Generative AI Leader Practice Test 4 — 25 Questions

Question 1

An enterprise deploys a large language model (LLM) for internal document summarization. Users complain that summaries sometimes include statements not present in the original document. Which mitigation strategy should the team prioritize to address this hallucination issue?

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

Accepted Answer

Implement retrieval-augmented generation (RAG) to ground the model in the original documents and require citations.. Retrieval-Augmented Generation (RAG) is the most direct and effective mitigation for hallucination in document summarization because it forces the LLM to base its output on retrieved chunks of the original document. By requiring citations, the model must reference specific passages, making it verifiable and reducing the likelihood of fabricating content. This grounds the generation in the source material, addressing the root cause of hallucination—lack of factual grounding—rather than relying on post-hoc correction or output tuning.

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Train a discriminator model to detect hallucinations and perform adversarial training.

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Apply reinforcement learning from human feedback (RLHF) using a reward model that penalizes hallucinations.

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Reduce the model's temperature parameter to 0 to make outputs deterministic.

Question 2

A company is deploying a generative AI application using Vertex AI. They need to minimize latency for real‑time inference while maintaining high quality. Which TWO actions are most effective?

Accepted Answer

Use a smaller model like Gemini Flash. Option B is correct because Gemini Flash is a lower-latency, lighter-weight model in the Gemini family, purpose-built for high-throughput, real-time tasks, so it directly reduces inference latency while still delivering strong quality for most generative AI use cases. Option D is correct because output tokens are generated sequentially (autoregressive decoding), so capping max output tokens to the shortest acceptable length proportionally cuts generation time and thus end-to-end latency. Option A is not appropriate because batching requests improves throughput and cost efficiency, not per-request real-time latency, and can actually add queuing delay. Option C is wrong because Gemini Pro is larger and slower than Flash, trading latency for quality rather than minimizing latency. Option E is wrong because raising temperature to 1.0 only affects randomness/creativity of sampling and does not reduce latency.

Answer

Batch multiple inference requests together

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Use Gemini Pro instead of Gemini Flash to ensure quality

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Increase the temperature to 1.0 for more creative outputs

Question 3

A streaming platform uses a large generative model for personalized content suggestions. Budget constraints require minimizing inference costs without significantly degrading quality. Which approach is most effective?

Accepted Answer

Use a distilled version of the model.. Distillation trains a smaller 'student' model to mimic a larger 'teacher' model, reducing parameter count and inference latency while retaining most of the recommendation quality. This directly addresses the budget constraint by lowering compute and memory costs per inference, making it the most effective approach among the options.

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Deploy the model on higher-end accelerators to save time.

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Implement stronger safety filters to reduce output length.

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Cache frequent prompts to avoid regeneration.

Question 4

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 5

A machine learning engineer wants to reduce the latency of a Gemini-based chatbot running in production. Which TWO strategies would be MOST effective?

Accepted Answer

Switch from Gemini Pro to Gemini Flash. Option A is correct because Gemini Flash is a lighter, lower-latency model variant than Gemini Pro, so switching to it directly reduces the time the model takes to generate each response. Option B is correct because max_output_tokens caps the number of tokens the model generates, and since generation time scales roughly with output length, lowering this value shortens the response and thus the overall latency. Option C is not correct because streaming improves perceived latency by delivering tokens incrementally, but it does not reduce the actual total generation time. Option D is not correct because fine-tuning adapts the model to a task and does not inherently lower inference latency. Option E is not correct because raising temperature to 1.0 affects randomness and creativity, not speed, and can even make outputs longer or less predictable.

Answer

Enable streaming mode

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Fine-tune the model on the specific task

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Increase the temperature to 1.0

Question 6

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 7

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 8

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 9

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 10

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 11

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 12

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 13

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 14

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.

Answer

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 15

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

Question 16

To improve factuality in generative AI, which is the best approach?

Accepted Answer

Grounded generation with citations. Grounded generation with citations directly addresses factuality by forcing the model to retrieve and cite verifiable sources (e.g., from a knowledge base or document store) before generating an answer. This approach, often implemented via retrieval-augmented generation (RAG), ensures outputs are anchored to external evidence rather than relying solely on the model's parametric memory, which can produce hallucinations. Citations also enable users to verify claims, making this the most effective technique for improving factual accuracy.

Answer

Set top_p to 0.1

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Reduce output length

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Increase model size

Question 17

Which technique allows a model to incorporate real-time data from external APIs?

Accepted Answer

RAG with tool calling. RAG with tool calling is correct because it enables a generative AI model to query external APIs in real-time, retrieve up-to-date information, and incorporate that data into its response. This technique combines retrieval-augmented generation (RAG) with function calling, where the model outputs a structured request (e.g., a JSON object) to invoke an API, receive the result, and then generate a context-aware answer. Unlike static methods, this allows dynamic data integration without retraining.

Answer

Prompt engineering

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Fine-tuning

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Model pruning

Question 18

A company is fine‑tuning a large language model for a domain‑specific task. They have a limited budget and want to minimize the cost of fine‑tuning. Which TWO approaches are most cost‑effective?

Accepted Answer

Use a smaller base model like Gemini Flash. Option A is correct because using a smaller base model such as Gemini Flash reduces compute and memory requirements, which directly lowers the cost of fine-tuning while still being adequate for many domain-specific tasks. Option D is correct because adapter-based fine-tuning with LoRA freezes the base model weights and trains only small low-rank adapter matrices, dramatically reducing the number of trainable parameters, GPU memory, and training time compared to full fine-tuning. Option B is not cost-effective because full fine-tuning updates all model parameters, requiring substantially more compute, memory, and storage. Option C is not cost-effective because increasing training epochs raises compute time and cost without guaranteeing better results, and can even cause overfitting. Option E is not cost-effective because a larger base model like Gemini Ultra increases training and inference costs significantly, which conflicts with the limited-budget goal.

Answer

Use full fine‑tuning for better quality

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Increase the number of training epochs

Answer

Use a larger base model like Gemini Ultra

Question 19

A developer wants to experiment with Gemini Nano for on-device inference in a mobile app. Which Gemini API tier or environment provides access to Gemini Nano?

Accepted Answer

MediaPipe and Android AICore. Gemini Nano is the smallest Gemini model designed specifically for on-device inference on mobile devices. Access to Gemini Nano is provided through MediaPipe and Android AICore, which are the frameworks that enable running the model locally on Android devices without requiring a network connection to Google's cloud servers.

Answer

Vertex AI

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Google AI Studio

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Gemini API directly

Question 20

A marketing team wants to generate blog post ideas and draft outlines. They need a solution that works within Google Docs and leverages a foundation model without leaving the document editor. Which service should they use?

Accepted Answer

Gemini for Google Workspace (Duet AI). Gemini for Google Workspace (formerly Duet AI) is the correct choice because it is the only service that integrates generative AI directly into Google Docs, allowing users to generate blog post ideas and outlines without leaving the document editor. It leverages a foundation model (Gemini) natively within the Workspace environment, enabling seamless, in-context assistance for content creation tasks.

Answer

Vertex AI Studio

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Model Garden

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Vertex AI Agent Builder