Generative AI Leader Practice Test 21 — 15 Questions

Question 1

A company is using Vertex AI Model Garden to deploy a foundation model for document summarization. They notice that the model sometimes generates summaries that include factual errors. They want to reduce hallucinations without sacrificing latency. Which approach should they try first?

Accepted Answer

Enable Vertex AI Grounding with a curated database of documents. Option C is correct because Grounding with a trusted knowledge base provides real-time fact verification with minimal latency impact. Option A is wrong because fine-tuning is time-consuming and may not eliminate hallucinations. Option B is wrong because safety filters do not address factual accuracy. Option D is wrong because increasing temperature increases randomness and hallucinations.

Answer

Increase the temperature parameter to make the model more confident

Answer

Add more safety filters to block uncertain responses

Answer

Fine-tune the model on a high-quality dataset of correct summaries

Question 2

An organization is building a search application using Vertex AI Vector Search. They have encoded their documents into embeddings and want to retrieve the most similar documents for a query. Which TWO actions are required to set up a Vector Search index?

Accepted Answer

Deploy the index to the IndexEndpoint.. A and D are correct: Creating an IndexEndpoint and deploying the index to an endpoint are necessary steps. B is not required because dimensions come from the model, not set here. C is done after deployment. E is for training, not serving.

Answer

Specify embedding dimension size in the index config.

Answer

Train a custom embedding model.

Answer

Download the query embeddings to local storage.

Question 3

A large enterprise is migrating their on-premise ML workloads to Vertex AI. They have a custom PyTorch model for text classification that they want to serve with minimal code changes. Which Vertex AI capability should they use for model serving?

Accepted Answer

Vertex AI Prediction with a custom container. Option C is correct because Vertex AI Prediction with a custom container allows them to package their PyTorch model with dependencies and serve it without rewriting code. Option A (Vertex AI Endpoints) is an older term; custom containers are the way. Option B (Vertex AI Model Garden) hosts pre-built models. Option D (Vertex AI Vector Search) is for embeddings, not classification.

Answer

Vertex AI Endpoints with a pre-built PyTorch runtime

Answer

Vertex AI Model Garden

Answer

Vertex AI Vector Search for approximate nearest neighbor

Question 4

A developer is using Vertex AI's Generative AI Studio to prototype a text summarization model. The initial results are too verbose. What is the most efficient way to adjust the output length without retraining?

Accepted Answer

Modify the prompt with specific length instructions and adjust model parameters. Option B is correct because adjusting parameters like max output tokens, temperature, and top_p directly controls verbosity in the prompt design. Option A is wrong because retraining is unnecessary. Option C is wrong because building a separate classifier adds complexity. Option D is wrong because switching to a smaller base model may not yield desired quality.

Answer

Switch to a smaller base model like BERT

Answer

Use a separate classifier to filter long responses

Answer

Fine-tune the model with a dataset of concise summaries

Question 5

A retailer is building a product recommendation chatbot using Vertex AI Agent Builder. They want the agent to answer questions about product availability, prices, and promotions, but also to escalate to a human agent when the query is complex. What should they configure in Agent Builder?

Accepted Answer

Define an agent with a 'handoff to human' intent and configure the corresponding flow. Option A is correct because Agent Builder allows defining conversation flows with escalation to a live agent. Option B (generative fallback) only handles unknown queries, not escalation. Option C (tool integration) is for external APIs, not human takeover. Option D (playbooks) define steps but not escalation triggers.

Answer

Create a playbook with a step that transfers to a human via a webhook

Answer

Integrate a tool that calls a human support API when confidence is low

Answer

Use Vertex AI Agent Builder's generative fallback to automatically escalate

Question 6

A company deploys a Gemini model on Vertex AI for a healthcare application. They need to ensure that the model does not generate medical advice and that responses are grounded in trusted medical sources. Which combination of safety measures should they implement?

Accepted Answer

Enable safety filters and use Vertex AI Grounding with a labeled medical dataset. Option D is correct because safety filters and grounded generation with a labeled dataset ensure compliance. Option A is wrong because fine-tuning alone does not enforce safety filters. Option B is wrong because disabling grounding increases hallucination risk. Option C is wrong because using a public dataset without safety filters is risky.

Answer

Use Vertex AI Grounding with a public dataset and disable safety filters

Answer

Enable safety filters only, without grounding

Answer

Fine-tune the model on a curated medical dataset and disable safety filters for faster responses

Question 7

A global e-commerce company uses Vertex AI Gemini API for real-time product description generation. They observe that sometimes the model generates text in a language other than the user's language, despite being prompted in English. They need to ensure output language consistency. Which approach is most effective?

Accepted Answer

Configure a safety filter that blocks non-English text. Option C is correct because using a safety filter with language detection blocks unintended languages. Option A (setting the generation config language parameter) is not directly available in Gemini API. Option B (fine-tuning for language detection) is overkill. Option D (post-processing with translation) is reactive and adds latency.

Answer

Set the language parameter in the generation config to 'en'

Answer

Fine-tune the model on a dataset of English-only product descriptions

Answer

Run a language detection model on the output and regenerate if not English

Question 8

A marketing team wants to use Vertex AI to generate ad copy. They need the model to follow a specific tone and style. What is the best approach?

Accepted Answer

Provide few-shot examples in the prompt and adjust temperature. Option A is correct because prompt engineering with examples is the most flexible and quick method. Option B is wrong because fine-tuning requires large labeled datasets. Option C is wrong because grounding retrieves external info, not tone. Option D is wrong because safety filters do not control style.

Answer

Use Vertex AI Grounding to retrieve style guides

Answer

Fine-tune the model on a dataset of past ad copy

Answer

Enable safety filters to enforce brand guidelines

Question 9

A startup wants to deploy a custom-tuned large language model for real-time inference on Vertex AI. They need the lowest possible latency for end users. What deployment strategy should they choose?

Accepted Answer

Deploy the tuned model to a Vertex AI endpoint with GPU acceleration and autoscaling.. Option A is correct: a dedicated endpoint with GPU ensures low latency. Option B (batch prediction) is for asynchronous tasks. Option C (Cloud Functions) adds overhead. Option D (Model Garden with PaLM 2) does not allow custom model deployment.

Answer

Use Vertex AI Model Garden to deploy the base PaLM 2 model.

Answer

Wrap the model in a Cloud Function and invoke via HTTP.

Answer

Use Vertex AI Batch Prediction to process requests in batches.

Question 10

A company is building a customer service chatbot using Vertex AI Agent Builder. The chatbot needs to answer questions based on a large internal knowledge base stored in a Cloud Storage bucket. The team wants to ensure the model can reference the latest documents without fine-tuning. Which configuration should they use?

Accepted Answer

Ground the model with a Vertex AI Search data store connected to the Cloud Storage bucket. Option C is correct because Vertex AI Agent Builder can use grounding with Cloud Storage to dynamically retrieve information from documents without fine-tuning. Option A is wrong because using a pre-built model without retrieval would not incorporate the knowledge base. Option B is wrong because fine-tuning is not needed and would require retraining. Option D is wrong because exporting to BigQuery adds unnecessary complexity.

Answer

Fine-tune a model on the knowledge base documents

Answer

Use a pre-built model with no additional configuration

Answer

Store the documents in BigQuery and use a BigQuery connector

Question 11

A machine learning engineer is tuning a large language model on Vertex AI for question answering. They want to evaluate the model's performance before deployment. Which THREE metrics should they consider?

Accepted Answer

F1 score. Options A, B, and D are correct for evaluation. Option C is wrong because training time is not a performance metric. Option E is wrong because cost per epoch is a cost metric, not performance.

Answer

Cost per training epoch

Answer

Training time per epoch

Question 12

A startup wants to embed generative AI features into their mobile app but has limited ML expertise. Which Google Cloud service is best suited for rapid integration with no ML training?

Accepted Answer

Gemini API. Option C is correct because the Gemini API provides a simple REST API endpoint without requiring ML expertise. Option A is wrong because Vertex AI Model Garden still requires some ML knowledge. Option B is wrong because Vertex AI Agent Builder is more complex. Option D is wrong because Cloud Run is a compute service, not AI-specific.

Answer

Vertex AI Model Garden

Answer

Vertex AI Agent Builder

Answer

Cloud Run with a custom container

Question 13

A retail company is building a chatbot for customer service. They need the model to generate product descriptions based on a catalog but also answer questions about store policies. The team wants to minimize latency and cost while maintaining high accuracy. Which Google Cloud generative AI offering should they use?

Accepted Answer

Vertex AI Gemini API. Option B is correct because Gemini is a multimodal model that can handle both product descriptions (text) and policy questions, and Vertex AI offers optimized inference for latency and cost. Option A (Imagen) is for image generation, not text. Option C (Codey) is for code generation. Option D (PaLM 2 via Model Garden) is possible but Gemini is more modern and efficient.

Answer

Vertex AI Model Garden with PaLM 2

Answer

Vertex AI Imagen

Answer

Vertex AI Codey APIs

Question 14

A company is deploying a large language model on Vertex AI for real-time inference. They observe high latency and want to optimize. They have already enabled model caching. What next step should they take to reduce latency?

Accepted Answer

Apply model quantization to reduce precision. Option D is correct because reducing model precision (e.g., int8 quantization) can significantly reduce compute time and latency. Option A is wrong because increasing batch size increases latency per request. Option B is wrong while scaling predicts may help, it's not a model optimization. Option C is wrong because using a larger model increases latency.

Answer

Add more GPUs to the prediction endpoint

Answer

Use a larger, more accurate model variant

Answer

Increase the batch size for inference requests

Question 15

A media company is using Vertex AI Imagen to generate marketing images. The output frequently contains unrealistic artifacts, especially in human faces. The team has fine-tuned the model using their brand assets. What is the most likely cause and recommended fix?

Accepted Answer

The fine-tuning dataset is too small or too homogeneous; augment and diversify the training data.. Option D is correct. Overfitting to a small dataset can cause artifacts. Common symptoms include unrealistic details. Option A is less likely because safety filters usually block rather than degrade quality. Option B (low inference steps) could cause quality loss but typically not specific artifacts. Option C (negative prompt) might help but is not the root cause.

Answer

Safety filters are too aggressive; reduce them.

Answer

Negative prompts are missing; always include 'unrealistic'.

Answer

Inference steps are too low; increase to 100.