Generative AI Leader Practice Test 23 — 15 Questions

Question 1

A data scientist observes that their text generation model frequently uses gender stereotypes when generating job descriptions. Which responsible AI practice should be applied FIRST to address this issue?

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

Accepted Answer

Evaluate the model's outputs for bias using a diverse test set that includes gender-neutral prompts. The first step in addressing model bias is to evaluate and measure the bias using a diverse, representative test set. This diagnostic step identifies the specific types and severity of gender stereotypes before any mitigation technique is applied, ensuring that subsequent interventions are targeted and effective. Without this evaluation, any corrective action risks being misapplied or introducing new biases.

Answer

Fine-tune the model on a dataset with more male-dominated job descriptions

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Apply SynthID watermarking to the generated content

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Increase the number of safety filters to block all gender-related content

Question 2

A media company uses Vertex AI to generate video captions. The generated captions sometimes contain factual errors about named entities (e.g., actor names). Which technique would most likely reduce these errors?

Accepted Answer

Use Vertex AI grounding with a knowledge base of verified entities. Vertex AI grounding connects the model to a knowledge base of verified entities, allowing it to retrieve authoritative facts during generation. This reduces hallucinations about named entities by constraining outputs to validated data rather than relying solely on the model's parametric knowledge.

Answer

Enable response caching

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Increase the temperature parameter

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Decrease top_p to 0.3

Question 3

A healthcare startup uses a generative model fine-tuned on general medical literature to provide preliminary diagnostic suggestions from patient text. The model frequently misses rare diseases and sometimes suggests common conditions that are unlikely given the symptoms. The startup has a curated dataset of rare disease case reports and wants to improve the model’s sensitivity to rare conditions without sacrificing overall accuracy. They cannot afford to retrain the entire model from scratch. The model is deployed on Vertex AI Prediction with low latency requirement. Which approach should they take?

Accepted Answer

Implement a human-in-the-loop system: for outputs with low confidence or suspected rare disease, route to a human expert.. Implementing a human-in-the-loop process for rare disease flags combines AI with expert review, catching misses while maintaining speed for common cases. Option A is wrong because prompt engineering alone may not teach the model about rare diseases. Option B is wrong because increasing top-p restricts vocabulary but doesn't inject knowledge. Option C is wrong because fine-tuning again might cause catastrophic forgetting of common conditions.

Answer

Perform continued fine-tuning on the rare disease dataset using a low learning rate.

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Add a system prompt instructing the model to consider rare diseases more carefully.

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Reduce top-p sampling to focus on high-probability tokens, assuming rare diseases have lower probability.

Question 4

What is the purpose of grounding in Vertex AI?

Accepted Answer

To connect model outputs to verifiable sources. Grounding in Vertex AI connects model outputs to verifiable, external sources of information (such as Google Search, enterprise data sources, or third-party databases) to reduce hallucinations and improve factual accuracy. By referencing grounded sources, the model can provide citations and allow users to verify claims, which is critical for enterprise applications requiring trust and compliance.

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To improve training speed

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To reduce model size for faster inference

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To enable multi-modal inputs

Question 5

A company wants to generate images from text descriptions. Which model in Vertex AI Model Garden should they use?

Accepted Answer

Imagen. Imagen is the correct choice because it is a text-to-image model in Vertex AI Model Garden specifically designed to generate high-fidelity images from natural language descriptions. Unlike the other options, Imagen uses a diffusion-based architecture to create photorealistic visuals, making it the only option that directly addresses the requirement of generating images from text.

Answer

Chirp

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Codey

Answer

PaLM 2

Question 6

A marketing team wants to generate social media posts using generative AI. They need the tone to be consistent with their brand voice. Which two prompt engineering techniques should they use? (Choose TWO)

Accepted Answer

Use few-shot examples of approved posts. Providing a style guide in the system prompt and using few-shot examples are effective techniques to enforce brand voice. Random examples and negative phrasing are not recommended. Maximum tokens does not affect tone.

Answer

Set maximum output tokens to a low value

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Use negative prompts like 'do not be casual'

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Set high temperature to encourage creativity

Question 7

A data science team wants to build a RAG pipeline to ground a chatbot in proprietary knowledge. They need to choose a vector database and embedding model. Which combination is NATIVELY integrated with Vertex AI and requires the least custom infrastructure?

Accepted Answer

Vertex AI Embeddings API + Vertex AI Vector Search. Vertex AI Embeddings API and Vertex AI Vector Search are both native, fully managed services within the Vertex AI ecosystem, requiring zero custom infrastructure for deployment. The Embeddings API generates text embeddings directly, and Vector Search provides a scalable, low-latency vector database that integrates seamlessly without additional servers or third-party tools.

Answer

TensorFlow Embedding Projector + BigQuery

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Custom embeddings using a BERT model + Elasticsearch

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Vertex AI Embeddings API + Pinecone

Question 8

An e-commerce company is using a generative AI model to write product descriptions. They want to ensure that the model does not generate harmful content such as hate speech or violence. Which Google Cloud feature should they configure?

Accepted Answer

Vertex AI Safety Filters. Google Cloud's Vertex AI provides built-in safety filters that can be configured to block harmful content categories like hate speech, violence, sexual content, and dangerous instructions.

Answer

Cloud DLP (Data Loss Prevention)

Answer

Vertex AI Explainable AI

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Vertex AI Model Monitoring

Question 9

A startup with limited ML expertise wants to add a GenAI feature to their SaaS application that can generate personalized email drafts for users. They need fast time-to-market and low maintenance. Which build-vs-buy decision is BEST?

Accepted Answer

Buy a pre-built API such as the Gemini API and integrate it with prompt engineering for personalization. Buying a pre-built API like the Gemini API and using prompt engineering for personalization gives the startup fast time-to-market with minimal ML expertise and low maintenance, since Google manages the model infrastructure. Prompt engineering can tailor email drafts without training or fine-tuning, aligning with the need for speed and low operational overhead.

Answer

Select a model from Model Garden and deploy it on Vertex AI

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Fine-tune an open-source model on a corpus of email drafts to create a custom model

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Build a custom transformer model from scratch

Question 10

A company is migrating a GenAI proof-of-concept to production. During the pilot, they used a large model (e.g., Gemini 1.5 Pro) and incurred high costs. The use case is simple: generating short product descriptions from structured data. Which cost optimization strategy should they implement first?

Accepted Answer

Switch to a smaller model like Gemini 1.5 Flash and use structured prompts. The primary cost driver in this scenario is the model size itself. Since the use case is simple (generating short product descriptions from structured data), a smaller model like Gemini 1.5 Flash can handle the task with significantly lower inference cost per token. Structured prompts further optimize by reducing token waste and ensuring consistent output, making this the most direct and impactful first step.

Answer

Fine-tune a smaller model on the specific task

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Implement batch processing to group requests

Answer

Reduce the model's temperature to 0.0

Question 11

A company needs to deploy a chatbot on a mobile device that must work offline. They want to use Gemini for natural language understanding but need minimal latency and no cloud dependency. Which Gemini model variant is most appropriate?

Accepted Answer

Gemini Nano. Gemini Nano is the most appropriate variant because it is specifically designed for on-device deployment, enabling offline operation with minimal latency. It is optimized for mobile devices through quantization and efficient architecture, allowing natural language understanding without any cloud dependency.

Answer

Gemini Flash

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Gemini Pro

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Gemini Ultra

Question 12

An organization is planning to roll out a generative AI internal knowledge base assistant to employees. They want to ensure adoption and manage change effectively. Which two change management practices should they prioritize? (Choose TWO)

Accepted Answer

Provide training sessions on how to write effective prompts. Effective prompt engineering is critical for generative AI assistants; without training, employees may produce vague or poorly structured prompts, leading to irrelevant or low-quality responses, which undermines adoption. Providing training on prompt writing directly addresses the skill gap and empowers users to leverage the assistant effectively.

Answer

Roll out to all employees on day one to maximize impact

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Mandate usage of the assistant for all employees

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Disable the assistant after two weeks if usage is low

Question 13

A team is using Vertex AI Pipelines to deploy a generative AI model for real-time inference. The model sometimes generates harmful content. They want to implement a safety filter that checks the output before returning it to the user, but they need to minimize latency. Which approach best balances safety and performance?

Accepted Answer

Use a secondary lightweight classifier to filter outputs in real-time.. Deploying a secondary lightweight classifier (e.g., a distilled BERT or a small logistic regression model) as a post-processing filter allows real-time inference with minimal latency overhead. This approach decouples safety from the primary generative model, enabling fast rejection of harmful outputs without retraining or blocking the main inference pipeline.

Answer

Retrain the model on every flagged harmful output.

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Manually review all outputs before delivery.

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Disable safety checks to improve latency.

Question 14

A company is using a large language model for automated translation of legal contracts. They find that the translations sometimes alter the meaning of specific clauses. Which TWO approaches would most effectively preserve the original meaning? (Choose two.)

Accepted Answer

Fine-tune the model on a parallel corpus of legal translations.. Fine-tuning on a parallel corpus of legal translations adapts the model's weights to the specific legal domain, improving fidelity for legal terminology and clause structures by learning from ground-truth translations. In addition, providing a glossary of key legal terms with their approved translations constrains the model to use consistent, correct terminology, reducing meaning-altering substitutions. Together, these two approaches most effectively preserve the original meaning. Prompt engineering alone (Option A) and parameter tweaks such as top-p or higher temperature (Options B and E) do not address the root cause of semantic drift.

Answer

Provide the full contract context in a single prompt.

Answer

Set top-p=0.1 to limit the vocabulary to the most likely tokens.

Answer

Increase the temperature to allow more creative phrasing.

Question 15

Which THREE strategies should be combined to effectively reduce biased outputs in a generative AI model? (Choose three.)

Accepted Answer

Implement safety filters targeting hate speech and stereotypes.. Implementing safety filters targeting hate speech and stereotypes directly blocks the generation of biased or harmful content at the output layer. These filters use predefined rule sets or trained classifiers to detect and suppress language that reflects demographic or cultural biases, reducing the risk of the model producing offensive or stereotypical responses.

Answer

Raise the temperature to increase output variability.

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Fine-tune the model on a biased dataset to learn patterns.