Generative AI Leader Practice Test 23 — 15 Questions

Question 1

A global retailer uses a generative AI model to personalize product recommendations. They need to ensure that customer prompts and responses are not logged for model improvement to meet GDPR data minimization principles. Which configuration should they apply?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Disable prompt/response logging in Vertex AI endpoint settings. To comply with GDPR data minimization, the retailer should disable prompt/response logging. Vertex AI offers settings to control whether prompts and responses are stored for model improvement.

Answer

Use a third-party logging service with data deletion policies

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Enable data logging for six months and then automatically delete

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Anonymize all customer data before logging

Question 2

A health-tech startup is fine-tuning a generative AI model on electronic health records (EHR) to assist in clinical decision support. They need to ensure responsible AI practices. Which THREE measures should they implement? (Select three.)

Accepted Answer

Evaluate model outputs for bias across different demographic groups. Responsible AI in healthcare requires human oversight for high-stakes decisions, evaluating the model for bias (especially across demographic groups), and ensuring transparency about the model's limitations. Training data representativeness is also critical.

Answer

Automate all decisions to reduce human error

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Train the model exclusively on data from a single hospital to ensure consistency

Question 3

A company is deploying a generative AI chatbot for customer support. They want to ensure that the chatbot does not generate harmful content and that they can customize the safety thresholds. Which TWO features in Vertex AI should they use? (Select 2)

Accepted Answer

Custom safety settings (adjustable thresholds). Safety filters block harmful categories; custom safety settings allow adjustment of thresholds. Model Cards and the People + AI Guidebook are not operational safety controls.

Answer

AutoML Tables

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Model Cards

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People + AI Guidebook

Question 4

A hospital wants to summarize patient-doctor conversations into structured clinical notes using GenAI. They need high accuracy and must avoid hallucinated medical information. Which combination of techniques is BEST?

Accepted Answer

Fine-tune a medical-specific model on de-identified transcripts and use a structured output format in the prompt. Fine-tuning with structured output (e.g., JSON schema) and a strict prompt ensures the model produces accurate, formatted notes and reduces hallucinations.

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Use RAG with medical textbooks and a low temperature setting

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Use a large model with zero-shot prompting and post-process the output with rule-based checks

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Use a Gemini model with a high temperature setting to encourage creativity

Question 5

A social media company is using a generative AI model to automatically moderate user-uploaded images for harmful content. They need to comply with the EU AI Act's requirements for high-risk AI systems. Which combination of actions is MOST appropriate?

Accepted Answer

Use Google's safety filters, allow users to appeal automated decisions, and document the model's capabilities and limitations. The EU AI Act mandates risk management, transparency, and human oversight for high-risk systems. Google's AI Principles align with these requirements. The correct answer involves using safety filters for content, maintaining human review for appeals, and documenting the system's purpose and limitations.

Answer

Deploy the model without any filters, but log all decisions for audit

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Only rely on human moderators and disable AI moderation

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Use a third-party model that has been certified by a notified body

Question 6

A project manager wants to reduce the cost of using Gemini API for batch processing of customer feedback. The team is on a tight budget. Which cost management strategy is MOST effective?

Accepted Answer

Use batch requests to group multiple prompts. Batch requests reduce per-token cost by grouping multiple prompts into one API call. Caching helps but is less impactful for varied feedback.

Answer

Increase the temperature to max to reduce output length

Answer

Disable logging to reduce storage costs

Answer

Switch to the largest model available for better accuracy

Question 7

A company uses Gemini 1.5 Pro to analyze customer call transcripts and generate summaries. They notice that the summaries occasionally include fabricated details that were not in the transcript. Which technique is specifically designed to reduce such hallucinations?

Accepted Answer

Ground the model responses by implementing Retrieval-Augmented Generation (RAG) with the transcript as a source. Grounding with RAG retrieves factual data from trusted sources to condition the model's response, directly reducing hallucinations. Prompt engineering can help but does not guarantee factual accuracy; fine-tuning may reduce but not eliminate; temperature reduction makes output more deterministic but does not address factuality from external data.

Answer

Decrease the temperature to 0.0

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Use a system prompt that instructs the model not to make up information

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Fine-tune the model on a larger dataset of call transcripts

Question 8

A data science team wants to run a large-scale transformer training job with custom model architectures. They need the highest compute density for a multi-node job and want to minimize inter-node communication latency. Which Google Cloud infrastructure is BEST suited for this workload?

Accepted Answer

TPU v4 Pod slices with high-speed inter-chip interconnect. TPU v4 Pod slices provide the highest compute density for multi-node transformer training by using a custom 3D torus interconnect with 800 Gbps per chip bandwidth, which minimizes inter-node communication latency far below what standard networking can achieve. This architecture is specifically designed for large-scale model parallelism, making it ideal for custom transformer architectures that require frequent all-reduce and collective communication operations.

Answer

A single TPU v5e VM with multiple accelerators

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A cluster of A100 GPU VMs connected via standard networking

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Cloud Run jobs with GPU acceleration

Question 9

A company is planning an iterative rollout of a GenAI assistant for sales teams. They want to maximize adoption and minimize disruption. Which TWO change management practices should they prioritize? (Choose two.)

Accepted Answer

Provide hands-on training sessions and documentation. Hands-on training and documentation directly address the learning curve and anxiety associated with adopting a GenAI assistant, ensuring users understand how to interact with the model effectively (e.g., prompt engineering basics, interpreting outputs) and reducing friction. This practice aligns with the iterative rollout strategy by providing staged support, which maximizes adoption through competence and confidence rather than coercion.

Answer

Disable the legacy system immediately after GenAI launch

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Roll out to all employees simultaneously to create urgency

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Set a mandatory usage target for each sales rep

Question 10

A company is deploying a generative AI application that must comply with GDPR's right to explanation. The application must be able to justify its decisions. Which model or approach provides the MOST inherent interpretability?

Accepted Answer

Use a smaller, simpler model that is inherently more interpretable, such as a logistic regression or decision tree. Smaller, simpler models are inherently more interpretable. Large black-box models (Gemini, PaLM) are difficult to explain. RAG improves factual grounding but not interpretability of the model's reasoning. Prompting does not make the model's internal logic transparent.

Answer

Use Gemini 1.5 Pro with a system prompt asking for explanations

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Use Retrieval-Augmented Generation (RAG) to ground responses in source documents

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Use PaLM 2 with prompt engineering to provide step-by-step reasoning

Question 11

What is the fundamental difference between a foundation model and a fine-tuned model?

Accepted Answer

A foundation model is pre-trained on a large, diverse corpus; a fine-tuned model is adapted from a foundation model on a specific domain or task. A foundation model is pre-trained on broad data for general tasks, while a fine-tuned model is further trained on a specific dataset to specialize for a particular use case.

Answer

Foundation models only generate text; fine-tuned models can generate images

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Foundation models are open-source; fine-tuned models are proprietary

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Foundation models require no inference infrastructure; fine-tuned models do

Question 12

A data scientist wants to share a Jupyter notebook with interactive visualizations for a Kaggle competition. Which tool should they use to easily publish and collaborate?

Accepted Answer

Kaggle Notebooks. B is correct because Kaggle Notebooks are natively integrated with the Kaggle competition platform, allowing data scientists to publish interactive visualizations directly within the competition environment and collaborate with other participants. Unlike generic tools, Kaggle Notebooks provide seamless access to competition datasets, GPU/TPU resources, and version control tailored for Kaggle workflows.

Answer

Google Colab

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Vertex AI Workbench

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Cloud Shell Editor

Question 13

A company wants to implement a change management strategy for GenAI adoption. Which TWO actions are MOST effective in driving adoption among employees?

Accepted Answer

Appoint AI champions in each department to advocate and assist. Training programs build competence; AI champions provide peer support. Mandatory adoption and tool removal are counterproductive.

Answer

Remove existing tools to force adoption

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Limit access to GenAI tools to senior leadership only

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Make the use of GenAI tools mandatory for all tasks

Question 14

An enterprise wants to use generative AI to help employees search through internal documents, including text, scanned PDFs, and images. They need to index the content and enable semantic search. Which TWO Google Cloud services should they use? (Choose two.)

Accepted Answer

Vertex AI Vector Search. Vertex AI Embeddings API generates vector embeddings from text and images. Vertex AI Vector Search allows semantic search over those embeddings. Document AI is for document processing but not for embeddings or search. Gemini API is for generation, not indexing. Cloud Storage is for storage, not search.

Answer

Document AI

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Cloud Storage with Object Versioning

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Gemini API for content generation

Question 15

A healthcare startup needs to generate synthetic patient records for research. They require accurate output that adheres to medical syntax and semantics, and they must be able to explain why the model produces certain outputs for regulatory compliance. Which combination of techniques should they use?

Accepted Answer

Supervised fine-tuning on a large medical corpus, followed by RLHF. Supervised fine-tuning on medical data adapts the model to the domain, while RLHF aligns outputs with human preferences and can improve interpretability. LoRA is efficient but doesn't directly help with explainability. RAG is for knowledge retrieval, not explainability. In-context learning may not be reliable enough for regulatory compliance.

Answer

Use RAG with a vector store of medical literature

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Use Gemini in a zero-shot prompt with a strict system instruction

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Apply LoRA adapter fine-tuning with a small medical dataset