Generative AI Leader Practice Exam 57 — 20 Questions

Question 1

An enterprise architect must let a fleet of internal applications call Gemini models on Google Cloud while enforcing VPC Service Controls perimeters, customer-managed encryption keys, and audit logging tied to the organization's existing IAM. Which Google Cloud offering should the architect standardize on?

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

Accepted Answer

Vertex AI with Gemini models. Vertex AI is the Google Cloud platform where Gemini models are served to applications under the same enterprise controls as other Cloud services, including IAM, VPC Service Controls, customer-managed encryption keys, and audit logging. Because the architect needs perimeter enforcement and key management for API traffic, the Vertex AI model endpoints are the appropriate foundation rather than consumer-style APIs or assistant products.

Answer

Gemini API via Google AI Studio

Answer

Gemini Enterprise with data connectors

Answer

Gemini for Google Workspace

Question 2

A startup's developer wants to quickly test different prompts against Gemini models, compare model outputs side by side, and export working prompt code, all from a browser interface before committing to an application architecture. Which Google Cloud offering should the developer use?

Accepted Answer

Vertex AI Studio. Vertex AI Studio is the browser-based environment built for prompt design and rapid experimentation with Gemini models, including side-by-side comparison and code export. When the goal is to evaluate prompt behavior before committing to an application design, this console is the intended starting point rather than pipeline orchestration, end-user assistants, or a model catalog.

Answer

Vertex AI Model Garden

Answer

Gemini for Google Workspace

Answer

Vertex AI Pipelines

Question 3

A logistics company stores delivery exception reports as PDFs in a Cloud Storage bucket. An analyst needs to ask natural-language questions across all the reports and receive answers with citations to the source pages, with minimal development work. Which Google Cloud capability should the analyst use?

Accepted Answer

Vertex AI Search with a data store grounded on the Cloud Storage documents. Vertex AI Search ingests documents directly from Cloud Storage, builds a managed index, and returns natural-language answers grounded in those documents with citations to source pages. The alternatives all require substantial custom development: Pipelines orchestrates custom code, BigQuery ML needs the data loaded and parsed, and Cloud Vision only extracts raw text and features without answering questions.

Answer

BigQuery ML with a remote Gemini model

Answer

Vertex AI Pipelines orchestrating a custom document parser

Answer

Cloud Vision API batch annotation of the PDFs

Question 4

A logistics firm's leadership wants a plain-language summary of how generative AI could reduce costs in dispatch operations before approving any budget. The team has no data scientists available. Which first step best aligns with a generative AI business strategy?

Accepted Answer

Run a short, scoped pilot using a managed generative AI model on a sample of dispatch tasks and report measured outcomes.. A short pilot on a managed generative AI model converts an abstract idea into measured dispatch outcomes, such as handling time or routing decision quality, using the team already in place. That evidence lets leadership evaluate cost impact in plain language before committing budget, which is the essence of a staged generative AI business strategy.

Answer

Ask each regional dispatch manager to independently experiment with public consumer chatbots and report anecdotes.

Answer

Commission a multi-year research program to train a custom dispatch model on historical route data.

Answer

Purchase GPU hardware for an on-premises cluster so the firm controls its own model hosting from day one.

Question 5

A mid-sized insurance company wants to adopt generative AI to help claims adjusters draft customer correspondence. Executives are unsure how to prioritize the first use case and want a framework that balances business value with implementation feasibility. Which first step best aligns with a value-driven generative AI adoption strategy?

Accepted Answer

Select the use case with the highest volume of manual effort and a clear measurable baseline for time saved.. A value-driven generative AI strategy starts where effort is high, the task is repetitive, and a baseline already exists to measure improvement. High-volume correspondence drafting offers fast, quantifiable time savings that build credibility for later, more complex initiatives. This sequencing balances impact with feasibility instead of optimizing for technical novelty or waiting on the sidelines.

Answer

Wait until competitors publicly report their generative AI results before committing any budget.

Answer

Begin with the most technically complex use case to prove the organization's engineering capability.

Answer

Deploy generative AI across all claims functions simultaneously to maximize transformation speed.

Question 6

A marketing team wants to launch a generative AI campaign-copy tool. Executives ask the team to justify the investment by identifying the primary business objective before any technical design begins. Which statement best represents a valid business objective for this initiative?

Accepted Answer

Reduce the average time to produce approved campaign copy by a target percentage.. A valid business objective states a measurable improvement the organization wants, such as cutting the time to produce approved campaign copy. That kind of target can be baselined, tracked, and tied to value, giving executives a clear basis for funding decisions. Model selection, deployment platform, and prompting techniques are implementation choices that should be driven by the objective, not used in place of one.

Answer

Use prompt engineering techniques such as few-shot examples in every request.

Answer

Adopt the largest available foundation model for text generation.

Answer

Deploy the tool on Google Kubernetes Engine with autoscaling enabled.

Question 7

A media company wants to let its editors query a large archive of internal video transcripts using everyday conversational questions, and the app must return grounded answers that cite the exact source clips. The team has no machine learning engineers and wants the least operational overhead. Which Google Cloud offering should they use?

Accepted Answer

Vertex AI Search with a connected transcript data store. A managed search-and-grounding service is the right fit when business users need conversational answers over an indexed corpus with citations and no ML engineering effort. Indexing transcripts into a data store and letting the search service handle retrieval, grounding and citation generation satisfies both the accuracy and low-overhead constraints. Custom pipelines or raw model calls push that work back onto the team.

Answer

BigQuery ML with a remote Gemini model reference

Answer

Vertex AI Pipelines with a custom retrieval component

Answer

Gemini via the Gemini API in a stateless prompt loop

Question 8

A financial services firm is preparing a board presentation on its generative AI program. Directors want assurance that spending is disciplined and that failures are contained. Which TWO practices should the program adopt? (Choose two.)

Accepted Answer

Fund generative AI initiatives through staged gates, releasing additional budget only when agreed outcome metrics are met.. Board-level confidence in a generative AI program comes from disciplined capital allocation and bounded downside. Staged funding tied to outcome metrics ensures money follows evidence, while predefined stop and decommission criteria guarantee that underperforming initiatives end quickly and cheaply. Together they make the portfolio's risk explicit and controllable, which is what directors are asking to see.

Answer

Require every generative AI use case to run on the same foundation model to simplify vendor management.

Answer

Defer all evaluation of model output quality until after the production launch to save time.

Answer

Centralize all generative AI engineering in one team that owns every use case end to end.

Question 9

A global bank wants to use Gemini models in Vertex AI to summarize sensitive customer emails. The security team requires that prompts and responses never leave the bank's controlled network perimeter and that access is restricted to approved projects. Which Google Cloud capability should they configure?

Accepted Answer

VPC Service Controls. VPC Service Controls let organizations define a service perimeter around Google Cloud services, including Vertex AI. Resources inside the perimeter can communicate, but access from outside is blocked, which prevents data exfiltration. Combined with IAM policies limiting access to approved projects, this satisfies the bank's requirement that sensitive prompts and responses remain within a controlled boundary.

Answer

Cloud Armor

Answer

Cloud CDN

Answer

Cloud Interconnect

Question 10

A bank is deploying a Gemini model on Vertex AI to draft responses to customer complaints. Compliance requires that the deployment (Choose two.)

Accepted Answer

Records prompts and responses for audit purposes. A compliant generative AI deployment in a regulated setting needs traceability and output control. Logging prompts and responses creates the audit trail examiners expect, while configurable safety filters prevent harmful or policy-violating content from reaching customers. Bulk unreviewed training data, disabled logging, and public weight release either increase risk or remove evidence, so they do not belong in this deployment.

Answer

Disables all logging to reduce storage costs

Answer

Trains the model on the bank's entire historical email archive without review

Answer

Publishes the model's weights publicly to demonstrate transparency

Question 11

A startup wants to quickly prototype a generative AI application that can write marketing copy. They have limited machine learning expertise and want to avoid managing infrastructure. They prefer a fully managed, no-code or low-code solution that provides access to Google's foundation models. Which Google Cloud offering should they use?

Accepted Answer

Vertex AI Studio. Vertex AI Studio is the correct choice because it offers a user-friendly, no-code environment for experimenting with and prototyping generative AI models. It allows users to design prompts, test models like Gemini, and even fine-tune them without managing infrastructure. The other options are either too technical or not focused on generative AI prototyping.

Answer

BigQuery ML

Answer

Vertex AI Pipelines

Answer

Cloud Functions

Question 12

A software company wants to use a Google Cloud generative AI model to automatically generate code snippets from natural language descriptions. The developers need the model to produce syntactically correct code in multiple programming languages. Which Google Cloud offering is specifically designed for this use case?

Accepted Answer

Codey APIs on Vertex AI. Codey APIs on Vertex AI are purpose-built for code generation tasks, including translating natural language to code in multiple languages. They are trained on a large corpus of code and are optimized for syntax and correctness. The other options are either general multimodal models, search services, or conversational agents, none of which are specialized for code generation.

Answer

Dialogflow CX

Answer

Vertex AI Matching Engine

Answer

Gemini Pro Vision

Question 13

A large enterprise is using Vertex AI to deploy a generative AI model for internal document summarization. They need to ensure that the model's responses are based on the most current internal documents and that the model does not hallucinate. They also want to minimize latency and cost. Which feature of Vertex AI should they implement?

Accepted Answer

Grounding with Vertex AI Search. Grounding with Vertex AI Search is the correct feature because it enables the model to retrieve and cite relevant passages from a designated data store, ensuring responses are based on the latest internal documents. This approach reduces hallucinations and avoids the need for frequent fine-tuning, which can be costly and slow. It also supports low-latency responses by integrating retrieval with generation.

Answer

Model evaluation

Answer

Batch prediction

Answer

Model fine-tuning

Question 14

A telecommunications provider is preparing a business case for a generative AI virtual assistant that handles billing inquiries. Executives want the proposal to address financial viability, not just technical feasibility. Which two elements should the business case include to demonstrate responsible financial planning? (Choose two.)

Accepted Answer

A model of cost per resolved inquiry that includes inference, grounding, and human escalation.. A credible financial case for the billing assistant needs unit economics and a forward-looking benefit estimate. Cost per resolved inquiry ties inference, grounding, and human escalation into one comparable figure, while projected handling-cost reduction with sensitivity ranges shows how savings vary under different automation and volume assumptions. Together they let executives judge viability and set targets, whereas model inventories, prompt counts, and network diagrams do not quantify financial impact.

Answer

The number of prompt variants tested during internal prototyping.

Answer

A list of every foundation model available in Vertex AI Model Garden.

Answer

A diagram of the virtual private cloud network topology used by the assistant.

Question 15

A university's IT department is evaluating Google Cloud generative AI offerings to build a course-assistant tool for students. They want to reduce engineering effort by using managed services rather than hosting models themselves. Which two Google Cloud offerings should they consider? (Choose two.)

Accepted Answer

Vertex AI Agent Builder for assembling a grounded agent with tools and a knowledge base.. The department wants managed services to cut engineering effort. Gemini through Vertex AI supplies the model without infrastructure ownership, and Agent Builder supplies the orchestration, grounding, and tooling layer needed for a usable course assistant. Self-managing a model on GKE, pretraining a custom model, or forwarding requests to an external API all reintroduce significant engineering or governance work that the managed first-party offerings avoid.

Answer

A custom training pipeline built on Vertex AI Training to pretrain a domain-specific language model.

Answer

A self-managed open-weights model served from a Google Kubernetes Engine cluster with GPU node pools.

Answer

BigQuery ML with a remote model that forwards prediction requests to an external vendor's API.

Question 16

A software company wants to embed an AI coding assistant into its internal developer portal. The assistant must complete code in the developer's current editor context and also answer natural-language questions about the repository. Which Google Cloud offering is designed for this use case?

Accepted Answer

Gemini Code Assist, which provides code completion and conversational assistance integrated with developer tooling.. The scenario needs two developer-centric capabilities: inline completion in the editor and conversational answers about the repository. Gemini Code Assist is the Google Cloud offering built for both, integrating with supported IDEs and developer tooling. The other services serve data analytics, ML workflow orchestration, or data security, and none provides editor-integrated code assistance for a developer portal.

Answer

Vertex AI Pipelines, which orchestrates machine learning workflow steps such as training and evaluation.

Answer

Cloud Data Loss Prevention, which discovers and de-identifies sensitive data across storage systems.

Answer

Gemini in BigQuery, which generates SQL from natural-language prompts against datasets in BigQuery.

Question 17

A healthcare provider is building a generative AI assistant on Vertex AI to answer patient questions about appointment scheduling. They must ensure the model does not produce harmful or medically unsafe content, and they want to monitor and tune safety behavior over time. Which two Google Cloud features should they use? (Choose two.)

Accepted Answer

Vertex AI Model Monitoring for skew and drift detection. Safety filters with configurable thresholds block harmful or medically unsafe inputs and outputs, directly protecting patients. Model Monitoring tracks changes in data and predictions over time, surfacing drift that may indicate new safety risks or emerging question patterns. Together they provide both real-time protection and ongoing oversight, which the healthcare provider requires for a patient-facing assistant.

Answer

BigQuery ML logistic regression for sentiment analysis

Answer

Cloud KMS customer-managed encryption keys

Answer

Cloud Trace for distributed latency tracing

Question 18

A financial services company wants to use generative AI to summarize large volumes of internal documents while ensuring that sensitive data never leaves their virtual private cloud (VPC). They need a solution that provides Gemini models with enterprise-grade security and data residency controls. Which Google Cloud service should they use?

Accepted Answer

Vertex AI with Gemini models. Vertex AI offers Gemini models with enterprise-grade security, including VPC Service Controls and data residency, which are critical for handling sensitive financial data. It enables the company to use generative AI while maintaining strict control over data location and access. The other services either lack generative capabilities or do not provide the necessary security and compliance features for this scenario.

Answer

Gemini API in Google AI Studio

Answer

Document AI

Answer

Cloud Natural Language API

Question 19

A national logistics company is preparing a board presentation on responsible generative AI adoption. The board wants assurance that the program includes concrete organizational controls, not just technical safeguards. Which TWO actions should be included in the governance plan? (Choose two.)

Accepted Answer

Establish an AI governance committee with representatives from legal, security, data science, and business units to review use cases.. Organizational governance for generative AI rests on clear accountability and documented rules. A cross-functional governance committee provides structured review and decision rights, while an acceptable use policy defines permitted tools, prohibited data, and incident escalation. Together they demonstrate to the board that adoption is managed through people and process controls, complementing whatever technical safeguards are deployed.

Answer

Enable automatic model retraining every night to keep responses current.

Answer

Increase the model's temperature setting to encourage more creative and varied responses.

Answer

Purchase additional GPUs to ensure the training cluster can handle future model sizes.

Question 20

A global logistics company wants to build a generative AI assistant that can answer operational questions by retrieving information from internal PDF and HTML documents stored in Cloud Storage. They need a managed, serverless retrieval-augmented generation (RAG) capability that requires minimal infrastructure management and integrates with Vertex AI. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Search. Vertex AI Search delivers a fully managed, serverless retrieval-augmented generation pipeline that ingests documents from Cloud Storage, builds an index, and grounds Gemini responses without requiring the team to manage embeddings or vector databases. The other services are either feature stores, self-managed vector databases, or analytics tools that do not provide end-to-end RAG for unstructured content.

Answer

Vertex AI Feature Store

Answer

Cloud SQL with pgvector

Answer

BigQuery ML