Generative AI Leader Google Cloud's Generative AI Offerings Practice Test 4 — 15 Questions

Question 1

A company is using Vertex AI Model Garden to discover and test various foundation models. They need a model that can generate code from natural language. Which model should they select?

Accepted Answer

Codey. Codey models are specifically designed for code generation and completion. Imagen generates images, Chirp generates speech, Med-PaLM is for medical domain.

Answer

Chirp

Answer

Med-PaLM

Answer

Imagen

Question 2

During a load test, a Vertex AI endpoint serving a large language model experiences high latency and increased error rates. The endpoint is configured with autoscaling. What is the most likely cause?

Accepted Answer

The autoscaling metric is based on CPU utilization but the model is GPU-bound. If autoscaling is based on CPU utilization but the model is GPU-bound, the scaling metric does not reflect the actual load, causing insufficient resources.

Answer

There is a network bottleneck

Answer

The model size is too large for the machine type

Answer

The endpoint is using a global load balancer

Question 3

A financial services firm needs to generate synthetic data for training models while ensuring that no real customer data leaks. Which technique should they use?

Accepted Answer

Differential privacy during fine-tuning. Differential privacy provides formal guarantees that individual data points cannot be reverse-engineered. Data masking alone may not be sufficient.

Answer

Using the Vertex AI PII redaction service

Answer

Using a public foundation model without fine-tuning

Answer

Data masking before training

Question 4

A project manager wants to understand which Google Cloud generative AI services are subject to the 'Prohibited Use' policy. Where can they find the most up-to-date information?

Accepted Answer

The Google Cloud Acceptable Use Policy. The Google Cloud Acceptable Use Policy outlines prohibited uses for all services, including generative AI. Other documents may not be the authoritative source.

Answer

Google Cloud documentation

Answer

Google's AI Principles

Answer

The Gemini Terms of Service

Question 5

A company is evaluating Google Cloud's generative AI offerings for enterprise use. Which TWO considerations are most important when selecting the right model deployment option?

Accepted Answer

Data residency. Latency requirements and data residency are critical enterprise considerations. Model size and training time are secondary, and developer preference is not a primary factor.

Answer

Developer preference

Answer

Model size

Answer

Training time

Question 6

An organization is building a generative AI application on Vertex AI. Which THREE actions should they take to ensure responsible AI practices?

Accepted Answer

Implement human review for sensitive outputs. Implementing human review, conducting fairness evaluation, and creating a safety policy with content filtering are key responsible AI practices. Using only Google's models does not guarantee responsibility, and disabling filtering is counterproductive.

Answer

Disable content filtering

Answer

Use only Google's foundation models

Question 7

A developer wants to use the Gemini API to generate creative text. Which TWO parameters can they adjust to influence the output?

Accepted Answer

Top-k. Temperature and top-k are standard parameters to control randomness and creativity. Color space, image size, and audio sample rate are not relevant for text generation.

Answer

Color space

Answer

Audio sample rate

Answer

Image size

Question 8

A company is building a customer support chatbot using Vertex AI Agent Builder. They want the agent to answer questions based on internal knowledge base documents stored in Cloud Storage. Which feature should they configure to ensure the agent can retrieve relevant information from these documents?

Accepted Answer

Enable grounding with a data store. Grounding connects the agent to external data sources like Cloud Storage, allowing it to retrieve and use information from the knowledge base. Option A is wrong because deploying to an endpoint is for model serving, not data retrieval. Option B is wrong because safety filters control content, not retrieval. Option D is wrong because custom training is for fine-tuning, not retrieval.

Answer

Deploy the agent to a Vertex AI endpoint

Answer

Fine-tune a Gemini model on the knowledge base

Answer

Configure a safety filter to block irrelevant queries

Question 9

A data scientist is using Vertex AI Model Registry to manage multiple versions of a custom text classification model. They need to ensure that only the version that passes all evaluation metrics can be deployed to a Vertex AI Endpoint for online predictions. What deployment strategy should they use?

Accepted Answer

Set up continuous evaluation with model monitoring to auto-promote versions that meet thresholds. Model Registry supports continuous evaluation and can automatically promote versions that meet thresholds. Option A is wrong because manual version control is error-prone. Option B is wrong because A/B testing is for traffic splitting, not automatic promotion. Option D is wrong because batch predictions are offline.

Answer

Use A/B testing between versions and manually select the best performer

Answer

Manually track version IDs and deploy the latest version

Answer

Deploy all versions to a single endpoint and route traffic manually

Question 10

A startup wants to generate images from text descriptions for their marketing materials. They prefer a managed service that requires minimal coding. Which Google Cloud generative AI offering should they use?

Accepted Answer

Vertex AI Imagen. Imagen on Vertex AI provides a managed API for text-to-image generation with simple API calls. Option A is wrong because Speech-to-Text is for audio transcription. Option B is wrong because Natural Language AI is for text analysis. Option D is wrong because Document AI is for document processing.

Answer

Natural Language API

Answer

Document AI

Answer

Cloud Speech-to-Text

Question 11

An organization is using Vertex AI Gemini API for a multimodal chatbot. They notice that the model sometimes provides incorrect information with high confidence. They want to reduce hallucinations without retraining the model. What is the most effective approach?

Accepted Answer

Provide ground-truth context from a knowledge base using grounding. Safety settings reduce hallucinations? Actually, hallucinations are not purely safety. Grounding with a knowledge base provides factual retrieval to reduce hallucinations. Option B is wrong because safety settings block harmful content, not necessarily hallucinations. Option C is wrong because increasing temperature increases randomness. Option D is wrong because reduced token count limits responses but may not reduce hallucinations.

Answer

Increase the temperature parameter to make the model more creative

Answer

Reduce the maximum output tokens to force concise answers

Answer

Adjust safety settings to filter uncertain responses

Question 12

A retailer wants to use generative AI to write product descriptions automatically. They have a large dataset of existing product descriptions and need to customize a foundation model for their brand voice. Which Vertex AI feature should they use?

Accepted Answer

Vertex AI custom model tuning. Tuning (like supervised fine-tuning) allows customizing a pretrained model with your own data. Option A is wrong because prompt design is a technique, not a Vertex AI feature. Option B is wrong because Vertex AI Search is for search, not text generation customization. Option D is wrong because Model Evaluation assesses performance, not customization.

Answer

Vertex AI Search with grounding

Answer

Prompt design with the Gemini API directly

Answer

Vertex AI Model Evaluation

Question 13

A company deploys a fine-tuned text generation model on Vertex AI Endpoints. They want to monitor for data drift and performance degradation over time. Which GCP service should they integrate?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is specifically designed for drift detection and performance monitoring of deployed models. Option A is wrong because Cloud Monitoring is general infrastructure monitoring. Option B is wrong because Cloud Logging is for logs. Option D is wrong because Vertex AI Experiments is for tracking training runs.

Answer

Cloud Monitoring

Answer

Cloud Logging

Answer

Vertex AI Experiments

Question 14

A financial services firm is using Vertex AI to generate investment reports. They need to ensure that the model outputs are explainable and comply with regulatory requirements. Which Vertex AI feature should they use?

Accepted Answer

Vertex Explainable AI. Vertex Explainable AI provides feature importance and explanations for model predictions. Option A is wrong because Model Registry is for version management. Option B is wrong because Safety Settings block harmful content but don't provide explanations. Option D is wrong because AutoML is for model training, not explainability.

Answer

Vertex AI Model Registry

Answer

Vertex AI Safety Settings

Answer

Vertex AI AutoML

Question 15

A developer wants to integrate Gemini multimodal capabilities (text + image) into a mobile app using Python. Which Google Cloud client library should they use?

Accepted Answer

Vertex AI client library (google-cloud-aiplatform). The Google Generative AI client library (or Vertex AI client library for Gemini API) supports multimodal inputs. Option A is wrong because Cloud Vision is for image analysis, not generative. Option B is wrong because Natural Language is text-only. Option D is wrong because Dialogflow is for conversational agents, not direct API calls.

Answer

Dialogflow CX

Answer

Cloud Vision API

Answer

Natural Language API