Generative AI Leader Google Cloud's Generative AI Offerings — 25 Questions

Question 1

An organization is using Vertex AI Agent Builder to create a customer service agent. They want the agent to be able to hand off to a human agent when it cannot answer a question. What should they configure in the agent's design?

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

Accepted Answer

Add an 'Escalation' intent that triggers a human handoff. In Vertex AI Agent Builder, the 'Escalation' intent is specifically designed to hand off a conversation to a human agent when the agent cannot resolve a query. Option A is incorrect because slot filling collects parameters but does not transfer to a human. Option B is incorrect because a confirmation prompt only verifies user intent, not escalate. Option D is incorrect because a fallback intent handles unrecognized inputs but does not inherently route to a human; it can be configured to do so, but the standard mechanism for human handoff is the escalation intent.

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Configure 'Slot filling' to collect more info

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Implement a 'Confirmation' prompt for the user

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Use a 'Fallback' intent to route to a human

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.

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

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

Question 3

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

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Use the Vertex AI Codey API with safety settings

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Use a custom safety attribute filter

Question 4

A team is deploying a real-time chat application using Gemini. They need to ensure the model does not generate harmful content. Which safety filter configuration should they use?

Accepted Answer

Implement custom safety attribute filters with low thresholds. Setting safety thresholds to low (e.g., BLOCK_LOW_AND_ABOVE) for all categories blocks even low-probability harmful content, ensuring strict safety in a real-time chat application. Option A is incorrect because a high threshold (e.g., BLOCK_ONLY_HIGH) only blocks content with high probability of being harmful, allowing potentially harmful content through. Option B is incorrect because grounding with a safe knowledge base limits the knowledge source but does not filter the model's generated responses. Option D is incorrect because fine-tuning with safe examples reduces but does not eliminate the need for active safety filtering during inference.

Answer

Set safety_threshold high for all categories

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Use grounding with a safe knowledge base

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Fine-tune the model with safe examples

Question 5

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

Accepted Answer

Use batch prediction instead of online. Batch prediction reduces cost because it processes multiple requests asynchronously in a single batch, allowing Vertex AI to optimize resource utilization and charge lower per-token rates compared to online (real-time) prediction, which requires dedicated infrastructure for low-latency responses.

Answer

Increase the max output tokens

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Use grounding with Google Search

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Use a larger model

Question 6

Which TWO components are essential for building a multi-turn conversational agent using Vertex AI Agent Builder? (Choose two.)

Accepted Answer

Dialogflow CX. Dialogflow CX is essential for building multi-turn conversational agents because it provides advanced state management, flow-based design, and natural language understanding (NLU) capabilities specifically optimized for complex, multi-turn interactions. Agent Builder Agent (the core agent component within Vertex AI Agent Builder) is the second essential component, as it serves as the container for the agent's configuration, knowledge bases, and integration settings, enabling the orchestration of conversations across channels.

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BigQuery

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Vertex AI Prediction

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Cloud Storage

Question 7

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

Accepted Answer

Model size. Model size (C) is a critical factor because it directly impacts computational requirements, latency, and cost. Larger models generally offer higher capability but require more memory and processing power, which influences deployment decisions on Vertex AI.

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Number of model versions

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The color of the model card

Question 8

Refer to the exhibit. You ran the gcloud command to list a model, but received this error. What is the most likely issue?

Accepted Answer

The model is missing a serving container. The error indicates that the model cannot be deployed because it lacks a serving container specification. In Vertex AI, models must have either a pre-built or custom serving container defined in the model resource to handle prediction requests. Without this, the gcloud command cannot list or deploy the model, making option C correct.

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The model's artifact URI is wrong

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The model wasn't uploaded correctly

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The model is in a different project

Question 9

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

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The agent needs a bigger quota

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The data store is not in the same region

Question 10

Refer to the exhibit. A developer has defined a dynamic action in the Vertex AI Agent Builder agent YAML. The agent is not triggering the action. What is the most likely issue?

Accepted Answer

The action name is misspelled. The most common reason a dynamic action fails to trigger in Vertex AI Agent Builder is a mismatch between the action name defined in the agent YAML and the name referenced in the agent's configuration or invocation logic. The YAML parser is case-sensitive and requires exact string matching; even a minor typo or capitalization error will cause the agent to ignore the action entirely, as it cannot resolve the reference to the defined action block.

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The endpoint returns a 4xx status

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The endpoint is not HTTPS

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The agent is not enabled

Question 11

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

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Increase the batch size

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Use a higher learning rate

Question 12

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 (AUP) is the authoritative document that defines prohibited uses of Google Cloud services, including generative AI offerings. It is regularly updated to reflect current legal, ethical, and security requirements, making it the most reliable source for the most up-to-date information on prohibited use cases. The AUP explicitly covers restrictions on generating harmful content, engaging in illegal activities, and violating intellectual property rights, which directly apply to generative AI services.

Answer

Google Cloud documentation

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Google's AI Principles

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The Gemini Terms of Service

Question 13

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. Data residency is a critical consideration because many enterprises have regulatory or compliance requirements that mandate their data (including model inputs, outputs, and fine-tuning data) must remain within specific geographic boundaries. Google Cloud's Vertex AI offers regionalized endpoints and dedicated model deployment options (e.g., Private Service Connect) to ensure data does not leave the chosen region, directly addressing sovereignty needs.

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Developer preference

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Model size

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Training time

Question 14

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 for sensitive outputs ensures oversight for edge cases and high-risk content, aligning with Google's AI Principles that emphasize human oversight for consequential decisions. Option C is correct because conducting fairness evaluations helps identify and mitigate biases in model outputs, which is a core responsible AI practice. Option D is correct because creating a safety policy and enforcing it via content filtering establishes automated safeguards to prevent harmful outputs. Options A and E are incorrect: disabling content filtering removes a critical safety layer, and using only Google's foundation models does not automatically ensure responsible AI practices—active implementation of safeguards is required.

Answer

Disable content filtering

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Use only Google's foundation models

Question 15

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. Top-k is a sampling parameter that restricts the model to consider only the k most likely next tokens, reducing randomness and focusing the output on higher-probability choices. This directly influences the diversity and coherence of generated text in the Gemini API.

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Color space

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Audio sample rate

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Image size

Question 16

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. Vertex AI Agent Builder uses grounding to connect the agent to external data sources, such as documents stored in Cloud Storage. By enabling grounding with a data store, the agent can retrieve and reference relevant information from the knowledge base documents in real time, ensuring accurate and context-aware responses without requiring model retraining.

Answer

Deploy the agent to a Vertex AI endpoint

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Fine-tune a Gemini model on the knowledge base

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Configure a safety filter to block irrelevant queries

Question 17

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. Vertex AI Model Registry supports continuous evaluation pipelines that automatically promote a model version to a 'default' or 'deployed' status only when it meets predefined evaluation thresholds (e.g., accuracy, F1 score). This ensures that only the version passing all metrics is eligible for deployment to a Vertex AI Endpoint, enabling automated, gated rollouts without manual intervention.

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

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Manually track version IDs and deploy the latest version

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Deploy all versions to a single endpoint and route traffic manually

Question 18

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. Grounding with a knowledge base is the most effective approach because it forces the model to base its responses on verified, external data rather than relying solely on its parametric knowledge. By providing ground-truth context via Vertex AI's grounding feature, the model can cross-reference its outputs with authoritative sources, significantly reducing the likelihood of hallucinations. This method directly addresses the root cause of incorrect high-confidence responses without requiring retraining.

Answer

Increase the temperature parameter to make the model more creative

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Reduce the maximum output tokens to force concise answers

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Adjust safety settings to filter uncertain responses

Question 19

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. Vertex AI custom model tuning (Option D) is correct because it allows the retailer to fine-tune a foundation model on their proprietary dataset of existing product descriptions, adapting the model's output to match their specific brand voice and style. This process adjusts the model's weights using supervised learning on the retailer's data, enabling personalized and consistent content generation that generic prompt engineering cannot achieve.

Answer

Vertex AI Search with grounding

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Prompt design with the Gemini API directly

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Vertex AI Model Evaluation

Question 20

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 attributions and explanations for model predictions, which is essential for financial services firms that must comply with regulatory requirements like the EU's GDPR or the US SEC's model risk management guidelines. It helps auditors and stakeholders understand why a model generated a specific investment report output, ensuring transparency and accountability in AI-driven decisions.

Answer

Vertex AI Model Registry

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Vertex AI Safety Settings

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Vertex AI AutoML