Generative AI Leader Google Cloud's Generative AI Offerings Practice Test 5 — 15 Questions

Question 1

You are a generative AI architect for a large e-commerce company. Your team has built a product description generator using Vertex AI's text-bison model. The model is accessed via the Vertex AI API from a web application. You have set the temperature to 0.5 and top_k to 40. The team reports that the generated descriptions are often too generic and lack creativity. They want the descriptions to be more diverse and engaging. You are also concerned about cost, as each API call is billed. Which change should you recommend to increase creativity while managing cost?

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

Accepted Answer

Increase the temperature to 0.8 and keep top_k at 40.. Increasing the temperature to 0.8 makes the model's output probability distribution flatter, which increases randomness and allows less likely tokens to be selected. This directly addresses the need for more diverse and creative descriptions. Keeping top_k at 40 ensures the model still considers a broad set of candidate tokens, balancing creativity with coherence, and does not increase API call costs since temperature and top_k are inference parameters that do not affect billing.

Answer

Keep temperature at 0.5 but reduce top_k to 20.

Answer

Switch to a larger model like text-bison@002 and keep same parameters.

Answer

Decrease the temperature to 0.2 and increase top_k to 60.

Question 2

A global e-commerce company is using Vertex AI to build a generative AI chatbot for customer support. The chatbot is powered by the Gemini 1.5 Pro model and uses a vector search index for retrieval-augmented generation (RAG) over product documentation. The company has deployed the application in four regions (us-central1, europe-west4, asia-east1, and australia-southeast1) using a multi-region deployment with a global endpoint. The application is critical and requires high availability with a target latency of under 500ms for the RAG pipeline. Recently, users in Australia are experiencing inconsistent latency spikes, with response times exceeding 2 seconds during peak hours. The team suspects that the issue is related to the vector search index's replication and serving configuration. The index has 10 million embeddings with a dimension of 768. It is stored in a single regional bucket in us-central1, and the vector search index endpoint is deployed in all four regions with the same deployed index ID. The team is using the default configuration for index updates and serving. Which action should the team take to resolve the latency issue for Australian users?

Accepted Answer

Create a new regional bucket in australia-southeast1 and store a copy of the index there, then redeploy the vector search index endpoint to use the local bucket.. The latency issue for Australian users is likely caused by the vector search index serving from a single regional bucket in us-central1. When users in Australia query the index, the nearest endpoint must read the index data from a bucket far away, leading to high latency during peak hours. Option B solves this by creating a local bucket in australia-southeast1 and storing a copy of the index there. The endpoint in australia-southeast1 can then serve directly from the local bucket, reducing read latency. Moving to a multi-regional bucket (Option A) does not help because the bucket is still in the US, and Australia still has to fetch from a distant region. Option C would also work but is more complex and costlier than simply using a local bucket with the same endpoint. Option D addresses throughput but not the geographic distance causing latency.

Answer

Move the vector search index to a multi-regional Cloud Storage bucket (e.g., 'us') to reduce latency for index updates.

Answer

Deploy a separate vector search index endpoint for each region with its own index copy stored in a regional bucket in that region.

Answer

Increase the number of replicas for the vector search index in all regions to improve throughput and reduce latency.

Question 3

A company is building a customer support chatbot using Vertex AI Agent Builder. They want the agent to answer questions based on their internal knowledge base. Which feature should they use?

Accepted Answer

Grounding with enterprise data stores. Vertex AI Agent Builder supports grounding with enterprise data stores, which allows the agent to retrieve and answer questions based on the company's internal knowledge base (e.g., documents, PDFs, websites) without relying on public web search. This ensures responses are grounded in proprietary, controlled data, making it the correct choice for a customer support chatbot that needs to reference internal policies or product documentation.

Answer

Grounding with Google Search

Answer

Model tuning

Answer

Prompt engineering

Question 4

A company is using Gemini Pro for code generation. They want to ensure that the generated code does not contain security vulnerabilities. Which approach should they implement?

Accepted Answer

Implement a human-in-the-loop review with automated scanning. Combining human-in-the-loop review with automated scanning directly addresses the need to catch security vulnerabilities in AI-generated code. Human reviewers can identify logic flaws and context-specific risks that automated tools miss, while automated scanners provide consistent, rapid detection of known vulnerability patterns (e.g., OWASP Top 10). This layered approach is a best practice for production-grade code generation with Gemini Pro, as it mitigates the inherent limitations of relying solely on AI safety filters or static analysis.

Answer

Enable grounding with security scanning tools

Answer

Use the Vertex AI Codey API with safety settings

Answer

Use a custom safety attribute filter

Question 5

You want to use a Google foundation model to generate text summaries of news articles. Which Vertex AI service should you use?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio (now part of Vertex AI Agent Builder) provides a no-code/low-code environment to access, test, and tune Google's foundation models, including PaLM 2 and Gemini, specifically for generative tasks like text summarization. It offers built-in prompt templates and safety settings tailored for summarization use cases, making it the correct service for this task.

Answer

Vertex AI Prediction

Answer

Vertex AI Model Registry

Answer

Vertex AI Feature Store

Question 6

A developer is using the Vertex AI Gemini API to generate product descriptions. They get a 400 error 'INVALID_ARGUMENT: The model's maximum input token limit is 8192.' What is the most likely issue?

Accepted Answer

The prompt is too long. The 400 error 'INVALID_ARGUMENT: The model's maximum input token limit is 8192' explicitly indicates that the combined token count of the prompt (system instructions, user input, and any conversation history) exceeds the 8192-token context window of the Gemini model being used. This is a hard limit enforced by the Vertex AI Gemini API, and the error is triggered before any generation begins. Therefore, the most likely issue is that the prompt is too long.

Answer

The API key is invalid

Answer

The output tokens are too high

Answer

The model is not available in the region

Question 7

A company is using Vertex AI Agent Builder to create a travel booking agent. They want the agent to book flights and hotels dynamically. What action type should they use?

Accepted Answer

Webhook. Vertex AI Agent Builder uses webhooks to integrate with external systems for dynamic, real-time operations like booking flights and hotels. A webhook allows the agent to make HTTP calls to external APIs (e.g., a travel booking service) to fetch or update data during a conversation, enabling dynamic booking actions. Static or notification actions cannot handle the two-way, real-time data exchange required for live reservations.

Answer

Dynamic call

Answer

Static call

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Notification

Question 8

Which TWO actions can reduce the cost of using Vertex AI Gemini API? (Choose two.)

Accepted Answer

Use batch prediction instead of online. Option A is correct because batch prediction processes many requests asynchronously in a single job and is priced at a discount (typically 50%) compared to online prediction, directly lowering per-request cost for workloads that tolerate latency. Option E is correct because context caching lets you store frequently reused input tokens (e.g., long system prompts or documents) and pay a reduced rate for cached tokens plus a small storage fee, cutting costs when the same context is sent repeatedly. Option B is wrong because increasing max output tokens generates more billable output tokens, raising cost. Option C is wrong because grounding with Google Search adds grounding charges and does not reduce API cost. Option D is wrong because larger models have higher per-token prices, increasing rather than reducing cost.

Answer

Increase the max output tokens

Answer

Use grounding with Google Search

Answer

Use a larger model

Question 9

Which THREE factors should you consider when selecting a foundation model from Model Garden? (Choose three.)

Accepted Answer

Model size. When selecting a foundation model from Model Garden, model size (C) matters because parameter count directly affects inference latency, throughput, memory footprint, and cost, so it must match your deployment and budget constraints. Model accuracy on benchmarks (D) is essential because benchmark results (e.g., MMLU, GSM8K, HELM scores) indicate how well the model performs on the tasks relevant to your use case. Model license (E) must be evaluated because it determines whether commercial use, redistribution, or fine-tuning is permitted, which directly impacts legal and business viability. The number of model versions (A) is not a primary selection factor since version count alone says nothing about capability, cost, or fit. The color of the model card (B) is purely cosmetic and has no bearing on model selection.

Answer

Number of model versions

Answer

The color of the model card

Question 10

Refer to the exhibit. This is the IAM policy for a project containing a Vertex AI Agent Builder agent and a data store. The agent is unable to access the data store. What is the most likely cause?

Accepted Answer

The agent service account needs the data store viewer role. The agent service account must have the Data Store Viewer role (or equivalent permissions) to read data from the data store. Without this role, the agent cannot access the indexed content, even if the user has permissions. This is a common IAM misconfiguration in Vertex AI Agent Builder.

Answer

The user needs more permissions

Answer

The agent needs a bigger quota

Answer

The data store is not in the same region

Question 11

A data scientist wants to quickly prototype a text generation application using Google's foundation models. Which Google Cloud service should they use?

Accepted Answer

Generative AI Studio. Generative AI Studio is the correct service because it provides a purpose-built environment for quickly prototyping and experimenting with Google's foundation models, including text generation models like PaLM 2 and Gemini. It offers a no-code interface and SDK access for rapid iteration, directly aligning with the data scientist's goal of fast prototyping without needing to manage infrastructure or training pipelines.

Answer

Cloud Natural Language API

Answer

Vertex AI Prediction

Answer

AI Platform Training

Question 12

An organization is using Vertex AI to fine-tune a large language model. They notice training is taking longer than expected and cost is increasing. Which action is most likely to reduce training time and cost without significantly impacting model quality?

Accepted Answer

Enable mixed-precision training (bfloat16). Mixed-precision training with bfloat16 reduces memory usage and accelerates computation by using half the bits of standard float32, which directly decreases training time and cost on TPUs and modern GPUs. Vertex AI supports bfloat16 natively on TPU v3+ and A100 GPUs, and for many large language models, this precision preserves model quality because bfloat16 retains the same exponent range as float32, avoiding underflow issues common with float16.

Answer

Increase the number of training steps

Answer

Increase the batch size

Answer

Use a higher learning rate

Question 13

A security team wants to prevent prompt injection attacks on their generative AI application hosted on Vertex AI. Which best practice should they implement?

Accepted Answer

Implement input validation and output filtering. Prompt injection attacks exploit the model's inability to distinguish between user instructions and untrusted input. Implementing input validation (e.g., sanitizing special characters or known injection patterns) and output filtering (e.g., using a classifier to detect and block malicious responses) directly mitigates this risk by controlling what the model processes and returns. On Vertex AI, this can be enforced via custom safety attributes or integration with services like Cloud DLP for data loss prevention.

Answer

Use a custom model instead of a foundation model

Answer

Disable all logging

Answer

Use a private endpoint

Question 14

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 15

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 is Google's family of models specifically designed for code generation, including converting natural language descriptions into code. It is built on the PaLM 2 architecture and is optimized for tasks like code completion, code generation, and code chat, making it the correct choice for generating code from natural language.

Answer

Chirp

Answer

Med-PaLM

Answer

Imagen