Generative AI Leader Practice Test 14 — 15 Questions

Question 1

An enterprise deploys a generative AI chatbot that must comply with GDPR right to deletion. Users can request deletion of their personal data. The chatbot uses a RAG pipeline with a vector database. What is the MOST effective way to handle deletion requests?

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

Accepted Answer

Delete the user's documents from the vector index and original storage, then rebuild the index. GDPR Article 17 (right to erasure) requires that personal data be deleted from all systems where it is stored, including derived stores like vector indexes. In a RAG pipeline, the user's documents exist both in original storage and as embeddings in the vector database, so the compliant approach is to delete from both and rebuild or re-index the affected vectors. Simply filtering responses or anonymizing records leaves the personal data recoverable and does not satisfy erasure.

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Update the user's records in the vector index with anonymized placeholders

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Add a filter to the chat application to block the user's name from appearing in responses

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Retrain the LLM from scratch without the user's data

Question 2

A data scientist is fine-tuning a large language model for a legal document summarization task. The dataset contains only 500 examples, and the model must not forget its general language capabilities. Which fine-tuning method is most suitable?

Accepted Answer

Adapter-based fine-tuning using LoRA. With only 500 examples and a requirement to preserve general language capabilities, parameter-efficient fine-tuning via LoRA adapters is ideal. LoRA freezes the base model weights and trains small low-rank matrices injected into attention layers, so the model retains its pretrained knowledge while learning the legal summarization task. It also dramatically reduces GPU memory and training time versus full fine-tuning.

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Retraining the model from scratch on the legal dataset

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In-context learning with a few examples in the prompt

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Full fine-tuning of all model parameters

Question 3

A fintech company is deploying a generative AI system that offers investment advice. To comply with regulations and Google's AI Principles, they need to ensure appropriate human oversight and transparency. Which two actions should they take? (Choose two.)

Accepted Answer

Require a human advisor to review and approve all AI-generated recommendations. Option C is correct because requiring a human advisor to review and approve all AI-generated recommendations directly implements the human oversight (human-in-the-loop) that Google's AI Principles and financial regulations demand for high-stakes advice, ensuring accountability before any recommendation reaches a client. Option D is correct because a Model Card is the standard transparency artifact that documents a model's intended use, limitations, performance, and ethical considerations, giving regulators and users the visibility needed to understand and trust the system. Option A is not required here: synthetic data can help with privacy but does not by itself provide human oversight or transparency, and it may even reduce real-world representativeness. Option B is wrong because fully automated trade execution removes the human oversight the scenario explicitly requires and could violate fiduciary and regulatory obligations. Option E is wrong because disabling logging destroys the audit trail needed for transparency, accountability, and regulatory review, and it does not enhance meaningful privacy in a compliant manner.

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Use only synthetic data for training to avoid privacy issues

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Allow the AI to execute trades automatically without human intervention

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Disable logging of all interactions to maximize user privacy

Question 4

A large enterprise is planning to roll out a GenAI assistant for contract negotiation. The legal team wants to ensure that the assistant's outputs are consistent and follow a predefined format for downstream processing. What is the BEST prompt engineering technique?

Accepted Answer

Define a structured output schema (e.g., JSON) in the prompt and request the model to output in that format. Defining a structured output schema (e.g., JSON) in the prompt explicitly constrains the model's output format, ensuring consistency and machine-readability for downstream contract negotiation processing. This technique leverages the model's instruction-following capability to produce parseable, schema-compliant responses, which is critical for automated legal workflows where variable formatting would break integration.

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Use few-shot examples with variable formatting

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Add a system instruction to 'be consistent'

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Use chain-of-thought prompting to have the model reason step-by-step

Question 5

A company wants to build a chatbot that answers questions based on internal documents. Which approach is most appropriate?

Accepted Answer

Use a prompt with the documents in the context. Retrieval-Augmented Generation (RAG) allows the chatbot to dynamically include relevant internal documents in the prompt context without modifying the underlying model. This approach leverages the pre-trained model's language understanding while grounding answers in specific, up-to-date internal data, avoiding the cost and latency of fine-tuning or retraining.

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Use a pre-trained model without any customizations

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Train a custom model from scratch

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Fine-tune a model on the documents

Question 6

A startup wants to quickly integrate a generative AI chatbot into their customer support platform. They need a solution that can answer questions based on their internal knowledge base with minimal setup. Which Google Cloud service should they use?

Accepted Answer

Use Vertex AI Agent Builder to create a conversational agent grounded in their data. Vertex AI Agent Builder (now part of Vertex AI Agent Platform) provides a low-code, out-of-the-box solution for building conversational agents that are grounded in enterprise data. It automatically handles retrieval-augmented generation (RAG) by indexing the startup's internal knowledge base into a vector store and orchestrating the LLM to answer questions using only that data, requiring minimal setup compared to manual integration or fine-tuning.

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Use Model Garden to deploy a pre-built Q&A model

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Call the Gemini API directly and implement grounding logic manually

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Use Cloud AI Notebooks to fine-tune a model on their knowledge base

Question 7

A company wants to offer a generative AI feature where the output must follow a very specific tone and style as per the brand guidelines. Which strategy is most reliable?

Accepted Answer

Fine-tune a model on a dataset of branded content.. Fine-tuning a model on a dataset of branded content is the most reliable strategy because it adjusts the model's internal weights to consistently produce outputs that match the specific tone and style of the brand. Unlike prompt-based methods, fine-tuning embeds the stylistic constraints directly into the model's parameters, ensuring adherence even for complex or nuanced brand guidelines.

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Post-process the output with a style transfer algorithm.

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Use a general-purpose model with a system prompt describing the style.

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Use a different model for each content type.

Question 8

Which parameter controls the randomness of a language model's output?

Accepted Answer

Temperature. Temperature is the parameter that directly controls the randomness of a language model's output by scaling the logits before applying the softmax function. A higher temperature (e.g., >1.0) makes the probability distribution more uniform, increasing randomness, while a lower temperature (e.g., <1.0) sharpens the distribution, making the model more deterministic and focused on high-probability tokens.

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Embedding dimension

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Context window

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Top-k

Question 9

A marketing team wants to generate product descriptions for 1000 new items. They need consistent brand voice and the ability to review and edit outputs before publishing. Which approach is most suitable?

Accepted Answer

Use the Gemini API with a few-shot prompt that includes examples of the desired brand voice. Using the Gemini API with a few-shot prompt that includes examples of the desired brand voice is the most suitable approach because few-shot prompting guides the model to produce consistent tone and style without the cost and time of fine-tuning. It also allows human review and editing before publishing, matching the requirement for oversight.

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Use the Gemini API with grounding in Google Search to ensure factual accuracy

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Deploy a customer service chatbot in Vertex AI Agent Builder to generate descriptions

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Build a custom fine-tuned model on past product descriptions

Question 10

A company wants to ensure that its generative AI application complies with the GDPR right to erasure (right to be forgotten) for user data used in model fine-tuning. What is the best approach?

Accepted Answer

Maintain a mapping of user identities to training data, and upon request, remove the specific data points and retrain the model. Only option B fully addresses GDPR compliance by identifying and removing specific user data from the training set, then retraining. The other options do not effectively erase the user's influence from the model.

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Store data with expiration dates and automatically delete after a set period

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Use a broad data deletion request on all training data

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Implement differential privacy during training to prevent memorization

Question 11

A company is building a generative AI application that will generate marketing copy. They want to ensure that the content does not contain hate speech, violence, sexual content, or dangerous instructions. Which Google Cloud feature should they enable?

Accepted Answer

Configure safety filters and custom content controls in Vertex AI. Vertex AI provides safety filters and custom content controls that can be configured to block hate speech, violence, sexual content, and dangerous instructions. These filters are applied to both prompts and responses, allowing developers to enforce content policies. This is the most direct and automated way to ensure generated content meets safety requirements.

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Use the People + AI Guidebook to design prompts

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Enable Model Card generation in Vertex AI

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Implement a human review loop for all outputs

Question 12

Which of the following best describes the transformer architecture's key innovation that enabled modern large language models?

Accepted Answer

Self-attention mechanism that captures dependencies between all words in parallel. The transformer architecture's key innovation is the self-attention mechanism, which allows the model to compute attention scores between every pair of tokens in the input sequence simultaneously, rather than processing tokens sequentially. This parallelization enables the model to capture long-range dependencies efficiently and scale to massive datasets, which is the foundation for modern large language models like GPT and BERT.

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Recurrent connections that process sequences one element at a time

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Memory-augmented neural networks

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Convolutional layers that extract local features

Question 13

A financial analyst wants to run a regression model inside BigQuery using SQL, without moving data to a separate ML environment. Which Google Cloud service allows this directly?

Accepted Answer

BigQuery ML. BigQuery ML enables creating and running ML models using standard SQL queries directly in BigQuery. Vertex AI and AI Platform require data to be moved, and AutoML is a higher-level service but still not SQL-based.

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AI Platform

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AutoML Tables

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Vertex AI

Question 14

You are a Generative AI architect at a large financial services firm. The firm has deployed a custom large language model (LLM) fine-tuned on proprietary financial reports to assist analysts in generating quarterly earnings summaries. The model is hosted on Vertex AI using a dedicated endpoint with autoscaling enabled. Recently, the model's output has exhibited two issues: (1) occasional factual inaccuracies about specific financial figures, and (2) a tendency to produce overly verbose and repetitive text in the summaries, sometimes exceeding the desired length of 200 words. The team has already tried adjusting the temperature parameter from 0.7 to 0.2 and increased the top-k sampling from 40 to 50, but the problems persist. The model's training data includes over 10,000 financial reports, and the fine-tuning process used low-rank adaptation (LoRA) with rank 16. The production environment uses a batch size of 1 for inference. You need to recommend a course of action that most directly addresses both the factual accuracy and verbosity issues without requiring a full retraining of the model. Which approach should you take?

Accepted Answer

Implement a retrieval-augmented generation (RAG) pipeline that queries a vector database of verified financial data, and apply constrained decoding with a maximum token limit and a repetition penalty.. It directly addresses both issues without retraining. A RAG pipeline grounds the model's outputs in verified financial data, eliminating factual inaccuracies. Constrained decoding with a maximum token limit and repetition penalty directly curbs verbosity and repetition, which temperature and top-k adjustments failed to fix.

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Increase the LoRA rank to 32 and fine-tune the model for additional epochs on a curated subset of reports that focus on concise and accurate summaries.

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Switch to a larger pre-trained model (e.g., PaLM 2 or GPT-4) and use the same fine-tuning data with higher rank LoRA to improve capability, then rely on the larger model's inherent accuracy.

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Experiment with higher temperature (e.g., 0.9) and lower top-k (e.g., 20) to encourage more diverse and concise outputs, and add a post-processing step to truncate summaries to 200 words.

Question 15

A company is using a fine-tuned generative model to create marketing copy. They want to ensure that when the model references statistics, it provides citations to original sources. Which technique should they use?

Accepted Answer

Grounding by referencing source documents in the output. Grounding by referencing source documents in the output directly ties the generated statistics to verifiable original sources, ensuring citation accuracy. This technique retrieves and cites specific passages from trusted documents, which is essential for responsible AI in marketing copy where factual claims must be traceable.

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Model tuning with citation data

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Chain-of-thought prompting

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Confidence indicators