Generative AI Leader Google Cloud's Generative AI Offerings Practice Test 8 — 15 Questions

Question 1

A developer wants to use Gemini 1.5 Pro to analyze hour-long video content and generate a summary. Which feature of Gemini 1.5 Pro is most suitable for this task?

Accepted Answer

Long context window (up to 1 million tokens). Gemini 1.5 Pro's long context window of up to 1 million tokens allows it to process and analyze hour-long video content in a single pass, including both audio and visual frames. This capability is essential for generating a coherent summary of long-form video, as it can retain and reason over the entire video's context without needing to chunk or downsample the content.

Answer

Multimodal generation from text and images

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Code generation and debugging

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Function calling to retrieve external data

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. Model size and architecture directly determine the capacity for learning domain-specific patterns during fine-tuning. Larger models with more parameters can capture nuanced relationships, while architecture choices like transformer depth or attention mechanisms affect how well the model adapts to specialized tasks. For domain-specific fine-tuning, the base model must have sufficient representational power to avoid catastrophic forgetting and to generalize effectively within the target domain.

Answer

Model popularity in the developer community

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Model license (open-source vs. proprietary)

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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. VPC Service Controls are required to create a service perimeter around Vertex AI resources, preventing data exfiltration by restricting data movement across the perimeter boundary. This is critical for sensitive customer data because it mitigates the risk of unauthorized access or leakage, even from within the same project or organization.

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. Vertex AI Agent Builder provides automatic scaling and load balancing as a managed service, handling infrastructure provisioning and traffic distribution across multiple instances without manual intervention. This eliminates the need to configure Kubernetes clusters or load balancers yourself, which is required when building a custom conversational agent from scratch.

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Full control over the underlying ML model architecture

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Guaranteed lower inference latency

Question 5

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. Imagen on Vertex AI is Google Cloud's specialized service for generating high-quality, photorealistic images from text prompts. It is built on diffusion models and is directly designed for image generation tasks, making it the correct choice for creating product images for an online catalog.

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Codey API for code generation

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Gemini API with text-to-text prompts

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Vertex AI Model Garden without a specific model

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. It combines two essential safety layers: safety filters block harmful content (including medical advice), and Vertex AI Grounding anchors responses to a labeled medical dataset, ensuring factual accuracy and compliance with healthcare regulations. This dual approach prevents the model from generating unverified or dangerous medical information while maintaining relevance to trusted sources.

Answer

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

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Enable safety filters only, without grounding

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

Question 7

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

Accepted Answer

Codey. Codey is Google Cloud's family of models specifically designed for code generation, completion, and chat, built on the PaLM 2 architecture and fine-tuned on code-heavy datasets. For a developer needing to generate Python code, Codey is the correct choice because it is purpose-built for code-related tasks, unlike other models that specialize in different modalities.

Answer

Chirp

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Imagen

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Meena

Question 8

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. Vertex AI Grounding connects the model to a curated database of documents, allowing it to retrieve and cite factual information in real-time. This directly reduces hallucinations by grounding responses in verified sources without adding significant latency, as the retrieval step is optimized for speed. Other approaches either increase latency (fine-tuning), reduce output quality (temperature increase), or do not address factual accuracy (safety filters).

Answer

Increase the temperature parameter to make the model more confident

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Add more safety filters to block uncertain responses

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

Question 9

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. The F1 score is a key metric for evaluating question-answering models because it measures the harmonic mean of precision and recall, capturing how well the model's predicted answer overlaps with the ground truth answer. In the context of Vertex AI and large language models, F1 score is particularly useful when answers are not exact matches but contain partial correct information, making it a standard evaluation metric for extractive and generative QA tasks.

Answer

Cost per training epoch

Answer

Training time per epoch

Question 10

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.

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Negative prompts are missing; always include 'unrealistic'.

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Inference steps are too low; increase to 100.

Question 11

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.. Vertex AI Model Garden provides a curated repository of Google's foundation models, including PaLM 2, Gemini, and other first-party and third-party models, allowing users to discover, test, and deploy them directly from the Google Cloud console. This access is a core capability of Model Garden, enabling rapid experimentation with pre-trained models without requiring custom model building.

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 12

Which THREE of the following are features of Vertex AI Studio (Gen AI Studio)? (Choose 3)

Accepted Answer

Compare responses from different models side-by-side.. Vertex AI Studio (formerly Gen AI Studio) provides a built-in interface that allows users to run the same prompt against multiple foundation models simultaneously, enabling direct side-by-side comparison of outputs to evaluate quality, tone, and accuracy before selecting a model for deployment.

Answer

Configure pre-built safety filters for generated content.

Answer

Deploy custom container images to Vertex AI endpoints.

Question 13

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.

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

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

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

Question 14

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.

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Add negative prompts to discourage incorrect facts.

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

Question 15

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.

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Deploy separate fine-tuned models for each language.

Answer

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