Generative AI Leader Google Cloud's Generative AI Offerings Practice Test 9 — 15 Questions

Question 1

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?

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

Accepted Answer

Vertex AI Gemini API. The Vertex AI Gemini API provides a single, unified multimodal model capable of both generating product descriptions from a catalog and answering policy questions, while offering optimized latency and cost through features like context caching and streaming. Unlike specialized models, Gemini's architecture handles diverse natural language tasks without requiring multiple endpoints, reducing operational overhead and inference time.

Answer

Vertex AI Model Garden with PaLM 2

Answer

Vertex AI Imagen

Answer

Vertex AI Codey APIs

Question 2

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.. Unrealistic artifacts in fine-tuned generative models, especially in human faces, typically stem from a training dataset that is too small or lacks diversity. When the dataset is homogeneous, the model overfits to limited patterns and fails to generalize, leading to distorted outputs. Augmenting and diversifying the training data with varied poses, lighting, and ethnicities helps the model learn robust facial features.

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.

Question 3

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.. Deploying the custom-tuned model to a Vertex AI endpoint with GPU acceleration and autoscaling is the best choice for lowest-latency real-time inference. A dedicated endpoint keeps the model loaded and ready to serve requests, avoiding the per-request startup overhead of serverless wrappers like Cloud Functions. GPU acceleration speeds up the model's forward-pass computation, and autoscaling adds or removes replicas to match traffic so that requests are not queued behind insufficient capacity. Batch prediction (D) is designed for high-throughput offline jobs and is not suitable for interactive, low-latency use cases.

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 4

Which TWO of the following are capabilities of Vertex AI Model Garden? (Choose 2)

Accepted Answer

Access to a curated set of foundation models like PaLM and Gemini.. Option D is correct because Vertex AI Model Garden provides access to a curated catalog of foundation models, including Google's PaLM and Gemini models, which users can discover and use directly. Option E is correct because Model Garden allows users to fine-tune and deploy foundation models, supporting customization and serving within Vertex AI. Option A is incorrect because generating code snippets is a generative AI application capability, not a core capability of Model Garden itself. Option B is incorrect because text-to-image generation is a specific model use case, not a defining capability of Model Garden. Option C is incorrect because deploying custom container images is a Vertex AI Prediction feature, not a Model Garden capability.

Answer

Generate code snippets for common programming tasks.

Answer

Ability to generate images from text descriptions.

Answer

Deploy custom container images for model serving.

Question 5

A healthcare startup is using Vertex AI Imagen to generate synthetic medical images for training a diagnostic model. The images must comply with HIPAA regulations and cannot contain any real patient data. The team fine-tuned Imagen on a dataset of de-identified medical scans. However, during testing, they notice that some generated images closely resemble specific patients from the original dataset, even though the dataset was de-identified. They suspect that the model memorized some training examples. The team needs to address this issue without losing image quality. They have access to the original training data and Vertex AI tools. What action should they take?

Accepted Answer

Re-tune the model using differential privacy (DP-SGD) to prevent memorization of individual examples.. Differential privacy (DP-SGD) during fine-tuning adds calibrated noise to the gradient updates, which mathematically bounds the model's ability to memorize any single training example. This directly addresses the memorization issue while preserving the utility of the generated images, as the noise is carefully controlled to maintain overall image quality. Vertex AI supports DP-SGD through its custom training infrastructure, making it a practical choice for HIPAA-compliant medical imaging.

Answer

Use a post-processing step to blur or distort generated images.

Answer

Increase the size of the training dataset by adding more synthetic images.

Answer

Apply stricter output safety filters to block images that look like any known patient.

Question 6

A marketing agency wants to use Vertex AI to automatically generate social media posts for clients. They plan to use the Gemini API with few-shot prompting. The agency's developers have limited experience with generative AI and want the fastest way to prototype and iterate on prompts. They are already using Google Cloud for other services. Which approach should they take to quickly develop and test prompts?

Accepted Answer

Use Vertex AI Studio (Gen AI Studio) to design and test prompts interactively.. Vertex AI Studio (Gen AI Studio) is the correct choice because it provides a no-code, interactive environment specifically designed for rapid prompt engineering and iteration with Gemini models. It allows developers with limited generative AI experience to test few-shot prompts, adjust parameters, and see results immediately without writing code, making it the fastest path from concept to working prototype within the Google Cloud ecosystem.

Answer

Use a third-party platform like OpenAI Playground and migrate later.

Answer

Use Google Cloud Shell to invoke the model via curl commands.

Answer

Write Python scripts using the Vertex AI SDK and run them in Airflow.

Question 7

An e-commerce company is using Vertex AI PaLM 2 for Text (via Model Garden) to generate product descriptions. They have an existing pipeline that calls the model with a prompt including product attributes. Recently, they migrated to the Gemini API. The team notices that the Gemini model sometimes outputs descriptions that are factually inconsistent with the input (e.g., wrong color or size). This was less frequent with PaLM 2. They have not changed the prompts. What is the most likely cause and solution?

Accepted Answer

Adjust the prompt to be more explicit about adhering to the input data, and reduce the temperature.. The core issue is that the prompt, originally optimized for PaLM 2, may not be sufficiently explicit for the Gemini model's different instruction-following behavior. By making the prompt more explicit about adhering strictly to the input data and reducing the temperature (e.g., to 0.2 or lower), the model's output becomes more deterministic and less prone to hallucinating incorrect attributes. This directly addresses the factual inconsistency without changing the model family, leveraging Gemini's ability to follow detailed instructions when properly guided.

Answer

Revert to PaLM 2 since it was more reliable for this task.

Answer

Add negative prompts to discourage incorrect facts.

Answer

Increase the model's temperature to make outputs more deterministic.

Question 8

A multinational corporation is using Vertex AI to generate multilingual customer support responses. They have fine-tuned the Gemini model on support tickets in English and now want to extend to 10 additional languages. The fine-tuning dataset for new languages is small (1000 tickets each). During evaluation, the model performs well for common languages (Spanish, French) but poorly for languages like Finnish and Thai. The team needs to improve performance for low-resource languages. They have budget constraints and cannot collect more data quickly. Which approach should they take?

Accepted Answer

Use a multilingual foundation model and fine-tune with cross-lingual transfer learning techniques.. Using a multilingual foundation model (like Gemini's multilingual variant) with cross-lingual transfer learning leverages the model's pre-trained knowledge across languages, allowing it to generalize from high-resource languages (Spanish, French) to low-resource ones (Finnish, Thai) even with small fine-tuning datasets. This approach is budget-friendly as it avoids separate models or costly data collection, and it directly addresses the performance gap by sharing linguistic patterns across languages.

Answer

Switch to Vertex AI Codey API for generating responses in all languages.

Answer

Deploy separate fine-tuned models for each language.

Answer

Collect more training data for low-resource languages via crowdsourcing.

Question 9

A gaming company is using Vertex AI Imagen to create concept art. They have a stable pipeline that generates images based on text prompts. Recently, they introduced a new feature: using a reference image to guide the style (image-to-image generation). However, when using a reference image, the generated images often have unnatural color shifts and artifacts. The team suspects that the reference image is being resized to a resolution that the model wasn't trained on. They are using the default Imagen settings. What is the most likely cause and the best solution?

Accepted Answer

The reference image is being resized to a non-standard aspect ratio; preprocess the image to the recommended resolution and aspect ratio.. The default Imagen settings expect input images at specific resolutions (e.g., 256x256, 512x512, or 1024x1024) and a 1:1 aspect ratio. When a reference image is resized to a non-standard resolution or aspect ratio, the model's internal processing can introduce artifacts and unnatural color shifts due to misalignment with its training distribution. Preprocessing the image to the recommended resolution and aspect ratio ensures the model operates within its optimal input space, eliminating these issues.

Answer

Increase the number of inference steps to improve detail.

Answer

Reduce the style weight in the image-to-image prompt.

Answer

Switch to a different image generation model.

Question 10

A company needs to fine-tune a foundation model on Vertex AI for a custom text classification task with only 500 labeled examples. They want to minimize cost while achieving high accuracy. What is the MOST cost-effective approach?

Accepted Answer

Design prompts with few-shot examples and test it with the available data.. The most cost-effective because it leverages prompt engineering with few-shot examples, which requires no training or infrastructure costs. With only 500 labeled examples, a well-designed prompt can often achieve high accuracy for custom text classification without the expense of fine-tuning or model distillation on Vertex AI.

Answer

Fine-tune the foundation model using full fine-tuning on the entire dataset.

Answer

Use model distillation to train a smaller student model.

Answer

Use Vertex AI LLM-based evaluation to compare multiple large models and select the best one.

Question 11

Which THREE capabilities are provided by Vertex AI Agent Builder? (Choose three.)

Accepted Answer

Integration with Dialogflow CX for conversational flows.. Vertex AI Agent Builder integrates with Dialogflow CX to enable the design of sophisticated conversational flows, including state management, conditional logic, and multi-turn interactions. This allows developers to build agents that can handle complex dialogues with branching paths, leveraging Dialogflow CX's visual flow builder and fulfillment capabilities within the Vertex AI ecosystem.

Answer

Automated model hyperparameter tuning.

Answer

Support for multimodal (text, image, video) input processing in agents.

Question 12

A software company is using Vertex AI to build a generative AI application that creates code snippets from natural language descriptions. They want to improve the model's performance on their specific coding style and libraries. Which two techniques should they use? (Choose two.)

Accepted Answer

Use prompt engineering with few-shot examples of the desired code style.. Fine-tuning on a custom dataset and using few-shot prompt engineering are both effective ways to adapt a generative model to a specific coding style. Fine-tuning provides deep customization, while few-shot prompting offers a lightweight alternative. Together, they can significantly improve the relevance and accuracy of generated code.

Answer

Deploy the model on a larger GPU to increase generation speed.

Answer

Use Vertex AI Model Evaluation to compare different models and select the best one.

Answer

Increase the model's temperature setting to encourage more creative code.

Question 13

A media company wants its internal knowledge assistant to answer employee questions using the company's own policy documents and past project reports, while keeping the Gemini model's general reasoning ability intact. The team has a large corpus stored in Cloud Storage and does not want to retrain or fine-tune the model. Which Google Cloud approach should they use?

Accepted Answer

Use Vertex AI Search with a data store pointing to the Cloud Storage corpus, and ground Gemini responses on the retrieved documents.. Grounding Gemini with Vertex AI Search connects the model to the company's own documents at query time, so responses reflect current policy and project material while the base model's general capabilities remain untouched. Because retrieval is separate from model weights, updating the corpus requires no retraining, which matches the team's stated constraint.

Answer

Deploy Gemini on a larger machine type with more GPU memory so the model can ingest the full document corpus at runtime.

Answer

Increase the Gemini model's temperature setting so it can explore the policy documents more broadly at inference time.

Answer

Fine-tune Gemini on the policy documents and past project reports using Vertex AI supervised tuning.

Question 14

A media company wants to create a custom AI assistant that can answer employee questions by referencing internal policy documents stored in Google Drive. They need a low-code solution that allows them to configure the assistant, connect data sources, and deploy it for internal use. Which Google Cloud offering should they use?

Accepted Answer

Vertex AI Agent Builder. Vertex AI Agent Builder is the only Google Cloud offering that provides a low-code environment specifically for building generative AI agents and applications. It allows integration with Google Drive and other data sources, enabling the assistant to retrieve and use internal policy documents to answer employee questions. The other services are either search-focused, productivity-focused, or document-processing-focused, and lack the agent orchestration and deployment features required.

Answer

Document AI

Answer

Gemini for Google Workspace

Answer

Vertex AI Search

Question 15

A small marketing team wants to use a Google Cloud generative AI model to generate creative text for social media posts. They prefer a fully managed, ready-to-use API that requires minimal setup and does not need model training. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Gemini API. The Vertex AI Gemini API offers direct access to powerful generative models without any training or infrastructure management. It is ideal for tasks like creative writing, where the team can simply send prompts and receive generated text. The other services are either for custom model training, image analysis, or conversational agents, and do not provide the same ease of use for text generation.

Answer

AutoML Natural Language

Answer

Vision AI

Answer

Dialogflow CX