Generative AI Leader Google AI Ecosystem and Strategy Practice Test 6 — 15 Questions

Question 1

A healthcare startup needs to process medical claims and extract structured data (e.g., patient name, procedure codes, amounts) from scanned PDF forms. They require HIPAA compliance and prefer a pre-built Google Cloud service. Which service should they use?

Accepted Answer

Document AI Healthcare API with the Claims processor. Option C is correct because the Document AI Healthcare API with the Claims processor is a pre-built, HIPAA-compliant Google Cloud service specifically designed to extract structured data (e.g., patient name, procedure codes, amounts) from scanned medical claim forms. It leverages specialized machine learning models trained on healthcare documents, ensuring accurate parsing of fields like CPT codes and ICD-10 codes while meeting regulatory requirements.

Answer

Cloud Vision API with OCR

Answer

Vertex AI AutoML Tables

Answer

Natural Language AI with entity extraction

Question 2

A data scientist wants to train a large transformer model from scratch on custom data. They anticipate the training will require thousands of TPU-v5e chips for several weeks. Which Google Cloud infrastructure component is designed for this scale?

Accepted Answer

TPU v5e Pod slice (e.g., 256-chip pod). A is correct because a TPU v5e Pod slice (e.g., a 256-chip pod) is specifically designed for large-scale distributed training, providing high-bandwidth inter-chip interconnect (ICI) and collective communication optimizations that enable efficient scaling across thousands of chips for weeks-long training runs. This infrastructure component is purpose-built for the massive parallelism and fault tolerance required when training large transformer models from scratch on custom data.

Answer

BigQuery ML

Answer

Single TPU v5e device

Answer

Compute Engine with A100 GPU clusters

Question 3

A developer wants to quickly prototype a multimodal application that can process images and text using the Gemini API without incurring any cost. Which access tier should they use?

Accepted Answer

Google AI Studio. Google AI Studio (option D) is the correct choice because it provides a free, browser-based environment specifically designed for prototyping with the Gemini API, including multimodal capabilities for images and text, without requiring any payment or cloud billing setup. This makes it ideal for quick experimentation and cost-free development.

Answer

Colab Enterprise

Answer

Cloud Shell

Answer

Vertex AI Gemini API

Question 4

A financial services company needs to deploy an AI model for fraud detection. They require that the model's predictions be explainable, the data never leaves their VPC, and they need a guarantee of 99.95% uptime. Which TWO Google Cloud offerings should they consider? (Select 2 options.)

Accepted Answer

Vertex AI (with VPC Service Controls and SLA). Vertex AI (with VPC Service Controls and SLA) is correct because it allows the company to deploy the fraud detection model within their VPC using VPC Service Controls, ensuring data never leaves their network, and the SLA provides a 99.95% uptime guarantee. Vertex AI Explainable AI is correct because it provides built-in feature attributions and explanations for model predictions, meeting the explainability requirement for fraud detection models.

Answer

BigQuery ML

Answer

Cloud Functions

Answer

AI Platform Prediction

Question 5

A research team wants to leverage Google DeepMind's work to accelerate drug discovery. They are interested in using a model that predicts protein structures and another that can generate novel protein sequences with desired properties. Which TWO Google DeepMind achievements are most relevant? (Select 2 options.)

Accepted Answer

AlphaFold. AlphaFold (D) is correct because it is Google DeepMind's breakthrough model for predicting protein 3D structures from amino acid sequences, which directly accelerates drug discovery by enabling researchers to understand target proteins. AlphaProteo (E) is correct because it is DeepMind's AI system designed to generate novel protein sequences that bind to specific targets, effectively creating new proteins with desired therapeutic properties.

Answer

WaveNet

Answer

Gemini

Answer

AlphaCode

Question 6

A company has a large dataset of customer support tickets stored in BigQuery. They want to predict ticket severity (high, medium, low) using SQL queries without moving data out of BigQuery. Which service should they use?

Accepted Answer

BigQuery ML. BigQuery ML (D) is correct because it allows users to create and execute machine learning models directly within BigQuery using SQL, without moving data out of the warehouse. For a classification task like predicting ticket severity, BigQuery ML supports models such as logistic regression, boosted trees, and deep neural networks, all trained and deployed using standard SQL queries on data already in BigQuery.

Answer

Vertex AI AutoML Natural Language

Answer

Cloud Natural Language API

Answer

Gemini API

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 company needs to deploy a chatbot on a mobile device that must work offline. They want to use Gemini for natural language understanding but need minimal latency and no cloud dependency. Which Gemini model variant is most appropriate?

Accepted Answer

Gemini Nano. Gemini Nano is the most appropriate variant because it is specifically designed for on-device deployment, enabling offline operation with minimal latency. It is optimized for mobile devices through quantization and efficient architecture, allowing natural language understanding without any cloud dependency.

Answer

Gemini Flash

Answer

Gemini Pro

Answer

Gemini Ultra

Question 9

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 10

A data science team wants to collaborate on a machine learning project using a shared notebook environment that supports Python and R, with free access to GPUs for prototyping. Which Google tool is most suitable?

Accepted Answer

Colab. Colab (D) is the most suitable Google tool because it provides a shared notebook environment that supports both Python and R via the `%load_ext rpy2.ipython` magic, offers free access to GPUs (e.g., NVIDIA T4 or V100) for prototyping, and enables real-time collaboration through Google Drive integration. This directly matches the team's requirements for a collaborative, multi-language notebook with free GPU resources.

Answer

Kaggle

Answer

Cloud Shell

Answer

Vertex AI Workbench

Question 11

A global e-commerce company wants to build a multilingual customer support chatbot that can understand queries in 50 languages and respond in the same language. They need to process both text and images (e.g., product photos) in the queries. Which THREE Google Cloud services should they consider? (Select 3 options.)

Accepted Answer

Translation API. Translation API is correct because it provides real-time language translation across 100+ languages, enabling the chatbot to understand and respond in 50 languages. It integrates directly with other Google Cloud services to maintain language consistency in a multilingual pipeline.

Answer

Vision API

Answer

Natural Language API

Answer

Text-to-Speech

Question 12

A company using Vertex AI for model training wants to reduce costs by automatically stopping training when the model's performance stops improving. Which Vertex AI feature should they configure?

Accepted Answer

Early stopping (via Vertex AI Training). Early stopping in Vertex AI Training stops training runs when performance plateaus. Hyperparameter tuning optimizes parameters, not cost. Workbench is for notebooks. AutoML handles this automatically but is for specific model types.

Answer

AutoML

Answer

Hyperparameter tuning

Answer

Vertex AI Workbench

Question 13

A regulated industry client requires that all AI model predictions be logged with a traceable audit trail, including the model version, input data, and output, for compliance with internal policies. Which Vertex AI feature should they enable?

Accepted Answer

Vertex AI Model Monitoring (with prediction logging). Vertex AI Model Monitoring with prediction logging captures model version, input data, and output for every prediction request, storing them in BigQuery or Cloud Logging to create a traceable audit trail. This directly meets compliance requirements for regulated industries by providing immutable logs that can be queried and audited. Other Vertex AI features like Explainable AI or Feature Store do not offer the same comprehensive logging and version tracking needed for audit trails.

Answer

Vertex AI Explainable AI

Answer

VPC Service Controls

Answer

Vertex AI Feature Store

Question 14

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. Option C is correct because 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.

Answer

Cloud Video Intelligence API and Translation API

Answer

Document AI and Translation API

Answer

Text-to-Speech and Translation API

Question 15

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). Option C is correct because 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.

Answer

Built-in model explainability for all models

Answer

Access to custom TPU hardware for training