answer

Use grounding with Vertex AI Search. Grounding with Vertex AI Search is the best approach because it connects the foundation model's outputs to a verifiable, curated knowledge base, ensuring factual accuracy and compliance with regulations that prohibit hallucination. By retrieving information from a trusted source (e.g., medical databases) in real time, the model can cite evidence and avoid generating unverified claims.

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Use a small model to hallucinate less

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Reduce temperature to 0

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Fine-tune on medical journals