Generative AI Leader Practice Test 46 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Vertex AI Imagen. Vertex AI Imagen is Google Cloud's managed generative AI service specifically designed for text-to-image generation. It requires minimal coding, as users can interact with it via the Cloud Console, API calls with simple prompts, or through Vertex AI's built-in tools, making it ideal for a startup needing to generate marketing images from text descriptions without extensive development effort.

Answer

Natural Language API

Answer

Document AI

Answer

Cloud Speech-to-Text

Question 2

A multinational corporation deploys a generative AI chatbot across multiple regions. They need to comply with GDPR and local data residency requirements. Which THREE actions are necessary?

Accepted Answer

Store and process data only in approved geographic regions (data residency controls). To comply with GDPR and data residency, the company must encrypt personal data, store data in specific regions, and control retention of prompts/responses. Consent management is part of GDPR but not specifically about data residency. Anonymization is one approach but not a universal requirement.

Answer

Obtain explicit consent from every user before collecting any data

Answer

Anonymize all training data before fine-tuning

Question 3

A developer needs to use the Vertex AI PaLM API to generate text embeddings for a large corpus of documents. Which model should they use?

Accepted Answer

textembedding-gecko@001. `textembedding-gecko@001` is the specific Vertex AI model designed for generating text embeddings, which convert text into dense vector representations. This model is optimized for semantic similarity, clustering, and retrieval tasks, making it ideal for processing a large corpus of documents. The other models are designed for code generation, text generation, or chat, not embeddings.

Answer

codey-bison@001

Answer

text-bison@001

Answer

chat-bison@001

Question 4

A company is deploying a generative AI model for customer support. They want to reduce hallucinations while maintaining fluency. They have a large dataset of previous support conversations. Which strategy should they prioritize?

Accepted Answer

Implement retrieval-augmented generation (RAG) using the conversation dataset as a knowledge base.. Retrieval-augmented generation (RAG) directly addresses hallucinations by grounding the model's responses in factual, retrieved data from the conversation dataset. This approach allows the model to generate fluent, contextually relevant answers while reducing the risk of inventing information, as it retrieves actual support interactions as evidence before generating a response.

Answer

Increase the beam search width to 10.

Answer

Fine-tune the model on the conversation dataset.

Answer

Set the temperature to 0.1.

Question 5

Which THREE of the following are features of Vertex AI Studio (Gen AI Studio)? (Choose 3)

Accepted Answer

Compare responses from different models side-by-side.. Vertex AI Studio (formerly Gen AI Studio) provides a built-in interface that allows users to run the same prompt against multiple foundation models simultaneously, enabling direct side-by-side comparison of outputs to evaluate quality, tone, and accuracy before selecting a model for deployment.

Answer

Configure pre-built safety filters for generated content.

Answer

Deploy custom container images to Vertex AI endpoints.

Question 6

A startup wants to quickly prototype a conversational AI application using Gemini. They need free access during development and do not require VPC controls. Which access tier should they choose?

Accepted Answer

Google AI Studio free tier. Google AI Studio's free tier provides free access to Gemini models for rapid prototyping without requiring VPC controls or any billing setup. This aligns directly with the startup's need for quick, cost-free development iteration before moving to production.

Answer

Vertex AI on a pay-as-you-go basis

Answer

Vertex AI with VPC-SC

Answer

Gemini API via Cloud Run

Question 7

Which THREE factors should be considered when choosing between a fine-tuned model and a prompted foundation model for a generative AI solution? (Select 3)

Accepted Answer

Need for domain-specific vocabulary. Fine-tuning allows the model to learn domain-specific vocabulary and terminology that may not be well-represented in the foundation model's pre-training data. This is critical for specialized fields like legal, medical, or technical domains where precise language is required for accurate outputs.

Answer

Inference latency requirements

Answer

Whether the model is open-source

Question 8

A developer needs to generate embeddings for text data to be used in a semantic search application. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Embeddings API. Vertex AI Embeddings API is the correct choice because it provides a managed service to generate vector embeddings from text data, which are essential for semantic search applications that rely on understanding meaning rather than exact keyword matches. This API leverages large language models to convert text into high-dimensional vectors, enabling efficient similarity search using vector databases or nearest neighbor algorithms.

Answer

Document AI

Answer

Cloud Translation API

Answer

Cloud Speech-to-Text

Question 9

Which THREE strategies should be combined to effectively reduce biased outputs in a generative AI model? (Choose three.)

Accepted Answer

Implement safety filters targeting hate speech and stereotypes.. Implementing safety filters targeting hate speech and stereotypes directly blocks the generation of biased or harmful content at the output layer. These filters use predefined rule sets or trained classifiers to detect and suppress language that reflects demographic or cultural biases, reducing the risk of the model producing offensive or stereotypical responses.

Answer

Raise the temperature to increase output variability.

Answer

Fine-tune the model on a biased dataset to learn patterns.

Question 10

A large enterprise is migrating their on-premise ML workloads to Vertex AI. They have a custom PyTorch model for text classification that they want to serve with minimal code changes. Which Vertex AI capability should they use for model serving?

Accepted Answer

Vertex AI Prediction with a custom container. Vertex AI Prediction with a custom container allows the enterprise to package their existing PyTorch model with any custom dependencies or runtime configurations into a Docker container, enabling deployment with minimal code changes. This approach provides full control over the serving environment while leveraging Vertex AI's managed infrastructure for scaling, monitoring, and endpoint management.

Answer

Vertex AI Endpoints with a pre-built PyTorch runtime

Answer

Vertex AI Model Garden

Answer

Vertex AI Vector Search for approximate nearest neighbor

Question 11

A machine learning engineer wants to convert text into numerical vectors for similarity search. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Embeddings API. The Vertex AI Embeddings API is the correct choice because it is specifically designed to convert text (and other data types) into dense numerical vectors (embeddings) that capture semantic meaning. These embeddings are the fundamental input for similarity search, enabling efficient comparison of text based on conceptual closeness rather than exact keyword matching.

Answer

Natural Language API

Answer

Vector Search

Answer

Gemini API

Question 12

A developer is using Gemini 1.5 Pro and needs to process a 2-hour video to answer questions about its content. The video is stored in Cloud Storage. What is the most efficient approach?

Accepted Answer

Send the video file as part of the prompt to Gemini 1.5 Pro. Gemini 1.5 Pro supports video input natively; you can pass the video directly (via GCS URI) and ask questions. Transcribing first adds latency and loses visual context.

Answer

Extract frames using Video Intelligence API and then send them as images to Gemini

Answer

Transcribe the video using Chirp, then analyze the text with Gemini

Answer

Use a custom model fine-tuned on video understanding tasks

Question 13

A global financial services firm wants to deploy generative AI for personalized investment recommendations. They must comply with regulations in multiple jurisdictions, including GDPR and the SEC's Marketing Rule. The solution must also be auditable. Which approach best balances regulatory compliance, scalability, and cost?

Accepted Answer

Deploy separate, jurisdiction-specific models with tailored guardrails and audit trails for each region.. Deploying separate, jurisdiction-specific models allows each model to be trained and governed with guardrails and audit trails that directly map to local regulations like GDPR (data minimization, right to erasure) and the SEC Marketing Rule (fair, clear, and not misleading disclosures). This approach avoids the compliance conflicts that arise when a single model must satisfy contradictory requirements across regions, and it scales cost-effectively by only applying the necessary compliance overhead to each region's data and inference pipeline.

Answer

Build a centralized model in a cloud region with the most stringent regulations and apply it globally.

Answer

Use a single global model with a unified compliance layer applied post-generation.

Answer

Rely on a third-party API with built-in compliance for all regions.

Question 14

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 15

A team uses Vertex AI to host a large language model. They want to reduce latency for real-time applications. What is the best strategy?

Accepted Answer

Use model quantization. Model quantization reduces the precision of the model's weights (e.g., from FP32 to INT8), which decreases memory footprint and computational requirements, directly lowering inference latency for real-time applications on Vertex AI. This is a standard optimization technique for deploying large language models with minimal accuracy loss while meeting latency SLAs.

Answer

Increase number of replicas

Answer

Switch to a smaller model

Answer

Use batch prediction instead of online