Generative AI Leader Fundamentals of Generative AI — Timed 10 Questions

Question 1

A startup is building a customer support chatbot using Vertex AI and wants to ground responses in their product documentation to reduce hallucinations. Which approach should they use?

Accepted Answer

Enable Vertex AI Grounding with a custom enterprise data store containing the documentation.. Vertex AI Grounding with a custom enterprise data store is the correct approach because it allows the chatbot to retrieve and cite specific chunks from the product documentation in real time, directly reducing hallucinations by constraining responses to verified content. This method uses the underlying grounding service to query a vector-based data store (powered by Vertex AI Search) and append source references to the model's output, ensuring factual accuracy without retraining.

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Use the Codey API for text generation.

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Use the base model without any grounding to maximize flexibility.

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Fine-tune the model on the documentation and deploy.

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.

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

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The model is overfitted; switch to a smaller model.

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

Question 3

A financial services company wants to use generative AI to generate personalized investment advice. They must ensure responses comply with regulatory requirements (e.g., no guarantees of returns). Which Vertex AI safety feature should they primarily use?

Accepted Answer

Safety filters with a custom blocklist that includes phrases like 'guaranteed return'.. Option C is correct because safety filters with a custom blocklist allow the company to define specific prohibited phrases (e.g., 'guaranteed return') that the model must avoid generating. This provides a deterministic, rule-based enforcement layer that directly addresses regulatory compliance by blocking disallowed content at inference time, without relying on the model's probabilistic behavior.

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Vertex AI Grounding with their compliance database.

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Prompt engineering with instructions to avoid guarantees.

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Reinforcement learning from human feedback (RLHF) on the model.

Question 4

A company is using Vertex AI to generate marketing copy. They notice that the output sometimes contains factual inaccuracies. Which parameter adjustment is most likely to improve factual accuracy?

Accepted Answer

Decrease the temperature parameter.. Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and less likely to generate creative but factually incorrect content. Lower temperature (e.g., 0.1) forces the model to choose higher-probability tokens, which aligns with more factual and consistent responses, especially in tasks like marketing copy where accuracy is critical.

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Increase the max_output_tokens parameter.

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Increase the top_p parameter.

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Add a post-processing step to verify facts using a database.

Question 5

A team is fine-tuning a large language model on custom data using Vertex AI. They find that the training loss decreases but validation loss increases. What is the best course of action?

Accepted Answer

Reduce the model size or add dropout regularization.. The increasing validation loss while training loss decreases is a classic sign of overfitting, where the model memorizes the training data but fails to generalize. Reducing model size or adding dropout regularization directly combats overfitting by limiting the model's capacity or introducing noise during training, which forces the model to learn more robust features. This is the best course of action because it addresses the root cause without further exacerbating the problem.

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Increase the number of training epochs.

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Increase the learning rate.

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Switch to a smaller batch size.

Question 6

A developer wants to generate product descriptions from a list of features using Vertex AI. Which model type is best suited for this task?

Accepted Answer

A text generation model (e.g., text-bison@001).. Option C is correct because text-bison@001 is a dedicated text generation model optimized for tasks like summarization, translation, and content creation from structured inputs. It can take a list of features as a prompt and generate coherent, descriptive product descriptions without needing conversational context or code-specific outputs.

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An embedding model (e.g., textembedding-gecko@001).

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A chat model (e.g., chat-bison@001).

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A code generation model (e.g., code-bison@001).

Question 7

A company is using Vertex AI to generate email responses. They want to ensure sensitive customer data (PII) is not included in the output. What is the most effective approach?

Accepted Answer

Configure safety filters to block PII categories.. Option D is correct because safety filters in Vertex AI are specifically designed to block categories of harmful content, including PII, at the model's output layer. This provides a deterministic, automated guardrail that prevents sensitive data from being generated, unlike prompt-based instructions which can be overridden by the model's training. Safety filters operate on the model's response before it is returned, ensuring PII is caught even if the model attempts to generate it.

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Use a system prompt instructing the model to avoid PII.

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Fine-tune the model on a dataset that excludes PII.

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Manually review each output before sending.

Question 8

Which TWO statements are true about generative AI models?

Accepted Answer

They are typically pre-trained on large datasets.. Option A is correct because generative AI models, such as GPT-4 or DALL-E, are typically pre-trained on vast, diverse datasets (e.g., terabytes of text or images) using unsupervised or self-supervised learning. This pre-training phase allows the model to learn statistical patterns, grammar, and world knowledge, which is then fine-tuned for specific tasks. Without this large-scale pre-training, the model would lack the foundational understanding needed to generate coherent and contextually relevant outputs.

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They are deterministic by design.

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They always produce the same output for the same input.

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They require no data for training.

Question 9

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.

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Maximize model throughput to handle high volumes.

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Use the cheapest model to reduce costs.

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.. Option A is correct because 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.