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

Question 1

Which TWO of the following are best practices for configuring safety settings in Vertex AI generative models? (Choose 2)

Accepted Answer

Adjust safety thresholds based on the specific use case and audience.. Safety thresholds in Vertex AI should be calibrated per use case and audience to balance content safety with utility. For example, a medical chatbot may require stricter thresholds than a creative writing tool, and Vertex AI's safety settings allow adjusting thresholds for categories like hate speech or harassment to match specific risk tolerances. Option C is correct because the Vertex AI Safety API allows programmatic review of generated content, enabling automated safety checks beyond initial model settings, which is a best practice for robust safety configuration.

Answer

Disable safety filters for maximum creativity.

Answer

Apply the same safety settings to all models in the organization.

Answer

Always use the maximum safety threshold to block all potentially harmful content.

Question 2

A financial services company is building a customer-facing chatbot using Vertex AI Gemini API to answer questions about account balances, transactions, and branch locations. The chatbot must adhere to strict data privacy regulations (e.g., GDPR) that prohibit sending personally identifiable information (PII) to the model provider. The architecture uses a retrieval-augmented generation (RAG) approach where customer queries are passed to a Cloud Run service, which queries a BigQuery database for relevant data and then sends the context along with the query to the Gemini API. The team is concerned that the context may contain PII. They want to minimize modifications to the existing architecture. What step should the team take to ensure compliance?

Accepted Answer

Configure the Gemini API to disable data logging and use the Enterprise tier that ensures data stays within Google Cloud's controls.. Configuring the Gemini API to disable data logging and using the Enterprise tier ensures that data stays within Google Cloud's controls, addressing GDPR concerns without major architectural changes. Option A (anonymization pipeline) is possible but would require significant modifications. Option B (rejecting queries with PII) is unreliable and would break functionality. Option C (third-party proxy) adds complexity and potential compliance risks.

Answer

Build a separate anonymization pipeline using Cloud Data Loss Prevention to remove PII before sending context.

Answer

Modify the chatbot to reject any query that might contain PII based on regex patterns.

Answer

Route all requests through a third-party proxy that strips PII before sending to Gemini.

Question 3

A healthcare startup is using Vertex AI Imagen to generate synthetic medical images for training a diagnostic model. The images must comply with HIPAA regulations and cannot contain any real patient data. The team fine-tuned Imagen on a dataset of de-identified medical scans. However, during testing, they notice that some generated images closely resemble specific patients from the original dataset, even though the dataset was de-identified. They suspect that the model memorized some training examples. The team needs to address this issue without losing image quality. They have access to the original training data and Vertex AI tools. What action should they take?

Accepted Answer

Re-tune the model using differential privacy (DP-SGD) to prevent memorization of individual examples.. Differential privacy (DP-SGD) during fine-tuning adds calibrated noise to the gradient updates, which mathematically bounds the model's ability to memorize any single training example. This directly addresses the memorization issue while preserving the utility of the generated images, as the noise is carefully controlled to maintain overall image quality. Vertex AI supports DP-SGD through its custom training infrastructure, making it a practical choice for HIPAA-compliant medical imaging.

Answer

Use a post-processing step to blur or distort generated images.

Answer

Increase the size of the training dataset by adding more synthetic images.

Answer

Apply stricter output safety filters to block images that look like any known patient.

Question 4

A gaming company is using Vertex AI Imagen to create concept art. They have a stable pipeline that generates images based on text prompts. Recently, they introduced a new feature: using a reference image to guide the style (image-to-image generation). However, when using a reference image, the generated images often have unnatural color shifts and artifacts. The team suspects that the reference image is being resized to a resolution that the model wasn't trained on. They are using the default Imagen settings. What is the most likely cause and the best solution?

Accepted Answer

The reference image is being resized to a non-standard aspect ratio; preprocess the image to the recommended resolution and aspect ratio.. The default Imagen settings expect input images at specific resolutions (e.g., 256x256, 512x512, or 1024x1024) and a 1:1 aspect ratio. When a reference image is resized to a non-standard resolution or aspect ratio, the model's internal processing can introduce artifacts and unnatural color shifts due to misalignment with its training distribution. Preprocessing the image to the recommended resolution and aspect ratio ensures the model operates within its optimal input space, eliminating these issues.

Answer

Increase the number of inference steps to improve detail.

Answer

Reduce the style weight in the image-to-image prompt.

Answer

Switch to a different image generation model.

Question 5

A healthcare company is building a chatbot to answer patient queries based on their medical documents stored in Cloud Storage. They want to minimize latency and ensure data residency in the EU. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Search with document grounding. Vertex AI Search with document grounding is correct because it allows the chatbot to ground responses in the customer's own medical documents stored in Cloud Storage, ensuring low latency through optimized indexing and retrieval, while supporting data residency controls to keep data within the EU. This service is specifically designed for enterprise search and Q&A over private document repositories, making it ideal for healthcare use cases requiring compliance and fast responses.

Answer

Vertex AI Model Garden with fine-tuning

Answer

Vertex AI Agent Builder with web search

Answer

Vertex AI Codey APIs

Question 6

A startup wants to generate product descriptions from a few keywords using a large language model. They have no prior ML experience and need the fastest time-to-market. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Studio. Vertex AI Studio provides a no-code/low-code environment with pre-trained foundation models and prompt templates, enabling rapid generation of product descriptions from keywords without any ML expertise. It offers the fastest time-to-market because it eliminates the need for custom model training, infrastructure setup, or coding, directly leveraging Google's generative AI capabilities through a simple interface.

Answer

Vertex AI Workbench with custom training

Answer

Vertex AI Agent Builder

Answer

Vertex AI Model Garden

Question 7

A financial services firm uses a fine-tuned Gemini model in Vertex AI for regulatory compliance checks. They notice that token usage is high, increasing costs. They want to reduce costs without sacrificing accuracy. Which approach should they take?

Accepted Answer

Set max output tokens to a lower value and use more precise prompts. Reducing max output tokens directly lowers the number of tokens generated per request, which is the primary cost driver in pay-per-token models like Gemini. Using more precise prompts further reduces token waste by guiding the model to produce concise, relevant outputs without sacrificing accuracy, as compliance checks often require specific, structured responses rather than verbose explanations.

Answer

Switch to a smaller base model like PaLM 2 Bison

Answer

Enable context caching to reuse previous responses

Answer

Reduce temperature to 0.0

Question 8

A retail company wants to build a customer service chatbot that can handle returns, order status, and FAQs. They need to integrate with their existing backend systems. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Agent Builder. Vertex AI Agent Builder is the correct choice because it provides a low-code platform specifically designed for building conversational AI agents (chatbots) that can be integrated with enterprise backend systems via APIs, connectors, and custom tools. It supports grounding in enterprise data, multi-turn dialogue management, and seamless integration with existing systems for handling returns, order status, and FAQs, making it the most suitable service for this use case.

Answer

Vertex AI Model Garden

Answer

Vertex AI Search

Answer

Vertex AI Codey API

Question 9

A media company uses Vertex AI to generate video captions. The generated captions sometimes contain factual errors about named entities (e.g., actor names). Which technique would most likely reduce these errors?

Accepted Answer

Use Vertex AI grounding with a knowledge base of verified entities. Vertex AI grounding connects the model to a knowledge base of verified entities, allowing it to retrieve authoritative facts during generation. This reduces hallucinations about named entities by constraining outputs to validated data rather than relying solely on the model's parametric knowledge.

Answer

Enable response caching

Answer

Increase the temperature parameter

Answer

Decrease top_p to 0.3

Question 10

A company is using Vertex AI Gemini API to analyze customer feedback. They notice that the model occasionally generates offensive content. They have already set safety settings to block high-probability harmful content. What additional step should they take to further reduce offensive outputs?

Accepted Answer

Adjust safety settings to block medium-probability harmful content. The company has already blocked high-probability harmful content, but offensive outputs can still occur at lower probability thresholds. By adjusting safety settings to block medium-probability harmful content, they tighten the filter to catch more borderline cases without requiring model retraining or sacrificing output diversity. This leverages Vertex AI's configurable safety filters, which operate on likelihood categories (e.g., high, medium, low) rather than just binary blocking.

Answer

Set the temperature to 0.0

Answer

Enable context caching

Answer

Fine-tune the model on customer feedback data

Question 11

A global e-commerce company wants to translate product descriptions into 50 languages with high accuracy. They need to handle domain-specific terms (e.g., 'size chart', 'return policy'). Which approach should they use?

Accepted Answer

Use Vertex AI Translation with custom glossaries. Vertex AI Translation with custom glossaries is specifically designed for high-accuracy, domain-specific translations. Custom glossaries allow you to define precise translations for terms like 'size chart' and 'return policy', ensuring consistency across 50 languages. This approach leverages Google's neural machine translation models while overriding generic translations with your business-specific terminology.

Answer

Use the Gemini API with a prompt like 'Translate to French'

Answer

Build a custom agent with Vertex AI Agent Builder

Answer

Use Imagen to generate translated images

Question 12

Which TWO options are benefits of using Vertex AI Model Garden compared to using raw pre-trained models from external sources? (Choose two.)

Accepted Answer

Integration with Vertex AI tools like evaluation and monitoring. Vertex AI Model Garden is deeply integrated with the Vertex AI ecosystem, providing native access to tools like Vertex AI Evaluation for model performance assessment and Vertex AI Monitoring for drift detection and observability. This integration eliminates the need for custom pipelines to connect external models with these managed services, streamlining the MLOps workflow.

Answer

Lower cost compared to using generic APIs

Answer

Ability to fine-tune models on custom data

Answer

Guaranteed data privacy and no data sharing

Question 13

Which THREE factors should be considered when choosing between Gemini 1.5 Pro and Gemini 1.5 Flash for a customer-facing chatbot? (Choose three.)

Accepted Answer

Cost constraints: Flash is more cost-effective per token. Gemini 1.5 Flash is designed as a cost-optimized model, offering significantly lower per-token pricing compared to Gemini 1.5 Pro. For a customer-facing chatbot with high query volumes, cost efficiency is a primary consideration, making Flash the more economical choice for routine interactions.

Answer

Safety filters: Pro has stricter safety defaults

Answer

Multimodal capability: Flash does not support image input

Question 14

Which TWO features are available in Vertex AI Studio for prompt engineering? (Choose two.)

Accepted Answer

Side-by-side comparison of model outputs. Vertex AI Studio provides a side-by-side comparison feature that allows prompt engineers to evaluate outputs from multiple model configurations or parameter settings simultaneously. This enables direct visual comparison of responses, helping to identify the most effective prompt phrasing or parameter combination without manual switching.

Answer

One-click deployment to a Vertex AI endpoint

Answer

Fine-tuning models directly in the interface

Answer

Building conversational agents with drag-and-drop

Question 15

What is the most likely cause of the error?

Accepted Answer

The predict schema must be stored in the same bucket as the model artifacts and referenced without the full gs:// URI. The error occurs because the Vertex AI Predict schema must be stored in the same Cloud Storage bucket as the model artifacts, and when referenced in the model upload request, it should use a relative path (without the full `gs://` URI). Using the full URI causes a parsing failure, as Vertex AI expects the schema to be co-located with the model artifacts for validation and deployment.

Answer

The display name contains a hyphen which is not allowed

Answer

The container image URI is incorrect

Answer

The region us-central1 does not support TensorFlow models