Generative AI Leader Practice Exam 56 — 20 Questions

Question 1

A logistics company wants to measure whether its new generative AI assistant for dispatchers is delivering business value. Leadership asks for indicators that connect assistant usage to operational outcomes. Which two metrics should the company track? (Choose two.)

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

Accepted Answer

Average time dispatchers spend resolving a standard route exception before and after assistant adoption.. Business value for a dispatcher assistant is demonstrated by operational outcomes and quality of assistance. Resolution time for route exceptions shows process improvement, and acceptance of suggested actions shows the assistant produces usable guidance. Token usage, deployment counts, and latency describe cost, release activity, and performance rather than whether dispatch work improved, so they do not answer leadership's question.

Answer

Total number of model versions deployed to the production environment during the quarter.

Answer

Average latency of each inference request in milliseconds across all regions.

Answer

Number of tokens consumed by the assistant per dispatcher per day.

Question 2

A media company wants to create a custom AI assistant that can answer employee questions by referencing internal policy documents stored in Google Drive. They need a low-code solution that allows them to configure the assistant, connect data sources, and deploy it for internal use. Which Google Cloud offering should they use?

Accepted Answer

Vertex AI Agent Builder. Vertex AI Agent Builder is the only Google Cloud offering that provides a low-code environment specifically for building generative AI agents and applications. It allows integration with Google Drive and other data sources, enabling the assistant to retrieve and use internal policy documents to answer employee questions. The other services are either search-focused, productivity-focused, or document-processing-focused, and lack the agent orchestration and deployment features required.

Answer

Document AI

Answer

Gemini for Google Workspace

Answer

Vertex AI Search

Question 3

A media company plans to launch a generative AI feature that creates personalized article summaries for subscribers. Before launch, the product team must choose an operating model for ongoing quality, cost, and safety oversight. Which approach best supports responsible scaling of the feature?

Accepted Answer

Assign a cross-functional team to own evaluation, monitoring, and incident response for the feature after launch.. Responsible scaling depends on continuing ownership after launch. A cross-functional team can evaluate summary quality against source articles, monitor safety and cost signals, and respond quickly when issues arise. Freezing the system, depending solely on provider filters, or leaving decisions to one function with annual reviews all leave gaps in quality, safety, and accountability for a live subscriber-facing feature.

Answer

Rely on the model provider's built-in safety filters as the complete control set for the feature.

Answer

Freeze the model version and prompt at launch so behavior remains stable and no further review is needed.

Answer

Delegate all quality decisions to the engineering team that built the integration and revisit them annually.

Question 4

A logistics company wants to forecast delivery delays and also generate plain-language explanations for dispatchers. Leadership asks the GenAI Leader to choose an approach that keeps the numeric forecast auditable while adding generative explanations. Which design should the GenAI Leader propose?

Accepted Answer

Train a Vertex AI forecasting model on historical shipment data, then pass its output and key features to Gemini to generate the dispatcher explanation.. Keeping prediction and narration in separate components lets the forecasting model be validated with standard regression metrics while the language model handles communication. The numeric output remains deterministic and traceable, and the generative layer receives grounded inputs rather than inventing figures, which satisfies both leadership requirements.

Answer

Use a single foundation model prompt that returns both the predicted delay in minutes and a narrative explanation in one response.

Answer

Fine-tune Gemini on historical delay records so it learns to output delay minutes directly, and have dispatchers read its responses.

Answer

Deploy a retrieval-augmented generation pipeline over past dispatch notes and ask the model to infer the expected delay from similar historical cases.

Question 5

A logistics company wants to build a generative AI application that answers questions using its internal policy documents stored in Cloud Storage. They want a managed, serverless way to index those documents and retrieve relevant passages for grounding responses, without managing their own vector database. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Search. Vertex AI Search is a managed retrieval and search service that ingests documents from Cloud Storage and other repositories, indexes them, and returns relevant passages. It is serverless, so the logistics company avoids building and tuning a vector database. Integrating it with a generative model provides grounded answers that cite internal policies, improving accuracy and trust.

Answer

Vertex AI Feature Store

Answer

Cloud SQL with pgvector

Answer

BigQuery ML

Question 6

A marketing agency wants to generate personalized product descriptions at scale but has no machine learning engineers on staff. They need a managed Google Cloud option that provides access to foundation models through an API with minimal infrastructure work. Which offering should they choose?

Accepted Answer

Vertex AI Model Garden with Gemini models accessed through the Vertex AI API.. A managed foundation model catalog accessed through an API lets teams without ML specialists generate text at scale while Google Cloud handles serving, scaling, and security. Self-managed GPU deployments demand engineering skills, BigQuery ML targets structured predictions, and the Natural Language API analyzes rather than generates text. The managed API path best matches the agency's staffing and infrastructure constraints.

Answer

Build a custom model using BigQuery ML with only SQL and no external model access.

Answer

Use Cloud Natural Language API sentiment analysis to generate product descriptions.

Answer

Deploy an open-source LLM on a self-managed Compute Engine VM with GPUs.

Question 7

A mid-size accounting firm wants to deploy a generative AI assistant that summarizes client meeting notes and drafts follow-up emails. The partners require that client financial data never leaves the firm's Google Cloud project boundary for third-party training, and that usage costs stay predictable month to month. Which two Google Cloud practices should the firm adopt? (Choose two.)

Accepted Answer

Set Cloud Billing budgets and alerts on the project, and monitor token consumption per assistant feature.. Running Gemini through Vertex AI keeps prompts and responses inside the firm's project under Google Cloud data governance, so client financial data is not used for foundation model training. Pairing that with budgets, alerts, and per-feature token monitoring delivers the cost predictability the partners require, and neither practice adds infrastructure the firm must operate.

Answer

Disable all Cloud Logging on the assistant so that prompt and response content is never recorded anywhere.

Answer

Send every prompt to a third-party public chatbot API outside Google Cloud because it offers a free usage tier.

Answer

Publish the meeting notes to a public Cloud Storage bucket so the assistant can retrieve them without authentication.

Question 8

A financial services firm wants to deploy a generative AI assistant that summarizes earnings call transcripts for its analysts. The firm's risk committee requires that the assistant never produce investment recommendations and that all outputs be traceable to source transcript passages. Which design choice most directly enforces both constraints?

Accepted Answer

Use retrieval-augmented generation over the transcripts and apply a system instruction that prohibits recommendations, returning cited source chunks.. Grounding responses in retrieved transcript passages with RAG gives analysts verifiable citations, while a system instruction establishes a hard behavioral boundary against investment recommendations. This combination enforces both the provenance and the policy constraint at generation time, rather than relying on post-processing or broad content filters that cannot distinguish summary from advice.

Answer

Store all prompts and responses in BigQuery and run a nightly batch job to flag outputs that look like recommendations.

Answer

Deploy the assistant with a content filter that blocks any output containing financial terminology.

Answer

Fine-tune a Gemini model on historical earnings transcripts and deploy it with a low temperature setting.

Question 9

A small marketing analytics team wants to build a generative AI assistant that can answer questions about their proprietary campaign performance data. They have very limited machine learning engineering resources and want the fastest possible path to a working prototype on Google Cloud. Which approach should they take?

Accepted Answer

Use the Gemini models through Vertex AI with a managed RAG Engine backed by their campaign data stored in BigQuery.. The key requirement is speed to a working prototype with minimal ML engineering, and managed Gemini endpoints plus a managed RAG pipeline satisfy that without infrastructure ownership. The team supplies campaign data and prompts while Google Cloud handles retrieval, grounding, and serving. Options that require training from scratch, self-managing GPU clusters, or calling external APIs all add engineering burden or governance risk that the scenario rules out.

Answer

Deploy an open-source large language model on a self-managed Google Kubernetes Engine cluster with autoscaling GPU node pools.

Answer

Train a custom transformer model from scratch on their campaign data using Vertex AI Training with A3 machine types.

Answer

Create a BigQuery ML remote model that calls a third-party public API and join it to campaign tables in scheduled queries.

Question 10

A global media company wants to add generative AI capabilities to its existing applications, including text summarization, image generation, and code assistance. They plan to use Google Cloud services and need a managed solution that provides access to multiple foundation models through a single API, with enterprise-grade security and scalability. Which Google Cloud offering should they choose?

Accepted Answer

Vertex AI. Vertex AI is the correct choice because it is Google Cloud's comprehensive platform for generative AI, offering access to multiple foundation models through a single API. It supports text, image, and code generation, and includes enterprise features like security, scalability, and integration with other Google Cloud services. The other options are either specialized tools or subsets of Vertex AI that do not fulfill the need for a unified, multi-modal generative AI platform.

Answer

Dialogflow CX

Answer

Document AI

Answer

AutoML

Question 11

A media company plans to add generative AI features to its video editing suite. Executives want to prove business value within one quarter but cannot predict which of three candidate features editors will actually adopt. Which strategy best balances fast validation against wasted investment?

Accepted Answer

Ship a thin prototype of the highest-uncertainty feature to a small editor group and measure adoption before committing further.. When demand is genuinely uncertain, the fastest path to business value is a small experiment against the riskiest assumption, measured with real users. A thin prototype of the most uncertain feature yields adoption data within the quarter and converts an unknown into a go, pivot, or stop decision. Parallel production builds, preference surveys, and cost-led launches all postpone or avoid that evidence.

Answer

Build all three features to production quality in parallel so the strongest one wins on merit.

Answer

Select the feature with the lowest estimated engineering cost and take it directly to general availability.

Answer

Run a company-wide survey asking editors to rank the three features by preference before writing any code.

Question 12

A national retailer's generative AI pilot for product description writing succeeded technically, but six months later only two of forty merchandising teams use it. Interviews reveal that teams were never trained, the tool sits outside their existing content workflow, and no one owns adoption targets. Which action best addresses the root cause of this outcome?

Accepted Answer

Establish an adoption plan with named business owners, role-specific enablement, and integration of the tool into the existing content workflow.. Adoption failures after a technically successful pilot are usually organizational, not technical. The interviews identify missing enablement, poor workflow fit, and absent ownership, so the effective response is an adoption plan that names business owners, trains users by role, and integrates the assistant into the tools merchandisers already use daily.

Answer

Upgrade to a newer foundation model with higher benchmark scores for creative writing tasks.

Answer

Publish a company-wide mandate requiring all merchandising teams to use the tool for every product description.

Answer

Reduce the per-token cost of the generative AI service by switching to a smaller, cheaper model.

Question 13

A telecommunications provider plans to offer a generative AI feature that answers billing questions in multiple languages through its mobile app. Leadership wants to control costs as usage grows unpredictably while keeping responses fast. Which combination of practices best supports cost governance for this workload on Google Cloud?

Accepted Answer

Route simple queries to a smaller Gemini model and complex ones to a larger model, and monitor token usage with quotas and budgets.. Cost governance for variable generative AI workloads requires aligning model choice with query complexity and enforcing financial guardrails. Tiered routing keeps simple billing questions on an economical model while reserving the larger model for complex cases, and quotas plus budgets cap exposure as usage scales. Fast responses are preserved because the cheaper model also tends to have lower latency.

Answer

Use the largest available Gemini model for every request to maximize answer quality regardless of query complexity.

Answer

Cache every response indefinitely so repeated questions never require a new model invocation.

Answer

Disable logging and monitoring to reduce the storage and processing costs associated with observability.

Question 14

An energy utility is preparing a generative AI assistant that drafts responses to regulator inquiries. Before launch, the GenAI Leader must define how the program will be evaluated and governed on Google Cloud. Which TWO practices should be included? (Choose two.)

Accepted Answer

Track model and prompt versions alongside evaluation results so changes to the assistant can be compared and rolled back.. A defensible program pairs human approval of each external response with versioned tracking of models, prompts, and evaluation results so behavior is reproducible and reversible. Together these provide accountability and change control. Broad permissions, disabled logging, and reliance on safety filters alone all weaken oversight without addressing factual accuracy.

Answer

Grant the assistant's service account broad project-level Owner permissions so it can retrieve any internal document it may need.

Answer

Disable all logging of prompts and responses to avoid storing potentially sensitive regulatory content in Cloud Logging.

Answer

Rely solely on the foundation model's built-in safety filters as the complete control set for regulatory accuracy.

Question 15

A healthcare company is building a generative AI application using Vertex AI. They need to ensure that the application adheres to responsible AI principles, such as avoiding harmful outputs and protecting patient data. Which two Google Cloud features or practices should they implement? (Choose two.)

Accepted Answer

Configure safety filters and thresholds in Vertex AI. Configuring safety filters helps prevent harmful outputs, directly supporting the principle of avoiding harm. Using CMEK ensures that patient data is encrypted with keys managed by the company, enhancing data protection and compliance. Together, these features address both output safety and data privacy, which are critical for responsible AI in healthcare. The other options either increase risk or violate privacy.

Answer

Increase the model's temperature to generate more diverse responses

Answer

Disable logging of all API requests to protect patient data

Answer

Fine-tune the model on a dataset of patient interactions without anonymization

Question 16

A financial services firm wants to deploy a generative AI assistant that summarizes analyst research for internal advisors. The security team requires that the assistant never expose confidential client data in its responses, and the business wants to move quickly without building a custom model from scratch. Which approach best balances speed, control, and data protection on Google Cloud?

Accepted Answer

Use a managed foundation model with grounding on an access-controlled document store and Sensitive Data Protection filters on inputs and outputs.. Combining a managed foundation model with grounded retrieval over an access-controlled store and Sensitive Data Protection filtering keeps confidential client data out of model weights and out of responses. Advisors receive summaries only from documents they are authorized to view, and inspection adds detection and redaction for sensitive identifiers. This satisfies the security requirement while avoiding the cost and delay of training a custom model.

Answer

Fine-tune a foundation model on all historical analyst reports, including confidential client sections.

Answer

Deploy an open-source model on a single Compute Engine VM and rely on network firewalls for data protection.

Answer

Store all analyst reports in a public Cloud Storage bucket so the model can retrieve them without authentication.

Question 17

A hospital wants to build a generative AI assistant that answers clinician questions using the hospital's own clinical guidelines. The guidelines change frequently, and the hospital wants the assistant to cite the exact guideline section used. Which approach best meets these requirements while minimizing model retraining?

Accepted Answer

Ground the assistant on the guidelines using retrieval-augmented generation with Vertex AI Search. Grounding with retrieval-augmented generation separates knowledge from the model: guideline passages are indexed and retrieved per query, so updates mean re-indexing rather than retraining, and responses can cite the retrieved section. Fine-tuning forces retraining on every change and gives no citations, temperature affects randomness not accuracy, and stuffing all guidelines into the prompt hits context limits without reliable attribution.

Answer

Expand the model's context window and paste all guidelines into every prompt

Answer

Fine-tune the Gemini model every time the clinical guidelines are updated

Answer

Increase the model's temperature so it recalls guideline details more accurately

Question 18

A logistics company plans to use generative AI to draft responses to customer shipment inquiries. Legal requires a documented process for reviewing model outputs and handling harmful or inaccurate content before responses reach customers. Which practice should be built into the solution?

Accepted Answer

Implement human-in-the-loop review with logging and content safety filters before responses are sent.. A documented review process for generative AI outputs typically combines automated safety filters, human review before customer delivery, and logging that preserves an audit trail. Reactive reporting, fine-tuning for tone, or avoiding review altogether do not provide the proactive controls and documentation legal demands. The layered human-in-the-loop design satisfies both safety and accountability requirements.

Answer

Fine-tune the model on past customer emails so it learns the company's tone and never produces harmful content.

Answer

Restrict the assistant to internal use only and skip any output review since customers never see it.

Answer

Deploy the model directly to customers and rely on users to report bad responses afterward.

Question 19

A media company plans to use generative AI to draft marketing copy for dozens of regional brands. Legal wants confidence that outputs respect brand tone and avoid unapproved claims, while finance wants to know how usage will be metered. Which two Google Cloud practices best support these goals? (Choose two.)

Accepted Answer

Track token consumption per brand through Vertex AI usage metrics and allocate budgets accordingly.. Grounding drafts in a versioned store of approved brand guidance gives legal traceable assurance that tone and claim rules are respected, while token-level usage metrics let finance attribute spend to each regional brand. The two practices reinforce each other: every grounded call is both policy-anchored and measurable, so the platform scales across brands without multiplying models.

Answer

Route every request through a single shared API key distributed to all regional marketing teams.

Answer

Fine-tune a separate foundation model for each regional brand to guarantee distinct writing styles.

Answer

Disable all logging on the generative endpoints so draft content never appears in audit records.

Question 20

A regional insurance company wants to launch a generative AI claims-triage assistant. The CISO requires that no claims data leave the company's existing Google Cloud project boundary and that every model call be attributable to a named employee for audit. Which combination of Google Cloud controls should the architecture team prioritize?

Accepted Answer

VPC Service Controls around the project plus Cloud Audit Logs capturing the authenticated principal on each Vertex AI request.. A service perimeter built with VPC Service Controls prevents claims data from being moved outside the approved project even by credentialed users, which is the strongest fit for the boundary mandate. Cloud Audit Logs then capture the authenticated principal behind every Vertex AI call, producing the employee-level attribution auditors demand. Encryption and edge protections are valuable but do not replace these two controls.

Answer

Customer-managed encryption keys in Cloud KMS plus a service account shared by the entire claims department.

Answer

Cloud Armor policies on the public load balancer plus Security Command Center premium findings.

Answer

Cloud CDN in front of the assistant plus Identity-Aware Proxy on the internal admin console.