Generative AI Leader Google Cloud's Generative AI Offerings Practice Test 7 — 15 Questions

Question 1

A developer is configuring a Vertex AI Agent Builder agent to use grounding. They receive the following error when calling the API: `404 Not Found: Data store resource not found.` What is the most likely cause?

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

Accepted Answer

The data store has not been created in the specified project. The error message indicates that the data store resource cannot be found in the specified Google Cloud project. Vertex AI Agent Builder requires the data store to exist and be fully provisioned in the same project as the agent before grounding can be configured. If the data store ID or project ID is incorrect, or the data store has not been created, the API will return a 'not found' error.

Answer

The data store was just created and is not yet propagated

Answer

The agent is not authenticated to access the data store

Answer

The grounding configuration is missing required permissions

Question 2

A data scientist is using the Vertex AI PaLM API for text generation. They notice that the model occasionally generates toxic content. Which parameter should they adjust to reduce the likelihood of toxic outputs?

Accepted Answer

safety_settings. Safety settings in the Vertex AI PaLM API allow you to configure thresholds for filtering harmful content categories (e.g., toxicity, harassment, hate speech). By adjusting these settings, you can block or reduce the likelihood of toxic outputs before they are returned, directly addressing the problem without altering the model's creativity or randomness.

Answer

max_output_tokens

Answer

temperature

Answer

top_k

Question 3

An organization is deploying a summarization model on Vertex AI and needs to ensure that the model's responses are consistent and avoid hallucinations. They have a labeled dataset of source documents and human-written summaries. Which approach would best align the model with their quality requirements?

Accepted Answer

Perform supervised fine-tuning using their labeled dataset. Supervised fine-tuning (D) directly optimizes the model's weights using the labeled dataset of source documents and human-written summaries, which teaches the model to produce consistent, factual outputs and reduces hallucinations. This approach aligns the model's behavior with the specific quality requirements by learning from ground-truth examples, unlike prompt engineering or parameter adjustments that do not modify the underlying model.

Answer

Deploy the model with a larger max_output_tokens

Answer

Use prompt engineering with few-shot examples

Answer

Increase the temperature to 0.9

Question 4

A developer is using the Vertex AI PaLM API and receives a 429 Resource Exhausted error. What is the most likely cause?

Accepted Answer

The user has exceeded the allowed number of requests per minute. HTTP 429 Resource Exhausted is the standard error returned when a client exceeds the rate limits or quota assigned to the API — most commonly the requests-per-minute (RPM) limit. In the Vertex AI PaLM API, each project has per-minute quotas for prediction requests, and exceeding them triggers this error. The correct remediation is to implement exponential backoff and/or request a quota increase.

Answer

The request payload is too large

Answer

The model is not available in the current region

Answer

The API key is invalid

Question 5

A company is using Vertex AI Model Registry to manage multiple versions of its custom generative model. They want to automatically route a percentage of traffic to a new model version for testing. What should they do?

Accepted Answer

Deploy both versions to the same endpoint and adjust traffic split settings. Vertex AI Endpoints support traffic splitting between model versions.

Answer

Set up a Cloud Tasks queue to distribute requests

Answer

Create a new endpoint for each version

Answer

Use a load balancer in front of the endpoints

Question 6

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 7

A machine learning engineer is deploying a large generative model on Vertex AI. The model requires a GPU with high memory. Which machine configuration should they choose?

Accepted Answer

a2-highgpu-4g with 4 A100 GPUs. The a2-highgpu-4g machine series is specifically designed for large-scale GPU-accelerated workloads, offering 4 NVIDIA A100 GPUs with 40GB of high-bandwidth memory (HBM2e) each, totaling 160GB of GPU memory. This configuration provides the high memory capacity required for training or serving large generative models, such as LLMs or diffusion models, which often exceed the memory limits of smaller GPUs.

Answer

c2-standard-16 with no GPU

Answer

n1-standard-4 with a single T4 GPU

Answer

n2-standard-8 with a single P4 GPU

Question 8

A developer is using the Vertex AI PaLM API to generate code. They want to ensure the output is safe and adheres to company policies. Which THREE attributes can they configure in the safety_settings parameter?

Accepted Answer

Toxicity. The safety_settings parameter in the Vertex AI PaLM API accepts a list of safety categories, each with a configurable threshold, and the supported categories include Toxicity (C), Harassment (D), and Sexually explicit content (E), so these three are the attributes the developer can configure to filter unsafe output and enforce company policies. Toxicity (C) lets them block content that is rude, disrespectful, or otherwise harmful, Harassment (D) targets content that bullies or intimidates individuals or groups, and Sexually explicit content (E) filters sexually explicit material; each is a distinct safety category with its own threshold setting. Language detection (A) is not a safety category but a separate text-analysis capability, and sentiment analysis (B) is likewise an analytical feature rather than a configurable safety attribute, so neither belongs in safety_settings.

Answer

Language detection

Answer

Sentiment analysis

Question 9

The exhibit shows the output of describing a model on Vertex AI. What does 'modelSource: MODEL_GARDEN' indicate about this model?

Accepted Answer

The model was imported from the Vertex AI Model Garden.. 'modelSource: MODEL_GARDEN' explicitly indicates that the model was sourced from Vertex AI Model Garden, which is a curated repository of pre-built and pre-trained foundation models. This field is set when a model is imported from Model Garden, not when it is trained or fine-tuned from scratch within Vertex AI.

Answer

The model was trained on Vertex AI from scratch.

Answer

The model has been exported to Model Garden.

Answer

The model was fine-tuned using AutoML.

Question 10

A model response deployed on Vertex AI includes safety attributes with a toxicity score of 0.9 and an insult score of 0.3. The application must reject any prediction where the toxicity score exceeds 0.8. Based on the response, what action should the application take?

Accepted Answer

Reject the prediction because the toxicity score exceeds 0.8.. The response from the model includes a safety attribute with a toxicity score of 0.9, which exceeds the application's threshold of 0.8. Vertex AI safety attributes provide scores for categories like toxicity, insult, and sexual content, and the application must enforce its own rejection logic based on these scores. Since the toxicity score is above the defined threshold, the application should reject the prediction to comply with safety policies.

Answer

Retry the request with a lower temperature.

Answer

Display the prediction because the insult score is below 0.8.

Answer

Log the prediction but still display it.

Question 11

A startup wants to quickly integrate a generative AI chatbot into their customer support platform. They need a solution that can answer questions based on their internal knowledge base with minimal setup. Which Google Cloud service should they use?

Accepted Answer

Use Vertex AI Agent Builder to create a conversational agent grounded in their data. Vertex AI Agent Builder (now part of Vertex AI Agent Platform) provides a low-code, out-of-the-box solution for building conversational agents that are grounded in enterprise data. It automatically handles retrieval-augmented generation (RAG) by indexing the startup's internal knowledge base into a vector store and orchestrating the LLM to answer questions using only that data, requiring minimal setup compared to manual integration or fine-tuning.

Answer

Use Model Garden to deploy a pre-built Q&A model

Answer

Call the Gemini API directly and implement grounding logic manually

Answer

Use Cloud AI Notebooks to fine-tune a model on their knowledge base

Question 12

A company is building a document summarization tool using Vertex AI Gemini API. They notice that the model sometimes returns incomplete summaries that miss key points. Which approach is most likely to improve summary quality without increasing token usage significantly?

Accepted Answer

Refine the system instruction to specify the desired summary format and key elements to include. Refining the system instruction directly addresses the root cause of incomplete summaries by providing explicit guidance on the desired output format and key elements to include. This approach improves the model's adherence to the task without increasing the number of input or output tokens, as it only modifies the instruction text, not the document length or generation limits.

Answer

Increase the context window to include more of the document

Answer

Switch to a larger Gemini model (e.g., from 1.0 Pro to 1.5 Pro)

Answer

Increase the max output token limit to allow longer summaries

Question 13

An engineer is testing a generative AI application using the Gemini API. They receive a 400 error with message 'INVALID_ARGUMENT: text has been blocked.' What is the most likely cause?

Accepted Answer

The input text was flagged by a safety filter. The 400 error with 'INVALID_ARGUMENT: text has been blocked' indicates that the input text was rejected by Gemini's safety filters before any model processing occurred. These filters evaluate content against categories like hate speech, harassment, or sexually explicit material, and when triggered, they block the request at the API gateway level, returning this specific error message.

Answer

The specified Gemini model version does not exist

Answer

The API key is invalid

Answer

The API quota has been exceeded

Question 14

A retailer is building a product recommendation chatbot using Vertex AI Agent Builder. They want the agent to answer questions about product availability, prices, and promotions, but also to escalate to a human agent when the query is complex. What should they configure in Agent Builder?

Accepted Answer

Define an agent with a 'handoff to human' intent and configure the corresponding flow. In Vertex AI Agent Builder, you define intents to handle specific user requests. A 'handoff to human' intent, when triggered, initiates a configured flow that transfers the conversation to a human agent. This is the standard method for escalation. Option A is incorrect because playbooks define conversation steps but not directly the escalation trigger; they can be part of a flow but not the primary escalation mechanism. Option C is incorrect: while you could integrate a tool to call a human support API, Agent Builder provides a built-in handoff intent and flow, making custom tool integration unnecessary. Option D is incorrect: generative fallback only generates responses when the agent cannot match a query, it does not provide a structured escalation to a human.

Answer

Create a playbook with a step that transfers to a human via a webhook

Answer

Integrate a tool that calls a human support API when confidence is low

Answer

Use Vertex AI Agent Builder's generative fallback to automatically escalate

Question 15

A developer wants to use Gemini 1.5 Pro to analyze hour-long video content and generate a summary. Which feature of Gemini 1.5 Pro is most suitable for this task?

Accepted Answer

Long context window (up to 1 million tokens). Gemini 1.5 Pro's long context window of up to 1 million tokens allows it to process and analyze hour-long video content in a single pass, including both audio and visual frames. This capability is essential for generating a coherent summary of long-form video, as it can retain and reason over the entire video's context without needing to chunk or downsample the content.

Answer

Multimodal generation from text and images

Answer

Code generation and debugging

Answer

Function calling to retrieve external data