Generative AI Leader Practice Exam 55 — 20 Questions

Question 1

A marketing team is using a generative AI model to create ad copy. They want to control the randomness of the output so that the same prompt produces consistent results for A/B testing. Which parameter should they adjust?

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

Accepted Answer

Temperature. Temperature directly controls the randomness of token selection. A temperature of 0 (or very close to 0) makes the model deterministically pick the highest-probability token, ensuring the same prompt yields the same output. This is essential for A/B testing where consistent ad copy variants are required. Other parameters affect diversity but do not guarantee reproducibility.

Answer

Top-p

Answer

Top-k

Answer

Max output tokens

Question 2

A hospital wants to use a generative AI model to draft patient discharge summaries. The model must be able to understand medical terminology and generate coherent, context-aware text. Which type of generative AI model is most suitable?

Accepted Answer

A large language model (LLM) like Gemini. Large language models like Gemini are pre-trained on extensive text, including medical content, and can generate coherent, context-aware narratives. They are well-suited for drafting discharge summaries because they understand medical terminology and can adapt to different patient cases. Other model types lack the language generation capabilities needed for this task.

Answer

A generative adversarial network (GAN)

Answer

A variational autoencoder (VAE)

Answer

A rule-based natural language generation (NLG) system

Question 3

A financial services firm is deploying a generative AI chatbot to answer employee questions about internal policies. The firm must ensure that the chatbot does not reveal sensitive information from other departments. Which technique should they implement?

Accepted Answer

Use retrieval-augmented generation with document-level access controls.. Retrieval-augmented generation can be combined with access control lists so that the retrieval step only returns documents the requesting user is permitted to view. This ensures the model's context is limited to authorized information, preventing leakage across departments. Fine-tuning or context expansion without access controls would not solve the security requirement.

Answer

Fine-tune the model on all internal documents to improve accuracy.

Answer

Set the temperature to a low value to reduce creative responses.

Answer

Increase the model's context window to include more documents.

Question 4

A healthcare startup is building a generative AI application to draft patient education materials. They need to ensure the outputs are accurate, up-to-date, and tailored to each patient's condition. Which two techniques should they use to ground the model's responses in reliable medical knowledge? (Choose two.)

Accepted Answer

Retrieval-augmented generation (RAG) using a curated medical knowledge base.. Retrieval-augmented generation (RAG) grounds the model by providing relevant, trusted documents as context, while a fact-checking layer validates outputs against a reliable API. Together, they ensure accuracy and currency. Fine-tuning, few-shot prompting, and larger context windows do not inherently guarantee factual correctness or access to the latest medical knowledge.

Answer

Fine-tuning the model on a large dataset of historical patient education materials.

Answer

Using a model with a larger context window to include more patient history.

Answer

Prompt engineering with few-shot examples of desired outputs.

Question 5

A healthcare company is building a generative AI assistant to answer patient questions about medications. The team wants to ensure the model's responses are grounded in approved clinical guidelines and avoid fabricated information. Which approach best addresses this requirement?

Accepted Answer

Implement retrieval-augmented generation (RAG) to fetch relevant passages from a curated clinical guideline database before generating a response.. Retrieval-augmented generation (RAG) grounds the model's output by retrieving relevant, up-to-date information from a curated database and conditioning the generation on that context. This reduces hallucinations and ensures responses align with approved clinical guidelines. Other options either increase randomness, rely solely on fine-tuning without retrieval, or incorrectly assume smaller models are safer.

Answer

Use a smaller model with fewer parameters to minimize the risk of generating incorrect information.

Answer

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

Answer

Fine-tune the model on a large corpus of general medical textbooks without any retrieval mechanism.

Question 6

A software company is using Vertex AI to build a generative AI application that creates code snippets from natural language descriptions. They want to improve the model's performance on their specific coding style and libraries. Which two techniques should they use? (Choose two.)

Accepted Answer

Use prompt engineering with few-shot examples of the desired code style.. Fine-tuning on a custom dataset and using few-shot prompt engineering are both effective ways to adapt a generative model to a specific coding style. Fine-tuning provides deep customization, while few-shot prompting offers a lightweight alternative. Together, they can significantly improve the relevance and accuracy of generated code.

Answer

Deploy the model on a larger GPU to increase generation speed.

Answer

Use Vertex AI Model Evaluation to compare different models and select the best one.

Answer

Increase the model's temperature setting to encourage more creative code.

Question 7

A financial institution is evaluating Google Cloud's generative AI offerings to build an internal knowledge assistant. They require a solution that can understand and generate text, support conversational interactions, and be customized with their proprietary data. They also need to ensure that sensitive data is not used to train the underlying models. Which two Google Cloud services or features should they use? (Choose two.)

Accepted Answer

Gemini models in Vertex AI. Gemini models in Vertex AI and Vertex AI Search and Conversation are the correct choices because they together provide the necessary generative AI capabilities and customization options. Gemini models offer advanced text generation and conversational AI, while Vertex AI Search and Conversation enables grounding in proprietary data and ensures data privacy. The other services are either for different modalities or lack generative AI features.

Answer

Dialogflow ES

Answer

AutoML Natural Language

Answer

Cloud Vision API

Question 8

A retail company wants to use a generative AI model to create personalized product descriptions for thousands of items. They need the descriptions to be consistent in style and format, but they also want to avoid the model inventing false features. Which approach best balances consistency and accuracy?

Accepted Answer

Set a low temperature and provide a structured prompt with clear guidelines and examples.. Setting a low temperature makes the model's output more predictable and consistent, while a structured prompt with guidelines and examples steers the model to follow a specific style and format. This combination reduces the likelihood of hallucinated features because the model is constrained by explicit instructions and examples. Other approaches either increase variability or do not directly address accuracy.

Answer

Use a high temperature to encourage creativity and let the model generate unique descriptions for each item.

Answer

Increase the max output tokens to allow the model to elaborate more on each product.

Answer

Fine-tune the model on a dataset of existing product descriptions to learn the style.

Question 9

A regional insurance company wants to let claims adjusters ask natural-language questions about 12 years of policy documents and claim histories stored in Cloud Storage. Leadership wants a working prototype in two weeks, minimal model-tuning effort, and answers that cite the exact source document. Which Google Cloud approach should the team implement?

Accepted Answer

Build a retrieval-augmented generation flow using Vertex AI Search to index the corpus and ground Gemini responses in retrieved passages.. Retrieval-augmented generation pairs a managed search index with a foundation model, so answers are grounded in retrieved passages and can point back to the originating document. Vertex AI Search handles ingestion and indexing of the Cloud Storage corpus, while Gemini synthesizes the retrieved context into an answer. This avoids costly fine-tuning and oversized prompts while meeting the citation and speed requirements.

Answer

Increase the Gemini model's context window to the maximum and paste the entire document corpus into every prompt.

Answer

Fine-tune a Gemini model on the historical claims corpus, then deploy the tuned endpoint behind an internal web app.

Answer

Train a custom text embedding model from scratch on the claims corpus, then serve similarity search from a self-managed cluster.

Question 10

A utility company's board approves a generative AI program and asks the program lead to present a plan showing how investment will be governed and how value will be tracked from pilot through production. The lead wants a framework that ties each initiative to a business owner, defines stage gates for continued funding, and specifies which metrics justify scaling. Which approach best meets the board's expectation?

Accepted Answer

Create a value realization framework that links each generative AI initiative to a business owner, defines stage-gate criteria for continued investment, and specifies scale-up metrics.. The board is asking for investment governance and value tracking across the program lifecycle. A value realization framework supplies exactly that: a named business owner per initiative, stage gates that decide whether funding continues after each phase, and pre-agreed metrics that must be met before scaling. This makes continued investment evidence-based rather than momentum-driven.

Answer

Adopt an AI acceptable use policy that prohibits employees from entering confidential data into public generative AI tools.

Answer

Standardize on a single foundation model and require every initiative to use it to simplify procurement and support.

Answer

Build a centralized generative AI center of excellence that provides prompt libraries and engineering support to all business units.

Question 11

A global logistics firm wants to add a generative AI feature that drafts replies to customer shipment inquiries. The team must prove business value to executives within one quarter, keep engineering effort low, and later swap in a different model without rewriting the application. Which design decision best supports those goals?

Accepted Answer

Call Gemini through the Vertex AI API and isolate prompt construction and model selection behind an internal abstraction layer.. Using a managed Vertex AI endpoint for Gemini accelerates delivery and removes infrastructure work, while an internal abstraction layer for prompts and model selection decouples the application from any single provider. That combination lets the firm demonstrate value quickly and later change models by adjusting one integration point rather than rewriting business logic.

Answer

Embed model-specific SDK calls directly in the application so each provider's latest features are immediately available.

Answer

Deploy an open-weights model on a single virtual machine and expose it through a custom REST endpoint.

Answer

Train a bespoke language model on historical shipment correspondence and serve it from a self-managed GPU cluster.

Question 12

A marketing team is using a generative AI model to create ad copy. They want to control the randomness of the output so that the same prompt produces consistent results for A/B testing. Which parameter should they adjust?

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

Accepted Answer

Temperature. Temperature is the parameter that directly controls the randomness of the model's output. Increasing temperature makes the output more diverse and creative, which is desired for ad copy generation. While top-k and top-p also influence sampling, temperature is the primary knob for adjusting diversity. Max output tokens only controls length.

Answer

Top-k

Answer

Top-p

Answer

Max output tokens

Question 13

A marketing team wants to use generative AI to draft product descriptions. Legal requires that no customer data or proprietary campaign plans appear in any prompt sent to the model. Which practice should the team adopt first?

Accepted Answer

Establish prompt input guidelines and a review step that excludes customer data and proprietary campaign details.. Because the restriction concerns what is placed into prompts, the effective first step is to define allowable prompt content and review submissions against it. That prevents sensitive data from leaving the organization and creates evidence of compliance. Temperature settings, output filtering, and contractual addenda do not control what data is included in the prompt, so they cannot satisfy the stated legal constraint.

Answer

Ask the model provider to sign a data processing addendum covering all prompts submitted by the team.

Answer

Configure the model with a low temperature to reduce the chance that sensitive text is reproduced.

Answer

Enable a content filter on the model output to block any sensitive information that appears in drafts.

Question 14

A global retailer wants to deploy a generative AI assistant that answers employee questions about HR policies in English, Spanish, and Japanese. The HR policy documents are updated monthly, and the company wants to avoid retraining the underlying large language model. Which approach should the retailer use?

Accepted Answer

Use retrieval-augmented generation with a vector index of the current HR policy documents and a foundation model.. Retrieval-augmented generation keeps the language model unchanged while supplying current, relevant HR policy passages at query time. That matches the need to avoid retraining, support multiple languages through prompting, and reflect monthly document updates after re-indexing. Fine-tuning, temperature changes, and full-corpus prompting either add recurring training work or fail to guarantee fresh, grounded answers.

Answer

Prepend the entire HR policy corpus to every prompt and rely on the model's context window for accuracy.

Answer

Fine-tune a foundation model on the translated HR policy documents each month and deploy the tuned model.

Answer

Increase the model's temperature setting so it can infer policy changes from patterns in previous answers.

Question 15

A regional insurance company wants to launch a generative AI assistant that answers policyholder questions. Legal requires that every customer-facing answer be traceable to an approved policy document, and that the system never invent coverage terms. The company has about 40,000 internal policy PDFs that change quarterly. Which approach should the GenAI Leader recommend?

Accepted Answer

Ground the assistant with retrieval-augmented generation using Vertex AI Search over the policy document corpus, and require citations in responses.. Grounding the assistant in the approved corpus with retrieval-augmented generation satisfies traceability because each response is generated from retrieved passages that can be cited, and it keeps answers current because the index is refreshed when policies change. Fine-tuning, whole-corpus prompting, and instruction-only approaches all leave the model without a verifiable source for coverage terms.

Answer

Deploy the assistant on Gemini with a system instruction telling it to be accurate and to avoid making up policy details.

Answer

Fine-tune a foundation model on the full set of policy PDFs and deploy the tuned model to a Vertex AI endpoint for the assistant to call.

Answer

Use a large context window model and paste the entire policy library into every prompt so the model always sees all approved documents.

Question 16

A financial services firm is evaluating generative AI use cases and must present a business case to its risk committee. The committee requires that each proposed use case have a measurable benefit and a clear owner before funding. Which action best aligns with a generative AI value-assessment practice?

Accepted Answer

Score each use case on expected business impact, implementation feasibility, and risk, and assign a named business owner.. The committee's requirements map to a prioritized business case: quantify expected impact, assess feasibility, account for risk, and name an accountable owner. Scoring proposals on those dimensions produces comparable evidence for funding decisions. Model size, submission order, and undirected pilots do not demonstrate measurable benefit or ownership, so they fail the stated governance criteria.

Answer

Select use cases based on which departments submitted requests first to ensure fair allocation of resources.

Answer

Rank use cases by the size of the model they require, prioritizing the largest models for maximum capability.

Answer

Fund all proposed use cases at a small scale and let usage metrics determine which ones receive more investment later.

Question 17

A small marketing team wants to use a Google Cloud generative AI model to generate creative text for social media posts. They prefer a fully managed, ready-to-use API that requires minimal setup and does not need model training. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Gemini API. The Vertex AI Gemini API offers direct access to powerful generative models without any training or infrastructure management. It is ideal for tasks like creative writing, where the team can simply send prompts and receive generated text. The other services are either for custom model training, image analysis, or conversational agents, and do not provide the same ease of use for text generation.

Answer

AutoML Natural Language

Answer

Vision AI

Answer

Dialogflow CX

Question 18

A global retailer's customer service team wants to deploy a generative AI chatbot that answers questions about order status, return policies, and product availability. The chatbot must always reflect the latest policies and inventory data without requiring frequent model retraining. Which approach should they use?

Accepted Answer

Use retrieval-augmented generation to ground responses in live policy documents and inventory APIs.. Grounding responses in live enterprise data through retrieval-augmented generation keeps a generative AI assistant accurate as policies and inventory change, without repeated model retraining. Fine-tuning and pre-training embed static knowledge that goes stale, and temperature adjustments affect style rather than factual currency. The retrieval pattern is the standard Google Cloud approach for assistants that must answer from authoritative, frequently updated sources.

Answer

Fine-tune a foundation model on historical customer service transcripts and redeploy it weekly.

Answer

Pre-train a custom foundation model from scratch using the retailer's historical order database.

Answer

Increase the model's temperature setting so it can generate more varied and current answers.

Question 19

A retail bank's risk committee will only approve a generative AI assistant for internal policy questions if every model output can be traced to an approved source, unsafe outputs are blocked before display, and reviewers can see why a response was allowed or denied. Which combination of Google Cloud capabilities should the architects design around?

Accepted Answer

Vertex AI grounding with citations plus Model Armor filtering and Cloud Logging of prompt and response metadata.. The committee's conditions map to grounding with citations for provenance, Model Armor for safety screening before output is shown, and Cloud Logging for an auditable record that explains each allow or deny decision. Combining these managed capabilities avoids custom safety tooling while giving reviewers both the source of an answer and the policy evaluation behind it.

Answer

A larger Gemini model with a higher temperature setting plus Cloud Monitoring dashboards on token usage.

Answer

A custom-trained classifier that scores response toxicity, deployed on Vertex AI Endpoints and called after the user sees the answer.

Answer

Vertex AI Model Registry versioning plus a Cloud Storage bucket that archives every raw model response.

Question 20

A national retail chain wants to deploy a generative AI assistant that recommends products to shoppers in six countries. The company's legal team requires that customer conversations never leave the country of origin, while the engineering team wants one consistent deployment pattern across all regions. Which Google Cloud approach best satisfies both requirements?

Accepted Answer

Deploy the assistant separately in a Google Cloud region within each country and keep conversation storage in that same region.. Data residency for generative AI workloads requires that both inference and any retained conversation data physically stay inside the required jurisdiction. Only a per-country regional deployment achieves that while letting the team reuse one design, one prompt library, and one deployment mechanism everywhere. Perimeter controls, global routing, and multi-region storage change access or durability, not the physical location of processing.

Answer

Configure VPC Service Controls per country and continue serving all shoppers from one central region.

Answer

Use a multi-region bucket for conversation logs and set a Cloud Storage lifecycle rule that deletes objects after 30 days.

Answer

Deploy the assistant in a single global region and rely on Google's global load balancing to route users to the nearest edge point of presence.