Generative AI Leader Google Cloud's Generative AI Offerings Practice Test 7 — 15 Questions

Question 1

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 2

Which TWO factors are most important when choosing a base foundation model for fine-tuning on a domain-specific task?

Accepted Answer

Model size and architecture. Options A and D are correct. Model size/architecture affects capability and cost; training data relevance ensures domain knowledge transfer. Option B (model license) is less critical for fine-tuning feasibility. Option C (popularity) is not a technical factor. Option E (inference latency) can be optimized post-fine-tuning, but choice of base model matters less.

Answer

Model popularity in the developer community

Answer

Model license (open-source vs. proprietary)

Answer

Inference latency of the base model

Question 3

Which THREE steps are required to secure a generative AI pipeline that uses Vertex AI and involves sensitive customer data?

Accepted Answer

Use VPC Service Controls to create a perimeter around Vertex AI resources. Options A, B, and E are correct. Data encryption at rest protects stored data; VPC Service Controls prevent data exfiltration; IAM with least privilege controls access. Option C (public endpoint with API key) is insecure. Option D (disable audit logging) reduces security visibility.

Answer

Expose the prediction endpoint publicly with an API key

Answer

Disable audit logging to reduce data exposure

Question 4

Which THREE benefits does Vertex AI Agent Builder provide over building a custom conversational agent from scratch?

Accepted Answer

Automatic scaling and load balancing. Options B, C, and E are correct. Pre-built grounding with your data reduces development effort; built-in safety filters ensure compliance; automatic scaling handles traffic without manual ops. Option A (full control over ML models) is more true for custom builds. Option D (lower latency) is not guaranteed; custom builds can optimize latency.

Answer

Full control over the underlying ML model architecture

Answer

Guaranteed lower inference latency

Question 5

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 6

A data scientist wants to generate realistic product images for an online catalog using Google Cloud's generative AI. Which service should they use?

Accepted Answer

Imagen on Vertex AI. Option B is correct because Imagen is Google Cloud's image generation model. Option A is wrong because Gemini is a multimodal model but not specialized for image generation. Option C is wrong because Codey is for code generation. Option D is wrong because Vertex AI Model Garden is a model repository, not a specific generation service.

Answer

Codey API for code generation

Answer

Gemini API with text-to-text prompts

Answer

Vertex AI Model Garden without a specific model

Question 7

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 8

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 9

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 10

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 11

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 12

A developer wants to generate Python code using Google Cloud's generative AI. Which model should they invoke?

Accepted Answer

Codey. Option A is correct because Codey is Google Cloud's specialized code generation model. Option B is wrong because Imagen generates images. Option C is wrong because Chirp is for audio. Option D is wrong because Meena is a chatbot, not code-focused.

Answer

Chirp

Answer

Imagen

Answer

Meena

Question 13

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 14

A company is building a generative AI application that must adhere to strict data residency regulations. Which TWO Google Cloud features can help ensure that data does not leave a specific geographic region?

Accepted Answer

Using the regional endpoint for Vertex AI. Options B and D are correct. Dedicated VMs and the regional endpoint for Vertex AI ensure data stays in a specified region. Option A is wrong because model caching may use regional resources but does not enforce residency. Option C is wrong because Cloud CDN distributes data globally. Option E is wrong because Global Load Balancer routes traffic globally.

Answer

Vertex AI Model Caching

Answer

Global load balancer with Cloud Armor

Answer

Cloud CDN for content delivery

Question 15

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