Generative AI Leader Practice Test 10 — 10 Questions

Question 1

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.

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Set top_p to 0.1

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Reduce output length

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Increase model size

Question 2

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.

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Prompt engineering

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Fine-tuning

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Model pruning

Question 3

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

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Use a larger base model like Gemini Ultra

Question 4

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.

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Vertex AI

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Google AI Studio

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Gemini API directly

Question 5

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.

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Vertex AI Studio

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Model Garden

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Vertex AI Agent Builder

Question 6

A healthcare organization is deploying a generative AI application that processes Protected Health Information (PHI). They must ensure compliance with HIPAA. Which Google Cloud offering should they use?

Accepted Answer

Vertex AI APIs with data residency and HIPAA compliance enabled. Vertex AI APIs with data residency and HIPAA compliance enabled is the correct choice because it meets regulatory requirements for PHI. Other options either lack HIPAA coverage or introduce unnecessary complexity.

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Google Workspace with Duet AI

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Model Garden open-source models deployed on Compute Engine

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Gemini API with default settings

Question 7

A media company wants to automatically generate captions for video content in multiple languages. The captions should be synced with the audio timeline. Which combination of Google Cloud services is most appropriate?

Accepted Answer

Speech-to-Text and Translation API. The workflow requires first transcribing the audio track into text using Speech-to-Text API, which provides timestamps for each word or phrase to ensure synchronization with the video timeline. The resulting transcript is then passed to the Translation API to generate captions in the target languages, preserving the original timing data for alignment.

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Cloud Video Intelligence API and Translation API

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Document AI and Translation API

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Text-to-Speech and Translation API

Question 8

A company deployed a large language model on Vertex AI using the configuration shown in the exhibit. During peak usage, users report high latency. Which change is most likely to improve latency?

Accepted Answer

Increase minReplicaCount to 3.. Increasing minReplicaCount to 3 ensures that at least three instances of the model are always running and ready to serve requests. This reduces cold-start latency and distributes the load across multiple replicas, directly addressing high latency during peak usage by providing more concurrent serving capacity.

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Remove the accelerator to simplify deployment.

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Switch to a GPU with more memory, such as NVIDIA_TESLA_A100.

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Change machineType to n1-standard-4 to reduce cost.

Question 9

A healthcare startup is exploring GenAI for clinical note summarization. They have concerns about patient data privacy. Which Google Cloud approach best addresses privacy while still using powerful models?

Accepted Answer

Use Vertex AI with data residency controls and no external data sharing. Vertex AI with data residency controls and no external data sharing ensures that patient data remains within specified geographic boundaries and is not used for model training or improvement, directly addressing healthcare privacy regulations like HIPAA. This approach leverages Google Cloud's powerful models while maintaining strict data governance, unlike options that risk data exposure or lack enterprise-grade controls.

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Deploy open-source models on-premises

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Use a third-party API with anonymization of patient data

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Use Vertex AI with model customization (fine-tuning)

Question 10

A team monitors their generative AI model on Vertex AI. They notice output quality declining. Which metric is most likely the root cause?

Accepted Answer

Input token count per request is increasing.. A is correct because an increasing input token count per request can degrade output quality by diluting the model's attention across a longer context window. In transformer-based models like those on Vertex AI, the attention mechanism has a fixed capacity; as input tokens grow, the model may lose focus on critical information, leading to less coherent or relevant outputs. This is a common issue in production systems where users gradually add more context without trimming irrelevant tokens.

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Output token count is decreasing.

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Prediction latency is stable.

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Error rate is less than 1%.