Generative AI Leader Practice Test 8 — 10 Questions

Question 1

A company wants to build a customer support chatbot that answers based on internal documentation. They use Vertex AI Search and want to ensure the model only uses retrieved documents. What should they do?

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

Accepted Answer

Enable grounding with Vertex AI Search. Grounding with Vertex AI Search ensures the model's responses are strictly based on the retrieved documents from the internal documentation, preventing hallucination or reliance on pre-trained knowledge. Grounding works by providing the model with a search result context that it must use as the sole source for generating answers, effectively constraining the output to the provided documents.

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Fine-tune the model on the documentation

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Increase max output tokens

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Set temperature to 0.0

Question 2

After fine-tuning a model on customer support data, the model starts using profanity. What is the most effective mitigation?

Accepted Answer

Enable a safety attribute filter. Enabling a safety attribute filter is the most effective mitigation because it acts as a post-processing guardrail that blocks profanity at inference time, regardless of the model's training data. This is a standard practice in production LLM deployments, where safety filters (e.g., using keyword matching or classifier models) intercept and redact harmful outputs before they reach the user, providing immediate and reliable control without requiring retraining.

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Add profanity to training data as negative examples

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Reduce learning rate and retrain

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Increase temperature to reduce confidence

Question 3

A product manager wants to quickly build a conversational agent that can answer FAQs from the company's help center articles. They have limited coding experience. Which Google Cloud service is BEST suited for this task?

Accepted Answer

Vertex AI Agent Builder. Vertex AI Agent Builder is the best choice because it provides a no-code/low-code interface specifically designed for building conversational agents and search experiences. It allows the product manager to connect help center articles as a data source and automatically generate a FAQ-answering agent without writing code, making it ideal for someone with limited coding experience.

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Vertex AI Pipelines

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Vertex AI Studio

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Model Garden

Question 4

Which Google Cloud AI service provides a unified ML platform for building, deploying, and managing ML models in production?

Accepted Answer

Vertex AI. Vertex AI is the correct answer because it is Google Cloud's unified ML platform that integrates data engineering, data science, and ML engineering workflows into a single service. It provides end-to-end capabilities for building, training, deploying, and managing ML models in production, including AutoML, custom training, model registry, and MLOps features like continuous evaluation and monitoring.

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AI Platform

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BigQuery ML

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Cloud AutoML

Question 5

A marketing team wants to generate consistent brand-aligned social media posts using Vertex AI Studio. Which prompt engineering technique should they use to ensure the output tone matches their brand voice?

Accepted Answer

Provide a few-shot prompt with examples of previous brand-aligned posts. Providing a few-shot prompt with examples of previous brand-aligned posts (B) is the most effective technique because it shows the model concrete patterns of tone, style, and vocabulary, allowing it to generalize and produce consistent output. Few-shot prompting is ideal when the desired style is nuanced and hard to describe abstractly. This directly ensures the generated posts match the brand voice.

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Set the temperature to 0 and use a long system instruction

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Use a zero-shot prompt describing the brand voice

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Use chain-of-thought prompting to explain the reasoning behind each post

Question 6

An enterprise deploys a large language model (LLM) for internal document summarization. Users complain that summaries sometimes include statements not present in the original document. Which mitigation strategy should the team prioritize to address this hallucination issue?

Accepted Answer

Implement retrieval-augmented generation (RAG) to ground the model in the original documents and require citations.. Retrieval-Augmented Generation (RAG) is the most direct and effective mitigation for hallucination in document summarization because it forces the LLM to base its output on retrieved chunks of the original document. By requiring citations, the model must reference specific passages, making it verifiable and reducing the likelihood of fabricating content. This grounds the generation in the source material, addressing the root cause of hallucination—lack of factual grounding—rather than relying on post-hoc correction or output tuning.

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Train a discriminator model to detect hallucinations and perform adversarial training.

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Apply reinforcement learning from human feedback (RLHF) using a reward model that penalizes hallucinations.

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Reduce the model's temperature parameter to 0 to make outputs deterministic.

Question 7

A company is deploying a generative AI application using Vertex AI. They need to minimize latency for real‑time inference while maintaining high quality. Which TWO actions are most effective?

Accepted Answer

Use a smaller model like Gemini Flash. Option B is correct because Gemini Flash is a lower-latency, lighter-weight model in the Gemini family, purpose-built for high-throughput, real-time tasks, so it directly reduces inference latency while still delivering strong quality for most generative AI use cases. Option D is correct because output tokens are generated sequentially (autoregressive decoding), so capping max output tokens to the shortest acceptable length proportionally cuts generation time and thus end-to-end latency. Option A is not appropriate because batching requests improves throughput and cost efficiency, not per-request real-time latency, and can actually add queuing delay. Option C is wrong because Gemini Pro is larger and slower than Flash, trading latency for quality rather than minimizing latency. Option E is wrong because raising temperature to 1.0 only affects randomness/creativity of sampling and does not reduce latency.

Answer

Batch multiple inference requests together

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Use Gemini Pro instead of Gemini Flash to ensure quality

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Increase the temperature to 1.0 for more creative outputs

Question 8

A streaming platform uses a large generative model for personalized content suggestions. Budget constraints require minimizing inference costs without significantly degrading quality. Which approach is most effective?

Accepted Answer

Use a distilled version of the model.. Distillation trains a smaller 'student' model to mimic a larger 'teacher' model, reducing parameter count and inference latency while retaining most of the recommendation quality. This directly addresses the budget constraint by lowering compute and memory costs per inference, making it the most effective approach among the options.

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Deploy the model on higher-end accelerators to save time.

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Implement stronger safety filters to reduce output length.

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Cache frequent prompts to avoid regeneration.

Question 9

A team is fine-tuning a large language model on custom data using Vertex AI. They find that the training loss decreases but validation loss increases. What is the best course of action?

Accepted Answer

Reduce the model size or add dropout regularization.. The increasing validation loss while training loss decreases is a classic sign of overfitting, where the model memorizes the training data but fails to generalize. Reducing model size or adding dropout regularization directly combats overfitting by limiting the model's capacity or introducing noise during training, which forces the model to learn more robust features. This is the best course of action because it addresses the root cause without further exacerbating the problem.

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Increase the number of training epochs.

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Increase the learning rate.

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Switch to a smaller batch size.

Question 10

A machine learning engineer wants to reduce the latency of a Gemini-based chatbot running in production. Which TWO strategies would be MOST effective?

Accepted Answer

Switch from Gemini Pro to Gemini Flash. Option A is correct because Gemini Flash is a lighter, lower-latency model variant than Gemini Pro, so switching to it directly reduces the time the model takes to generate each response. Option B is correct because max_output_tokens caps the number of tokens the model generates, and since generation time scales roughly with output length, lowering this value shortens the response and thus the overall latency. Option C is not correct because streaming improves perceived latency by delivering tokens incrementally, but it does not reduce the actual total generation time. Option D is not correct because fine-tuning adapts the model to a task and does not inherently lower inference latency. Option E is not correct because raising temperature to 1.0 affects randomness and creativity, not speed, and can even make outputs longer or less predictable.

Answer

Enable streaming mode

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Fine-tune the model on the specific task

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Increase the temperature to 1.0