Generative AI Leader Practice Exam 54 — 20 Questions

Question 1

A financial services firm is deploying a generative AI model to answer customer questions about investment products. They need to ensure that the model's responses comply with regulatory requirements and do not provide personalized financial advice. Which approach should they take?

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

Accepted Answer

Use a system prompt that instructs the model to avoid giving personalized advice and to include disclaimers.. Using a system prompt is an effective way to set guardrails for the model's behavior. By explicitly instructing the model to avoid personalized financial advice and to include necessary disclaimers, the firm can help ensure compliance with regulations. This approach is flexible and can be updated as regulations change, unlike fine-tuning which requires retraining.

Answer

Fine-tune the model on a dataset of compliant financial conversations.

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Deploy the model with a high temperature to encourage diverse responses.

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Limit the model's responses to a predefined set of FAQs.

Question 2

A retail company wants to build an internal assistant that answers employee questions using the company's own HR policy documents. The team has no machine learning engineers and wants a managed Google Cloud approach that grounds responses in those documents without training a new foundation model. Which Google Cloud capability best fits this need?

Accepted Answer

Vertex AI Search with grounding on a data store built from the HR documents. Vertex AI Search provides managed retrieval over enterprise documents and grounds generative answers in that content, so employees receive responses based on current HR policy rather than the model's general knowledge. It requires no foundation-model training and minimal ML operations, matching the team's skills and timeline. The other choices either demand heavy ML work or address unrelated prediction tasks.

Answer

Deploying an open-source model on a Compute Engine GPU VM and fine-tuning it weekly

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Using BigQuery ML to run a logistic regression over HR ticket categories

Answer

Training a custom foundation model from scratch on the HR documents

Question 3

A retail company wants to use a generative AI model to create unique product descriptions for thousands of items. They need the model to produce varied, creative text without requiring them to provide any examples. Which type of model should they use?

Accepted Answer

A large language model (LLM) such as Gemini. Large language models like Gemini are pre-trained on diverse text and can generate creative, varied outputs from prompts alone. They do not require task-specific examples, making them perfect for producing unique product descriptions at scale. Other model types are designed for different tasks and cannot fulfill the creative text generation requirement.

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A convolutional neural network (CNN)

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A recurrent neural network (RNN)

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A decision tree classifier

Question 4

A data scientist is using Vertex AI to generate product descriptions from a list of features. They notice that the model sometimes omits key features or invents details not present in the input. They want to reduce hallucinations and ensure all provided features are included. Which technique should they apply?

Accepted Answer

Use a few-shot prompting approach with examples that include all features.. Few-shot prompting is the most direct way to guide the model to include all features and avoid hallucinations. By showing examples where every feature is incorporated accurately, the model learns the expected pattern. Other parameters like temperature or top-p affect randomness but do not provide the necessary instruction on completeness and fidelity.

Answer

Enable the model's grounding feature with a public dataset.

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Set the top-p parameter to 0.1 to narrow the token selection.

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Increase the temperature to encourage more creative outputs.

Question 5

A team is preparing to use Vertex AI Studio to prototype a generative AI application. They want to understand which factors directly influence the quality and safety of the model's responses. (Choose two.)

Accepted Answer

The configured safety filters and their threshold settings. Prompt construction and safety filter configuration are the two levers that most directly shape a generative model's responses in Vertex AI Studio. A precise prompt improves relevance and accuracy, while safety thresholds govern what content is permitted. Presentation choices, client location, and programming language affect the surrounding application but not the model's generation or filtering behavior, so they do not influence response quality or safety.

Answer

The color scheme of the application's user interface

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The physical region where the developer's laptop is located

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The programming language used to call the Vertex AI API

Question 6

A marketing team wants to use a generative AI model to create dozens of unique social media captions for a new product launch. They need the outputs to vary in tone and wording each time they run the prompt, while staying on topic. Which model parameter should they adjust to directly control this variability?

Accepted Answer

Temperature. Temperature is the parameter that scales the logits before sampling, directly controlling how random or deterministic the model's token choices are. Increasing temperature makes the model more likely to pick less probable words, yielding more varied and creative captions. The other parameters influence sampling or output length but do not provide the same direct control over variability.

Answer

Top-P

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Top-K

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Max output tokens

Question 7

A team is designing a generative AI application that must avoid generating harmful or biased content. They are considering various techniques to implement safety measures. Which two approaches are recommended for mitigating harmful outputs in generative AI models? (Choose two.)

Accepted Answer

Using reinforcement learning from human feedback (RLHF) to align model behavior with human values.. RLHF aligns model behavior with human values by learning from human feedback, while content filters provide a post-generation safety net. Together, they form a robust strategy for reducing harmful outputs. Other options like increasing temperature or limiting output length do not effectively mitigate toxicity and may introduce other issues.

Answer

Fine-tuning the model on a dataset that contains only positive examples.

Answer

Reducing the max output tokens to limit the amount of generated text.

Answer

Increasing the model's temperature to encourage more diverse and creative outputs.

Question 8

A company is evaluating Google Cloud's generative AI offerings to build a custom chatbot that can answer questions about their proprietary product manuals. They want to ensure the solution can be grounded in their own data and can be integrated into their existing applications. Which two Google Cloud services should they consider? (Choose two.)

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Search and Conversation and Vertex AI Generative AI Studio are both Google Cloud services that enable grounding in proprietary data and integration into applications. Vertex AI Search and Conversation provides a turnkey solution for grounded search and chat, while Generative AI Studio offers more customization for building and deploying models with retrieval augmentation.

Answer

Cloud Vision API

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Cloud Translation API

Answer

Dialogflow CX

Question 9

A developer is deploying a generative AI model on Vertex AI for a production application that requires low latency and high throughput. They need to choose an appropriate endpoint type. Which deployment option should they use?

Accepted Answer

Online prediction with a dedicated endpoint. Online prediction with a dedicated endpoint is the correct choice for low-latency, high-throughput serving. It allows the application to send requests and receive immediate responses, with autoscaling to handle varying loads. Batch prediction, Pipelines, and monitoring serve different purposes and cannot fulfill the real-time inference requirement.

Answer

Vertex AI Pipelines

Answer

Model monitoring

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Batch prediction

Question 10

A retail company wants to build an assistant that can answer customer questions about its product catalog, but the catalog changes daily. The team wants the model to reference the latest product information without retraining. Which approach should they use?

Accepted Answer

Use retrieval-augmented generation (RAG) to fetch relevant product information at inference time.. Retrieval-augmented generation combines a retrieval system with a generative model, allowing the assistant to fetch the latest product details from a database or document store and include them in the prompt. This keeps answers accurate and current without the cost and delay of retraining. It also provides traceability, as the retrieved sources can be cited.

Answer

Increase the model's temperature to encourage more creative answers.

Answer

Fine-tune a foundation model on the product catalog each day.

Answer

Deploy the model on a larger machine type with more memory.

Question 11

A healthcare company is evaluating foundation models for a patient triage assistant. They need to ensure the model is appropriate for medical use and that they can control costs. Which two factors should they consider when selecting and using a foundation model in Vertex AI? (Choose two.)

Accepted Answer

The token-based pricing and expected request volume to estimate and control costs.. For a medical triage assistant, the model's training data cutoff and domain relevance are critical to ensure safe, current guidance. Token-based pricing and expected volume determine cost predictability and control. Together, these factors address the dual requirements of clinical appropriateness and financial management when selecting and using a foundation model in Vertex AI.

Answer

The model's ability to generate images for patient education materials.

Answer

The color scheme of the model's documentation website.

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The number of GPUs available in the company's on-premises data center.

Question 12

A media company is using a generative AI model to produce news summaries. They notice that the summaries sometimes include fabricated details not present in the source articles. Which approach should they take to reduce these hallucinations?

Accepted Answer

Ground the model with retrieval-augmented generation (RAG). Retrieval-augmented generation (RAG) retrieves relevant information from a trusted source and feeds it to the model, ensuring summaries are based on actual content. This reduces hallucinations by anchoring the model's output to provided facts. Other methods like increasing temperature or fine-tuning do not directly address the need for factual grounding.

Answer

Increase the model's temperature parameter

Answer

Fine-tune the model on the company's news articles

Answer

Use a larger model with more parameters

Question 13

A retail company wants to use a generative AI model to create product descriptions from a list of attributes. They want a fully managed service that allows them to quickly experiment with prompts and adjust parameters without managing infrastructure. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Studio. Vertex AI Studio is a Google Cloud service that provides a unified interface for experimenting with generative AI models. It allows users to write prompts, adjust model parameters, and see generated outputs in real time. Since the company wants a fully managed service to quickly create product descriptions without infrastructure management, Vertex AI Studio is the correct choice.

Answer

AutoML Natural Language

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Cloud Natural Language API

Answer

Dialogflow CX

Question 14

A marketing team wants to use a generative AI model to create ad copy in multiple languages. They are new to Google Cloud and want the fastest way to start experimenting without writing code. Which tool should they use?

Accepted Answer

Vertex AI Generative AI Studio in the Google Cloud console.. Generative AI Studio offers a no-code, console-based environment for prompting and experimenting with foundation models. The marketing team can quickly test multilingual ad copy by entering prompts and adjusting settings, without writing any code. This makes it the fastest and most accessible option for beginners on Google Cloud.

Answer

Cloud Shell Editor to write Python scripts that call the Vertex AI API.

Answer

Google Cloud CLI with the gcloud ai models command.

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BigQuery console to run SQL queries against ad performance data.

Question 15

A developer is building a generative AI application using Vertex AI and wants to ensure that the model's responses are safe and aligned with their company's content policies. They need to filter out harmful content such as hate speech and harassment from both user inputs and model outputs. Which Vertex AI feature should they configure?

Accepted Answer

Safety filters. Safety filters in Vertex AI are specifically designed to detect and block harmful content in both prompts and responses. By configuring these filters, the developer can enforce the company's content policies and reduce the risk of generating or processing unsafe material, ensuring a safer generative AI application.

Answer

Cloud Armor

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IAM policies

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VPC Service Controls

Question 16

A marketing team uses a generative AI model to produce ad copy. They notice that when they set the temperature parameter to 0.1, the outputs are very similar across runs, but when they set it to 0.9, the outputs vary widely. They want a balance between creativity and consistency for a campaign that requires some variation but also brand alignment. Which temperature value should they choose?

Accepted Answer

0.5. A temperature of 0.5 provides a middle ground between deterministic and highly random outputs. It allows for some creativity and variation while keeping the text coherent and aligned with brand guidelines. Lower values like 0.0 reduce variation, while higher values like 1.0 or 2.0 increase randomness and risk off-brand content.

Answer

0.0

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2.0

Answer

1.0

Question 17

A media company wants to generate personalized video summaries for users based on their viewing history. They plan to use a generative AI model on Vertex AI. Which technique should they use to ensure the summaries are tailored to each user's preferences?

Accepted Answer

Retrieval-augmented generation (RAG) with a vector database of user viewing history and video metadata.. Retrieval-augmented generation (RAG) with a vector database allows the system to fetch relevant user-specific data, such as past viewing history and video metadata, and use it to condition the model's output. This results in summaries tailored to each user's preferences. Fine-tuning per user is unscalable, and generic prompts or temperature adjustments do not provide true personalization.

Answer

Use a high temperature setting to generate diverse summaries that might match user preferences.

Answer

Apply prompt engineering with a generic template that asks for a summary in a friendly tone.

Answer

Fine-tune a separate model for each user on their viewing history.

Question 18

A junior developer is experimenting with a large language model and notices that the same prompt produces different outputs each time it is run. Which characteristic of generative AI models explains this behavior?

Accepted Answer

The model uses a stochastic sampling process to select the next token from a probability distribution.. The variability in outputs arises because generative models sample from a probability distribution over possible next tokens. This stochastic sampling, influenced by parameters like temperature, leads to different sequences even with identical prompts. The other options incorrectly attribute the behavior to retraining, caching, or a fixed temperature of zero.

Answer

The model retrains itself after every inference, incorporating the previous output into its weights.

Answer

The model caches previous responses and randomly selects one from its memory.

Answer

The model's temperature parameter is fixed at zero, causing deterministic outputs.

Question 19

A financial services company is deploying a generative AI model to summarize sensitive customer emails. The security team requires that no email content is used to train or improve the underlying foundation model. Which Google Cloud approach ensures this requirement is met?

Accepted Answer

Rely on the default data processing terms of Google Cloud, which state that customer data is never used to train foundation models.. Google Cloud's generative AI services are governed by terms that prohibit using customer data to train or improve foundation models without explicit consent. This contractual commitment is the primary control that satisfies the security team's requirement. Technical measures like encryption or network isolation are important for security but do not address the specific concern about training data usage.

Answer

Fine-tune the model on the email data using Vertex AI, then delete the fine-tuned model after use.

Answer

Deploy the model in a private VPC and use Cloud VPN to encrypt all traffic between the application and the model endpoint.

Answer

Use Vertex AI with customer-managed encryption keys (CMEK) and enable VPC Service Controls.

Question 20

A financial services firm is deploying a generative AI model to answer customer questions about investment products. They need to ensure that the model's responses comply with regulatory requirements and do not provide personalized financial advice. Which approach should they take?

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

Accepted Answer

Cloud Data Loss Prevention (DLP) API. To automatically detect and redact PII from text, the Cloud Data Loss Prevention (DLP) API is the correct service. It can be integrated into the chatbot's processing pipeline to scan both incoming user messages and outgoing model responses, identifying and removing sensitive data according to predefined or custom infoTypes. This ensures regulatory compliance and protects user privacy.

Answer

Security Command Center

Answer

Cloud Armor

Answer

Cloud Identity-Aware Proxy (IAP)