Generative AI Leader Fundamentals of Generative AI — Timed 20 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.

Answer

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.

Answer

Cost per million tokens.

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BLEU score against human-written moderation guidelines.

Question 11

A company wants to build a chatbot using Vertex AI that can answer customer questions based on their internal knowledge base. Which Google Cloud service should they use to store and retrieve the knowledge base efficiently?

Accepted Answer

Vertex AI Vector Search. Vertex AI Vector Search is the correct choice because it is purpose-built for semantic similarity search over embeddings, enabling the chatbot to retrieve relevant chunks from the knowledge base based on meaning rather than exact keyword matches. It integrates natively with Vertex AI and supports high-dimensional vector indexing, making it efficient for large-scale retrieval-augmented generation (RAG) workflows.

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Cloud Storage

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BigQuery

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Vertex AI Matching Engine

Question 12

A data scientist is fine-tuning a large language model using Vertex AI. The training job fails with an out-of-memory error. Which action should they take to resolve this issue?

Accepted Answer

Reduce the batch size. Reducing batch size lowers memory consumption per step, directly addressing OOM. Option A is wrong because increasing batch size worsens memory usage. Option B is wrong because switching to a larger model increases memory. Option D is wrong because TPUs also have memory limits; reducing batch size works on TPU as well.

Answer

Change the accelerator to TPU

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Use a larger model

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Increase the batch size

Question 13

A company is deploying a generative AI application that generates medical reports. They need to ensure the output is factual and minimizes hallucinations. Which approach is most effective?

Accepted Answer

Implement retrieval-augmented generation (RAG) with a curated knowledge base. Retrieval-Augmented Generation (RAG) is the most effective approach because it grounds the model's output in a curated, authoritative knowledge base of medical data. By retrieving relevant, verified documents at inference time, RAG directly reduces the model's reliance on its parametric memory, which is the primary source of hallucinations in generative AI. This is especially critical in high-stakes domains like medical reporting, where factual accuracy is paramount.

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Fine-tune the model with RLHF

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Set the temperature to 0.0

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Use prompt engineering to instruct the model to be accurate

Question 14

A developer is using Vertex AI PaLM API to generate code snippets. The responses sometimes contain security vulnerabilities. What is the best practice to mitigate this?

Accepted Answer

Implement input validation and output filtering with safety attributes. Option A is correct because input validation and output filtering with safety attributes directly address security vulnerabilities by sanitizing user inputs and filtering model outputs for harmful content. The Vertex AI PaLM API provides safety attribute scores (e.g., toxicity, harassment) that allow developers to programmatically block or flag responses that exceed defined thresholds, reducing the risk of generating insecure code snippets.

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Disable safety filters to allow more output

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Increase the max output tokens

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Set safety settings to block all categories

Question 15

A machine learning engineer is building a text-to-image model using Vertex AI. They want to reduce inference latency. Which strategy is most effective?

Accepted Answer

Use a smaller model variant. Option B is correct because using a smaller model variant directly reduces the number of parameters and computational operations required per inference pass, which lowers latency. In text-to-image models like Imagen or Stable Diffusion, the model size is the primary driver of forward-pass time, so a smaller variant (e.g., fewer layers or reduced latent dimensions) yields faster generation.

Answer

Use a larger image resolution

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Enable batch processing

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Increase the number of inference steps

Question 16

A company is using Vertex AI to generate personalized marketing emails. The model sometimes produces biased content. What is the most effective way to detect and mitigate bias?

Accepted Answer

Use Vertex AI Explainable AI to analyze predictions and detect bias in training data. Vertex AI Explainable AI provides feature attributions and model explanations that help identify which input features (e.g., demographic attributes, phrasing patterns) contribute most to biased outputs. By analyzing these attributions against training data, you can pinpoint and mitigate bias at the source, rather than relying on post-hoc manual review or model swapping.

Answer

Add more diverse training data

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Manually review all generated emails before sending

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Switch to a different generative model

Question 17

Which TWO options are best practices for deploying generative AI models on Vertex AI? (Choose two.)

Accepted Answer

Enable automatic scaling to handle variable traffic. Option B is correct because Vertex AI's automatic scaling dynamically adjusts the number of serving instances based on incoming traffic, ensuring low latency during spikes and cost efficiency during lulls. This is a best practice for production workloads where traffic patterns are unpredictable, as it eliminates the need for manual capacity planning.

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Disable logging to reduce cost

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Manually scale instances based on expected load

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Serve the model directly without optimization

Question 18

Which THREE factors should be considered when choosing between fine-tuning and prompt engineering for a generative AI task? (Choose three.)

Accepted Answer

Availability of labeled training data. Option A is correct because fine-tuning requires a labeled dataset specific to the target task to adjust model weights via supervised learning, whereas prompt engineering relies on the model's existing knowledge without additional training data. Without sufficient labeled data, prompt engineering is often the only viable approach, as fine-tuning would risk overfitting or poor generalization.

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Cost of API calls per request

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Size of the base model

Question 19

A data scientist sees the above error when trying to deploy a model to an endpoint. What is the most likely cause?

Accepted Answer

The model import into Vertex AI Model Registry is still in progress. The error indicates that the model is not yet fully imported into the Vertex AI Model Registry. Deploying a model to an endpoint requires the model resource to be in an 'ACTIVE' state; if the import is still in progress, the deployment request will fail. This is a common timing issue when a model is uploaded but not yet registered.

Answer

The IAM permissions are insufficient

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The endpoint does not exist

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The model is already deployed to another endpoint

Question 20

A developer receives the above JSON response from a Vertex AI PaLM API call for a medical advice application. What should the developer be most concerned about?

Accepted Answer

The output falls under the 'health' category, which may require compliance with regulations. Option C is correct because the JSON response includes a 'category' field with the value 'health', which triggers stringent regulatory compliance requirements such as HIPAA in the US or GDPR in Europe. For a medical advice application, the developer must ensure data handling, model transparency, and output validation meet these legal standards, as failure could result in severe penalties. The PaLM API's safety attributes and category labels are designed to flag such sensitive domains, making compliance the primary concern over other technical issues.

Answer

The safety score is very low (0.01)

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The deployed model ID is not recognized

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The prediction content is incorrect