Generative AI Leader Google AI Ecosystem and Strategy — 10 Questions

Question 1

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Fine-tune a base LLM on the policy documents monthly

Answer

Train a custom model from scratch on the policy documents each month

Question 2

Which Google Cloud service allows you to run machine learning models directly using SQL queries on data in BigQuery?

Accepted Answer

BigQuery ML. BigQuery ML enables users to create, train, and deploy ML models using standard SQL, eliminating the need to move data to a separate environment.

Answer

Cloud Functions

Answer

Vertex AI

Answer

Dataflow

Question 3

A financial services firm needs to use Gemini for analyzing customer transaction data. They require that all data remain within their VPC and that model inference logs be auditable. Which access tier should they choose?

Accepted Answer

Vertex AI. Vertex AI provides enterprise controls like VPC-SC, data isolation, and audit logging, while Google AI Studio is a prototyping environment without these guarantees.

Answer

Colab Enterprise

Answer

Gemini API without Vertex AI

Answer

Google AI Studio

Question 4

A research team is training a large multimodal model and needs to minimize training time for a fixed budget. Which Google Cloud infrastructure is specifically designed for large-scale training workloads?

Accepted Answer

TPU Pods. TPU pods are purpose-built for large-scale ML training, offering high-bandwidth interconnect and optimized performance for TensorFlow/JAX.

Answer

Compute Engine with A100 GPUs

Answer

Kubernetes Engine with GPU nodes

Answer

Cloud TPU v5e single chip

Question 5

Which Google service provides free access to Jupyter notebooks with GPU support for prototyping ML models?

Accepted Answer

Colab. Google Colab is a free notebook service with GPU support, commonly used for prototyping.

Answer

Kaggle Notebooks

Answer

Vertex AI Workbench

Answer

BigQuery Studio

Question 6

A company wants to extract structured data from scanned invoices. Which Google Cloud AI service is purpose-built for this use case?

Accepted Answer

Document AI. Document AI is designed for document understanding, including invoice parsing.

Answer

Vision AI

Answer

Translation API

Answer

Natural Language AI

Question 7

A developer is building a mobile app that needs to run an AI model on-device for low-latency inference even without internet. Which Gemini model variant is designed for on-device deployment?

Accepted Answer

Gemini Nano. Gemini Nano is optimized for on-device execution on mobile devices.

Answer

Gemini Pro

Answer

Gemini Ultra

Answer

Gemini Flash

Question 8

A healthcare startup needs to deploy an AI model that processes protected health information (PHI). They require a HIPAA BAA with the cloud provider. Which Google Cloud AI service can meet this compliance requirement?

Accepted Answer

Vertex AI. Vertex AI offers a HIPAA BAA for covered entities, while other services may not.

Answer

Colab

Answer

Gemini API via Google AI Studio

Answer

Cloud Vision API

Question 9

Which Google AI research organization is responsible for AlphaFold, a breakthrough in protein structure prediction?

Accepted Answer

Google DeepMind. Google DeepMind is the research lab behind AlphaFold, AlphaCode, and other notable achievements.

Answer

Google Research

Answer

Google Brain

Answer

X Development

Question 10

A company wants to ground Gemini responses with real-time Google Search results to improve accuracy of current events. Which feature enables this?

Accepted Answer

Google Search grounding. Google Search grounding connects Gemini to live search results to reduce hallucinations and provide up-to-date information.

Answer

BigQuery ML

Answer

Vertex AI Vector Search

Answer

Gemini API function calling