Generative AI Leader Practice Test 35 — 15 Questions

Question 1

A company is building a generative AI application that will generate marketing copy. They want to ensure that the content does not contain hate speech, violence, sexual content, or dangerous instructions. Which Google Cloud feature should they enable?

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

Accepted Answer

Configure safety filters and custom content controls in Vertex AI. Google's safety filters are designed to block harmful content categories. Vertex AI provides custom content controls to adjust thresholds. The other options are for different purposes.

Answer

Use the People + AI Guidebook to design prompts

Answer

Enable Model Card generation in Vertex AI

Answer

Implement a human review loop for all outputs

Question 2

A team is deciding between using fine-tuning and in-context learning for a document classification task. They have 500 labeled examples and need low latency. Which TWO statements are accurate?

Accepted Answer

Fine-tuning generally improves accuracy more than in-context learning when sufficient labeled data is available. Fine-tuning updates the model's weights on a labeled dataset, which generally leads to higher accuracy than in-context learning when sufficient labeled data (like 500 examples) is available. In-context learning relies on the model's pre-existing knowledge and a few examples in the prompt, which often yields lower accuracy for complex classification tasks.

Answer

In-context learning always has lower latency than fine-tuning

Answer

In-context learning can only be used with models that have a context window smaller than 1000 tokens

Answer

Fine-tuning eliminates the need for a validation dataset

Question 3

A product manager wants to transparently communicate the capabilities and limitations of their new generative AI feature to stakeholders. Which THREE resources from the Responsible AI Toolkit should they use?

Accepted Answer

Model Cards. Model Cards document model details, Datasheets for Datasets document data, and the People + AI Guidebook provides best practices for communication. PAIR Explorables are for education, not documentation.

Answer

SynthID

Answer

PAIR Explorables

Question 4

A company wants to build a multimodal application that can analyze images and generate captions. They are considering using Google Cloud AI. Which TWO services can be directly used for this purpose?

Accepted Answer

Vision AI. Vision AI (option A) is correct because it provides pre-trained models for image analysis, including object detection and image labeling, which are essential for generating captions. The Gemini API (option E) is correct because it is a multimodal model that can directly process images and text, enabling it to analyze images and produce descriptive captions without needing separate services.

Answer

Text-to-Speech AI

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Natural Language AI

Answer

Translation AI

Question 5

Which Google Cloud AI API is used to convert spoken language into text?

Accepted Answer

Speech-to-Text API. Speech-to-Text API transcribes audio into text.

Answer

Text-to-Speech API

Answer

Natural Language API

Answer

Translation API

Question 6

A healthcare startup needs to deploy an AI model that processes protected health information (PHI). They require a HIPAA BAA with the cloud provider. Which Google Cloud AI service can meet this compliance requirement?

Accepted Answer

Vertex AI. Vertex AI offers a HIPAA BAA for covered entities, while other services may not.

Answer

Colab

Answer

Gemini API via Google AI Studio

Answer

Cloud Vision API

Question 7

Which Google service provides free access to Jupyter notebooks with GPU support for prototyping ML models?

Accepted Answer

Colab. Google Colab is a free notebook service with GPU support, commonly used for prototyping.

Answer

Kaggle Notebooks

Answer

Vertex AI Workbench

Answer

BigQuery Studio

Question 8

A company is evaluating Google Cloud vs AWS Bedrock for building a multimodal application that needs to understand images, video, and text in a single model. Which unique Google Cloud capability supports this requirement?

Accepted Answer

Gemini API's native multimodal support across text, images, video, and audio. Gemini is a natively multimodal model capable of processing text, images, audio, video, and code. AWS Bedrock's models are primarily text or image-only, lacking native video understanding.

Answer

Use of Hugging Face models on Vertex AI

Answer

Integration with Amazon Bedrock's Titan model

Answer

BigQuery ML's image analysis functions

Question 9

An organization uses a fine-tuned model for medical diagnosis and must comply with HIPAA. Which measure is essential when deploying the model on Vertex AI?

Accepted Answer

Use a private Google Cloud Access and disable external internet access for the endpoint.. HIPAA requires that patient data be protected from unauthorized access during transmission and deployment. Using a private Google Cloud Access endpoint with external internet access disabled ensures that the model endpoint is only reachable within the organization's VPC network, preventing data exposure over the public internet and meeting HIPAA's security rule for safeguarding electronic protected health information (ePHI).

Answer

Store all patient data in Cloud Storage with object versioning.

Answer

Enable encryption at rest for all resources.

Answer

Use a publicly accessible endpoint for faster response times.

Question 10

A developer wants to integrate Gemini multimodal capabilities (text + image) into a mobile app using Python. Which Google Cloud client library should they use?

Accepted Answer

Vertex AI client library (google-cloud-aiplatform). The Vertex AI client library (google-cloud-aiplatform) provides the Generative AI SDK that supports multimodal capabilities, including the ability to send both text and image inputs to Gemini models. This library directly exposes the `GenerativeModel` class with methods like `generate_content()` that accept `Part` objects containing image data (e.g., `Part.from_image()` or `Part.from_uri()`), making it the correct choice for integrating Gemini multimodal features into a Python mobile app backend.

Answer

Dialogflow CX

Answer

Cloud Vision API

Answer

Natural Language API

Question 11

A marketing team wants to generate product descriptions using generative AI. They need to ensure factual accuracy and avoid hallucinations. Which approach should they use?

Accepted Answer

Implement a retrieval augmented generation (RAG) system that retrieves product facts from a database.. Retrieval Augmented Generation (RAG) is the correct approach because it grounds the model's output in verifiable, external data sources. By retrieving product facts from a database in real-time, the system ensures that the generated descriptions are based on accurate information, directly mitigating the risk of hallucination. This method combines the generative power of an LLM with a retrieval step that provides factual context, making it ideal for applications where precision is critical.

Answer

Use a code generation model to generate structured descriptions.

Answer

Fine-tune the model on all product descriptions using supervised learning.

Answer

Use a large language model with detailed prompt instructions to be accurate.

Question 12

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Train a custom model from scratch on the policy documents each month

Answer

Fine-tune a base LLM on the policy documents monthly

Question 13

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 14

A retail company wants to use GenAI to generate product descriptions. They have a small team of data scientists. What is the most efficient approach?

Accepted Answer

Use a foundation model API with prompt engineering and few-shot examples. Using a foundation model API with prompt engineering and few-shot examples is the most efficient approach for a small team. It leverages pre-trained models (e.g., GPT-4, Claude) via API calls, requiring no infrastructure or training data, while prompt engineering and few-shot examples allow the model to adapt to the company's product catalog with minimal effort and cost.

Answer

Collect more data for several months before starting

Answer

Train a model from scratch using their product data

Answer

Buy a proprietary model from a startup

Question 15

A developer is using Vertex AI Generative AI Studio to fine-tune a PaLM 2 model for code generation. After training, they notice the model generates plausible but incorrect code. What is the most likely cause?

Accepted Answer

Overfitting to training data. When a PaLM 2 model generates plausible but incorrect code after fine-tuning, the most likely cause is overfitting to the training data. Overfitting occurs when the model memorizes specific code patterns, syntax, or even bugs from the fine-tuning dataset rather than learning generalizable programming logic. This results in outputs that look syntactically correct and contextually relevant but fail to execute properly or solve the intended problem, because the model has not learned the underlying algorithmic principles.

Answer

Insufficient training steps

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Hallucination due to lack of grounding

Answer

Prompt format mismatch