Generative AI Leader Exam Simulation 1 — 100 Questions

Question 1

What are THREE best practices for responsible generative AI deployment?

Accepted Answer

Monitor model performance and data drift over time. Continuous monitoring of model performance and data drift is essential for maintaining the reliability and safety of generative AI systems. Data drift occurs when the statistical properties of input data change over time, which can degrade model accuracy and introduce unintended biases. Regular monitoring allows teams to detect these shifts early and retrain or adjust the model to sustain responsible behavior.

Answer

Maximize model size for best accuracy

Answer

Avoid fine-tuning the model to preserve original capabilities

Question 2

A legal team wants to use GenAI to review contracts and highlight risky clauses. They need the AI to consistently follow a specific classification taxonomy. The team has a small set of labeled examples (500 contracts). Which approach yields the BEST accuracy for this use case?

Accepted Answer

Fine-tune a base model using the labeled examples in Vertex AI. Fine-tuning a base model on the 500 labeled contracts teaches the model the specific classification taxonomy directly in its weights, producing consistent, high-accuracy outputs that follow the exact label set. With a small but well-labeled dataset, supervised fine-tuning is the most reliable way to enforce a fixed taxonomy rather than relying on prompt instructions.

Answer

Prompt engineer a large foundation model with few-shot examples in Vertex AI Studio

Answer

Use the RAG Engine to retrieve similar clauses and ask the model to classify

Answer

Use a larger foundation model without fine-tuning and rely on its pre-trained knowledge

Question 3

A data science team wants to run a machine learning model directly on data stored in BigQuery without moving the data to a separate environment. Which Google Cloud service should they use?

Accepted Answer

BigQuery ML. BigQuery ML enables creating and executing ML models using standard SQL queries directly on data in BigQuery, eliminating data movement.

Answer

Vertex AI Training

Answer

Cloud Dataproc

Answer

Google AI Studio

Question 4

A research team wants to use Google's AI to generate video content from text prompts for a creative project. Which Google Cloud generative AI model should they use?

Accepted Answer

Veo. Veo is Google's generative AI model specifically designed for text-to-video generation, making it the correct choice for creating video content from text prompts. It is part of Google's Vertex AI model portfolio and supports high-definition video generation with cinematic controls. Imagen, Codey, and Gemini serve different modalities.

Answer

Imagen

Answer

Codey

Answer

Gemini

Question 5

A company wants to ensure that AI-generated images can be traced back to their origin to prevent misuse. Which Google technology provides invisible watermarking for AI-generated content?

Accepted Answer

SynthID. SynthID is Google DeepMind's technology for embedding invisible watermarks into AI-generated images, audio, text, and video.

Answer

Datasheets for Datasets

Answer

Model Cards

Answer

People + AI Guidebook

Question 6

A company is building a document summarization tool using Vertex AI Gemini API. They notice that the model sometimes returns incomplete summaries that miss key points. Which approach is most likely to improve summary quality without increasing token usage significantly?

Accepted Answer

Refine the system instruction to specify the desired summary format and key elements to include. Refining the system instruction directly addresses the root cause of incomplete summaries by providing explicit guidance on the desired output format and key elements to include. This approach improves the model's adherence to the task without increasing the number of input or output tokens, as it only modifies the instruction text, not the document length or generation limits.

Answer

Increase the context window to include more of the document

Answer

Switch to a larger Gemini model (e.g., from 1.0 Pro to 1.5 Pro)

Answer

Increase the max output token limit to allow longer summaries

Question 7

Which Google Cloud AI service would you use to transcribe customer service call recordings into text for subsequent analysis?

Accepted Answer

Speech-to-Text. Speech-to-Text (STT) is the correct service because it is specifically designed to convert audio speech into written text using automatic speech recognition (ASR) models. For customer service call recordings, STT can handle domain-specific vocabulary, multiple speakers, and various audio formats, enabling downstream analysis like sentiment analysis or keyword extraction.

Answer

Text-to-Speech

Answer

Translation API

Answer

Document AI

Question 8

A user reports that the model's response to the same prompt varies significantly across different calls. Which parameter change would most likely reduce variability?

Accepted Answer

Decrease temperature to 0.2.. Temperature controls the randomness of token sampling. Lowering temperature (e.g., to 0.2) makes the model's output more deterministic by reducing the probability of low-likelihood tokens, thus decreasing variability across calls for the same prompt.

Answer

Decrease topK to 10.

Answer

Increase candidateCount to 3.

Answer

Increase maxOutputTokens to 2000.

Question 9

A machine learning engineer needs to deploy a Gemini model on an Android device for offline inference (no internet connection) to provide real-time suggestions. Which Gemini model variant is MOST appropriate?

Accepted Answer

Gemini Nano. Gemini Nano is specifically designed for on-device inference with small memory footprint. Ultra, Pro, and Flash are cloud-only models that require internet connectivity.

Answer

Gemini Ultra

Answer

Gemini Pro

Answer

Gemini Flash

Question 10

A company wants to build an internal knowledge base that allows employees to ask questions about company policies in natural language. The knowledge base is stored in a Google Cloud SQL database. Which architecture should they use?

Accepted Answer

Use Vertex AI Agent Builder with Grounding to connect to Cloud SQL. Vertex AI Agent Builder with Grounding allows connecting to the database and answering questions based on its content, providing a natural language interface.

Answer

Use Gemini API with a prompt that includes all policies

Answer

Use AutoML Natural Language to classify questions

Answer

Export Cloud SQL to BigQuery and use BigQuery ML

Question 11

A manufacturing company wants to use GenAI to generate maintenance reports from sensor data. They need structured output (JSON) for downstream systems, and they want to reduce token costs. Which THREE strategies should they use?

Accepted Answer

Use batch API requests for multiple sensor readings. Structured output ensures JSON format; batch requests reduce cost; the smallest suitable model minimizes token usage. Few-shot adds tokens; caching may not help for diverse sensor data.

Answer

Use the largest available model to ensure accuracy

Answer

Include multiple few-shot examples of JSON in every prompt

Question 12

A company is using a large language model for a customer-facing chat application. They notice that the model sometimes generates plausible-sounding but incorrect information. Which strategy is most effective to reduce this issue?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) to retrieve relevant documents before generating a response.. Retrieval-Augmented Generation (RAG) grounds the model's responses in retrieved, authoritative documents at inference time, which directly reduces hallucination by supplying factual context the model can cite. It does not require retraining and can be updated as source documents change. This is the most effective and operationally practical mitigation for plausible-sounding but incorrect outputs.

Answer

Fine-tune the model on a dataset of corrected conversations.

Answer

Reduce the context window to limit the information the model considers.

Answer

Increase the temperature parameter to make the model more deterministic.

Question 13

A global corporation with 50,000 employees has seen rapid adoption of GenAI across marketing, product, and engineering teams. Each team selected its own models and cloud accounts, resulting in fragmented governance, unexpected costs, and varying output quality. The CFO demands a unified strategy to control costs and ensure consistency. The Chief AI Officer proposes several solutions. Which course of action best balances control with innovation?

Accepted Answer

Establish a GenAI Center of Excellence (CoE) that provides approved models, shared APIs, and best practices, while allowing team-specific customizations. A GenAI Center of Excellence (CoE) provides centralized governance through approved models and shared APIs, enabling cost control and quality consistency while preserving team-level flexibility for innovation. This balances the CFO's need for unified strategy with the CAIO's goal of avoiding rigid mandates that stifle experimentation.

Answer

Migrate all GenAI workloads to a single on-premises server to reduce cloud costs

Answer

Mandate all teams use a single model (e.g., Gemini) via a centralized Vertex AI endpoint with usage quotas

Answer

Allow teams to continue using their own models but require them to submit monthly cost reports

Question 14

A company has deployed a GenAI-powered report generation system using Vertex AI. They notice that the cost is higher than expected. Investigation shows that many requests include very long prompts with repetitive boilerplate text. Which cost optimization strategy is MOST effective?

Accepted Answer

Enable context caching for repeated prompt prefixes. Caching repeated prompt prefixes can significantly reduce token usage and cost. Batch requests help with throughput but not with per-request token savings. Reducing model size may hurt quality. Ignoring is not a strategy.

Answer

Increase the batch size for batch requests

Answer

Switch to a smaller model size

Answer

Ignore the cost increase as it will stabilize

Question 15

A developer needs to integrate a GenAI model into an existing customer relationship management (CRM) system. The CRM exposes REST APIs and runs on-premises. Which integration pattern is MOST suitable?

Accepted Answer

Implement an API-first integration by calling Vertex AI API from the CRM's backend. API-first integration using Vertex AI API allows any system with HTTP capabilities to call the GenAI model. Workspace add-ons and Apps Script are for Google Workspace only. Fine-tuning doesn't help with integration.

Answer

Use Apps Script to call the model from Google Sheets

Answer

Fine-tune a model on CRM data and deploy it on-premises

Answer

Build a custom Google Workspace add-on

Question 16

A legal firm wants to automate contract analysis to extract key clauses and risks. They have 10,000 contracts in PDF format. The solution must handle varying layouts and be cost-effective. Which approach is BEST?

Accepted Answer

Use Document AI to convert PDFs to structured text, then use Vertex AI with a prompt that specifies clauses to extract. Using Document AI to parse PDFs into text, then Vertex AI with a structured prompt for clause extraction combines robust document understanding with flexible GenAI. Fine-tuning on 100 contracts is insufficient for layout variation. Agent Builder is overkill. Direct Gemini API on raw PDFs loses document structure.

Answer

Build a retrieval-augmented generation (RAG) system in Vertex AI Agent Builder

Answer

Use the Gemini API directly with the raw PDF files as input

Answer

Fine-tune a Gemini model on 100 annotated contracts and run inference on all contracts

Question 17

Which THREE are valid methods to reduce bias in generative AI outputs?

Accepted Answer

Using a more diverse training dataset. Option C is correct because a more diverse training dataset reduces representational bias by exposing the model to a wider range of demographics, cultures, languages, and viewpoints, so the learned distribution is less skewed toward a dominant group. Option D is correct because safety filters (e.g., content moderation classifiers, toxicity detectors, or bias guardrails applied to prompts and outputs) can detect and block biased or harmful generations, mitigating bias at inference time. Option E is correct because prompt engineering that explicitly instructs the model to be fair, neutral, or inclusive (e.g., system prompts specifying balanced representation) steers generation toward less biased outputs without retraining. Option A is not correct because restricting prompts to English narrows the linguistic and cultural scope, which can amplify bias rather than reduce it. Option B is not correct because increasing model size alone does not remove bias and can even amplify biases present in the training data.

Answer

Using only English prompts

Answer

Increasing model size

Question 18

A company has been using an on-premises ML infrastructure for generative AI and wants to migrate to Google Cloud. They have a pipeline that fine-tunes a large language model weekly using a proprietary dataset. The migration must minimize downtime and data transfer costs. Which approach best addresses these requirements?

Accepted Answer

Use Vertex AI Pipelines to orchestrate the fine-tuning process, and use Vertex AI Managed Datasets to incrementally sync new data with BigQuery as the source.. Vertex AI Pipelines provides a managed, serverless orchestration service that can run the weekly fine-tuning workflow with minimal operational overhead, while Vertex AI Managed Datasets can incrementally sync new data from BigQuery, reducing data transfer costs by avoiding full dataset copies. This combination minimizes downtime because the pipeline can be triggered on a schedule without manual intervention, and incremental syncs avoid re-transferring the entire proprietary dataset each week.

Answer

Use AutoML to train a new model directly from the dataset without fine-tuning.

Answer

Deploy the existing pipeline on a Google Kubernetes Engine cluster and use Google Cloud Filestore for shared storage.

Answer

Use Cloud Storage Transfer Service to move all data to Cloud Storage, then set up a Vertex AI custom training job to run the fine-tuning.

Question 19

An organization wants to ensure that when their employees use the Gemini API via Vertex AI, the grounding searches are restricted to internal company knowledge bases rather than the public web. Which feature should they enable?

Accepted Answer

Vertex AI Search (with private data indexing). Vertex AI Search allows organizations to index private, internal data sources (e.g., documents, databases) and use them as the grounding source for Gemini API queries. By enabling this feature, grounding searches are restricted to the indexed private knowledge base, ensuring no public web results are used. This directly meets the requirement to keep grounding internal.

Answer

Enterprise Data Governance on Vertex AI

Answer

VPC Service Controls

Answer

Google Search Grounding

Question 20

A company wants to use a pre-trained language model for customer support summarization. They need to ensure responses are concise and accurate. Which prompt engineering technique is most effective?

Accepted Answer

Few-shot prompting with examples. Few-shot prompting (B) is most effective because it provides the model with a small set of example input-output pairs (e.g., a customer query and its concise summary), which guides the model to produce outputs that match the desired format, length, and accuracy. This technique is particularly useful for summarization tasks where consistency and adherence to a specific style are critical, as it reduces ambiguity without requiring fine-tuning.

Answer

Zero-shot prompting

Answer

Chain-of-thought prompting

Answer

Negative prompting