Generative AI Leader Practice Test 9 — 10 Questions

Question 1

A researcher wants to adapt a large language model for a specialized medical terminology domain without retraining the entire model. Which fine-tuning method is MOST parameter-efficient?

Accepted Answer

Adapter-based fine-tuning using LoRA. LoRA (Low-Rank Adaptation) is the most parameter-efficient fine-tuning method because it injects trainable low-rank matrices into the transformer layers, updating only a tiny fraction (often <1%) of the model's parameters while keeping the original weights frozen. This allows the model to adapt to specialized medical terminology without the memory and compute cost of full fine-tuning, making it ideal for domain adaptation with limited resources.

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In-context learning with 50 examples

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RLHF (Reinforcement Learning from Human Feedback)

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Full supervised fine-tuning of all model weights

Question 2

A healthcare provider plans to implement gen AI for clinical note summarization. They have limited AI expertise. Which Google Cloud approach best aligns with their business strategy?

Accepted Answer

Use Vertex AI Agent Builder with pre-built templates. Vertex AI Agent Builder provides pre-built templates and a low-code interface specifically designed for organizations with limited AI expertise. It enables rapid deployment of generative AI solutions like clinical note summarization without requiring deep data science skills, directly aligning with the healthcare provider's business strategy of minimizing technical overhead while leveraging AI.

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Hire a team of data scientists

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Deploy an open-source model on Compute Engine

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Build a custom model from scratch

Question 3

A company deploys a fine-tuned text generation model on Vertex AI Endpoints. They want to monitor for data drift and performance degradation over time. Which GCP service should they integrate?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the correct choice because it is specifically designed to detect data drift (changes in input data distribution) and feature attribution drift in deployed models, including fine-tuned text generation models on Vertex AI Endpoints. It provides automated alerts when model performance degrades due to shifts in production data, enabling proactive retraining or intervention.

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Cloud Monitoring

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Cloud Logging

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Vertex AI Experiments

Question 4

A project manager wants to automatically generate weekly status reports from meeting notes and project data. The team uses Google Workspace. Which built-in capability is the QUICKEST to implement?

Accepted Answer

Use Gemini for Workspace (Duet AI) in Google Docs and Google Meet to generate summaries and reports. Gemini for Workspace (Duet AI) can generate summaries directly in Google Docs and Meet, leveraging existing data with no custom development. Custom API integration or Model Garden would require more effort. Apps Script is for custom automation, not built-in.

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Select a model from Model Garden and deploy it as a private endpoint for report generation

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Use Vertex AI Studio to design a custom prompt and call the Gemini API from a custom app

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Write a Google Apps Script to call the Gemini API and format the report

Question 5

A marketing team wants to use generative AI to create ad copy that matches their brand voice. They have several examples of previous high-performing ads. Which Vertex AI Studio feature would best help them achieve consistent tone and style without custom model training?

Accepted Answer

Few-shot prompting with examples of previous ads. Few-shot prompting in Vertex AI Studio allows the model to infer the desired tone and style from a small set of example ads without requiring custom model training. This approach leverages the model's in-context learning capability, making it ideal for quickly adapting to a brand voice while avoiding the cost and complexity of fine-tuning.

Answer

Use of a pre-built template in Vertex AI Studio

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Supervised fine-tuning on the ad examples

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Model evaluation to compare outputs

Question 6

A developer is tuning a text-generation model for creative writing. They want the outputs to be more diverse and less repetitive. Which THREE parameters/changes can help? (Choose three.)

Accepted Answer

Increase temperature to 0.9. Increasing temperature to 0.9 raises the randomness of the probability distribution over the vocabulary, making the model more likely to sample less probable tokens. This directly increases output diversity and reduces repetitiveness by flattening the softmax curve, which is a standard technique for creative generation.

Answer

Reduce top-k to 10

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Reduce frequency penalty to 0.0

Question 7

A company is considering migrating from AWS Bedrock to Google Cloud for generative AI. They want to leverage Google's unique differentiators. Which THREE advantages does Google Cloud offer over AWS Bedrock and Azure OpenAI? (Select 3 options.)

Accepted Answer

Native multimodal support in Gemini (text, image, audio, video). Gemini is natively multimodal, processing text, images, audio, and video in a single model without needing separate fine-tuned components. AWS Bedrock and Azure OpenAI require stitching together different models or services (e.g., Amazon Rekognition + Titan, or GPT-4V + Whisper) to achieve similar multimodal capabilities, which increases latency and complexity.

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Built-in model explainability for all models

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Access to custom TPU hardware for training

Question 8

A healthcare startup fine-tunes a model to generate patient education materials. They want to ensure the model never gives medical advice, only information. They add a safety instruction, but the model sometimes still gives advice. What advanced technique should they apply?

Accepted Answer

Apply RLHF with a reward model that penalizes outputs containing medical advice. RLHF (Reinforcement Learning from Human Feedback) directly addresses the model's behavior by training a reward model that penalizes outputs containing medical advice. This aligns the model's generation with the safety instruction at a fundamental level, rather than relying on brittle post-hoc filters or static embeddings that can be easily circumvented by novel phrasings.

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Hard-code a list of prohibited phrases in a post-processing script

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Add a secondary classifier to rewrite any detected advice into general information

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Use semantic similarity to a 'medical advice' embedding and reject if close

Question 9

A company is using Gemini to generate marketing copy. They want the outputs to be more creative and varied. Which generation parameters should they adjust?

Accepted Answer

Increase temperature and adjust top-p to a higher value. Increasing temperature raises the randomness of token selection, making outputs more creative and varied, while adjusting top-p to a higher value (e.g., 0.9) allows the model to sample from a larger cumulative probability mass of likely tokens, further increasing diversity. Together, these parameters directly control the stochasticity of generation, which is essential for creative marketing copy.

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Set temperature to 0 and top-k to 1

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Decrease temperature and increase top-k

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Increase the context window length

Question 10

A financial services firm wants to use generative AI to assist employees in drafting emails. They are evaluating Duet AI in Gmail (now Gemini in Workspace). Which capability directly supports this use case?

Accepted Answer

Smart Compose in Gmail. Smart Compose in Gmail is a generative AI feature that provides real-time, context-aware suggestions to help users draft emails faster. It directly supports the use case of assisting employees in drafting emails by predicting and completing sentences as they type, reducing effort and improving efficiency.

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Speaker notes generation in Slides

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Meeting summaries in Meet

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Help me write in Docs