Generative AI Leader Practice Test 11 — 15 Questions

Question 1

A team is fine-tuning a large language model for medical advice. Which TWO techniques are most effective for improving the safety and reliability of the model's outputs?

Accepted Answer

Constitutional AI. Constitutional AI (A) is correct because it embeds a set of ethical principles directly into the model's training process, allowing the model to self-critique and revise its outputs to avoid harmful or unsafe medical advice. This technique proactively enforces safety constraints without requiring extensive human labeling, making it highly effective for high-stakes domains like healthcare.

Answer

Lowering the temperature to 0.0

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Increasing training data size

Answer

Increasing top_p to 1.0

Question 2

A developer uses a generative AI model with the system instruction shown. The response is correct but very brief. Which parameter adjustment could encourage more detail without losing accuracy?

Accepted Answer

Add 'Provide a detailed response' to the system instruction.. Modifying the system instruction to explicitly request a detailed response directly influences the model's output behavior without altering its underlying probability distribution. This approach preserves accuracy by keeping temperature, topK, and other sampling parameters at their default values, ensuring the model remains faithful to the training data while simply prompting for more elaboration.

Answer

Set temperature to 0 to make output deterministic.

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Set topK to 1 to focus on most likely tokens.

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Increase temperature to 1.5 to encourage creativity.

Question 3

A content generation model for e-commerce product descriptions repeats the same phrases across multiple descriptions (e.g., 'high-quality', 'best-in-class'). The team wants more varied and engaging output. Which parameter adjustment is most appropriate?

Accepted Answer

Increase the frequency penalty parameter to 1.0.. Increasing the frequency penalty to 1.0 penalizes tokens that have already appeared in the generated text, directly reducing repetition of phrases like 'high-quality' and 'best-in-class'. This encourages the model to use more diverse vocabulary and sentence structures, leading to varied and engaging product descriptions.

Answer

Decrease the max output tokens to 50.

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Increase the temperature parameter to 1.5.

Answer

Set the top-p value to a very small number like 0.1.

Question 4

A healthcare organization wants to use generative AI to draft email responses to patient inquiries. They need to ensure that the model never generates medical advice and always includes a disclaimer. Where should they enforce these constraints?

Accepted Answer

Configure safety settings and system instructions in the Vertex AI API call. Safety settings and system instructions are applied at the API call level to constrain model behavior. Fine-tuning would embed the rules but is heavy. Grounding doesn't enforce constraints. Model Garden is for model selection.

Answer

Select a model from Model Garden that is pre-trained on medical data

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Fine-tune the model with a dataset that only includes disclaimers

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Use Grounding with Google Search to restrict knowledge to approved sources

Question 5

A startup with limited budget wants to quickly test a generative AI use case for personalized email marketing. Which approach minimizes time-to-market and cost?

Accepted Answer

Use a managed API like the PaLM API with prompt engineering.. Using a managed API like the PaLM API with prompt engineering eliminates the need for infrastructure setup, model training, and data preparation. This approach leverages a pre-trained model via a simple REST API call, allowing the startup to iterate on prompts and achieve personalized email content in hours rather than weeks, minimizing both time-to-market and cost.

Answer

Hire a team of AI researchers to build a solution.

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Develop a custom model from scratch.

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Fine-tune a large open-source model on internal data.

Question 6

A business wants to build a generative AI application but has limited data science resources. What is the recommended path?

Accepted Answer

Use Vertex AI's AutoML and pre-built APIs to accelerate development. Vertex AI's AutoML and pre-built APIs are the recommended path because they allow the business to leverage Google's managed infrastructure and pre-trained models, significantly reducing the need for in-house data science expertise. AutoML automates model training, tuning, and deployment, while pre-built APIs (e.g., for vision, language) provide immediate access to generative capabilities without custom development. This approach accelerates time-to-market and lowers the barrier to entry for organizations with limited ML resources.

Answer

Hire a team of ML engineers to develop an in-house solution

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Purchase a third-party generative AI SaaS product off-the-shelf

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Build a custom model from scratch using TensorFlow

Question 7

A team is developing a mobile app that must run AI inference on-device for low latency and offline capability. Which Gemini model variant is designed specifically for on-device deployment?

Accepted Answer

Gemini Nano. Gemini Nano is the smallest and most efficient model in the Gemini family, specifically optimized for on-device deployment. It is designed to run directly on mobile devices (e.g., Android phones) using hardware acceleration like Google's Pixel Neural Core or Qualcomm's AI Engine, enabling low-latency inference and offline capability without requiring a cloud connection.

Answer

Gemini Pro

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Gemini Ultra

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Gemini Flash

Question 8

A large enterprise is deploying a generative AI system for automated contract review. The system must provide confidence indicators for its legal analysis. How should confidence indicators be implemented to maximize transparency?

Accepted Answer

Provide a numerical confidence score between 0 and 1 for each conclusion. Providing a numerical confidence score between 0 and 1 for each conclusion directly quantifies the model's certainty, enabling users to assess the reliability of each legal analysis. This approach maximizes transparency by allowing legal professionals to calibrate their trust in the AI's output, which is critical for high-stakes contract review where false positives or negatives carry significant risk.

Answer

Show the top-k most likely outcomes without probabilities

Answer

Display a binary pass/fail indicator for each analysis

Answer

Hide confidence indicators to avoid confusing users

Question 9

What is the primary purpose of Google DeepMind's SynthID technology?

Accepted Answer

To detect AI-generated content and identify the model that created it. SynthID is an invisible watermarking tool for AI-generated content, enabling identification of synthetic images, audio, text, or video without altering the user experience.

Answer

To remove harmful content from AI outputs

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To encrypt AI-generated content for secure transmission

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To improve the accuracy of generative AI models

Question 10

An AI team is building a customer support chatbot for a telecom company using a fine-tuned LLM on Vertex AI. The model performs well on common issues but fails to answer correctly for rare or novel problems, often providing plausible-sounding but incorrect solutions. The team has a large corpus of internal troubleshooting documents. They want to minimize incorrect answers while keeping latency low. Which approach should they take?

Accepted Answer

Implement a retrieval-augmented generation (RAG) pipeline using Vertex AI Search to fetch relevant documents before generating answers.. Implementing a RAG pipeline with Vertex AI Search allows the chatbot to retrieve relevant troubleshooting documents from the internal corpus in real-time, grounding the LLM's responses in authoritative sources. This approach directly addresses the problem of plausible-sounding but incorrect answers for rare/novel issues without requiring retraining, and it keeps latency low by fetching only the most relevant documents before generation.

Answer

Switch to a larger base model (e.g., Gemini Ultra) without any retrieval.

Answer

Collect more data on rare issues and continue fine-tuning the model weekly.

Answer

Use a few-shot prompt with 10 examples of rare problems and solutions.

Question 11

A team is selecting a foundation model for a text summarization use case. They need to consider factors that affect both model performance and production deployment. Which THREE factors are most critical? (Choose three.)

Accepted Answer

Inference latency and throughput capabilities.. Inference latency and throughput are critical for production deployment because they directly determine the user experience and operational cost. A model with high latency may be unsuitable for real-time summarization, while low throughput limits the number of concurrent requests the system can handle, affecting scalability and cost-efficiency.

Answer

Model parameter count (billions of parameters).

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Training data provenance and licensing.

Question 12

An enterprise wants to use Gemini 1.5 Flash for a real-time chat application with low latency. Which trade-off should they expect compared to Gemini 1.5 Pro?

Accepted Answer

Lower latency but potentially lower quality. Gemini 1.5 Flash is specifically optimized for lower latency and cost efficiency, making it ideal for real-time chat applications. However, this optimization comes at the cost of reduced model capacity and reasoning depth compared to Gemini 1.5 Pro, which prioritizes higher quality and accuracy over speed. Therefore, the expected trade-off is lower latency but potentially lower quality.

Answer

Higher quality and lower latency

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Lower cost but longer context window

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Higher accuracy but slower responses

Question 13

A global company deploying gen AI across multiple regions needs to minimize latency and comply with data sovereignty. What architecture should they adopt?

Accepted Answer

Multi-region deployment with Vertex AI. A multi-region deployment using a platform like Vertex AI lets the company place model endpoints and data processing in each region where users and regulatory requirements exist, minimizing network latency while keeping data resident within sovereign boundaries. Vertex AI supports regional endpoints and data residency controls, so inference and training can occur locally rather than routing everything through one jurisdiction. This directly satisfies both the latency and data sovereignty constraints simultaneously.

Answer

Single global deployment with CDN

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Use a third-party API

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On-premises deployment only

Question 14

A researcher wants to quickly prototype an AI model using a free, hosted Jupyter notebook environment with GPU support. Which Google product is BEST suited for this?

Accepted Answer

Google Colab. Google Colab is a free, hosted Jupyter notebook environment that provides GPU (and TPU) runtimes with zero setup, making it the fastest way to prototype AI models. It requires only a Google account and offers preinstalled libraries like TensorFlow and PyTorch. Kaggle Notebooks also offer free GPU but are tied to the Kaggle platform and datasets, while Vertex AI Workbench and AI Platform Notebooks are managed enterprise environments with billing and setup overhead.

Answer

AI Platform Notebooks

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Kaggle Notebooks

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Vertex AI Workbench

Question 15

A regulated healthcare organization needs to use Google Cloud AI services for processing protected health information (PHI). They require a signed Business Associate Agreement (BAA) and HIPAA compliance. Which service provides these assurances?

Accepted Answer

Vertex AI. Vertex AI is the correct choice because it is the only Google Cloud AI service that offers a signed Business Associate Agreement (BAA) and is explicitly covered under Google Cloud's HIPAA compliance framework. This allows regulated healthcare organizations to process protected health information (PHI) with the necessary contractual and security assurances.

Answer

Google AI Studio

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Colab Enterprise

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Gemini API directly