Generative AI Leader Generative AI Concepts and Technologies Practice Test 6 — 15 Questions

Question 1

A financial services firm is deploying a Gemini-based application that must comply with GDPR. The application processes customer queries and may include personal data. Which Google Cloud capability should the firm use to ensure that the model does not expose personally identifiable information (PII) in its responses?

Accepted Answer

Integrate Cloud Sensitive Data Protection (DLP) to inspect and redact PII from prompts and responses. Sensitive Data Protection (formerly DLP) can be integrated into the GenAI pipeline to inspect and redact PII from prompts and responses, ensuring compliance.

Answer

Set the model's safety filters to block all categories at maximum threshold

Answer

Use Vertex AI's Model Garden with a restricted access policy

Answer

Fine-tune the model on a dataset that has no PII

Question 2

In the transformer architecture, what is the role of the attention mechanism?

Accepted Answer

It decides which parts of the input to focus on when generating each token. The attention mechanism in the Transformer architecture computes a weighted sum of all input token representations, allowing the model to dynamically focus on the most relevant parts of the input sequence when generating each output token. This is achieved through learned query, key, and value projections that produce attention scores, enabling the model to capture long-range dependencies and contextual relationships. Option B correctly identifies this core function of selectively attending to input elements during token generation.

Answer

It normalizes the output of each layer

Answer

It predicts the next token directly

Answer

It converts tokens into numerical vectors

Question 3

A developer is using Gemini 1.5 Pro and needs to process a large PDF (500 pages). The model's context window is 1 million tokens. However, the API returns an error that the input exceeds the context window. What is the most likely cause?

Accepted Answer

The token count of the PDF exceeds 1 million tokens. Option B is correct because the error indicates the input exceeds the model's context window. While Gemini 1.5 Pro supports a 1 million token context window, a 500-page PDF can easily surpass this limit when considering that a single page of dense text may contain 2,000–3,000 tokens, and images or embedded tables further inflate token counts. The API strictly enforces the token budget, and exceeding it triggers the error regardless of the model's theoretical capacity.

Answer

The PDF is not properly formatted as text; Gemini only accepts raw text

Answer

The model version is actually Gemini 1.0 which has a smaller context window

Answer

The temperature is set too high causing the model to reject long inputs

Question 4

An e-commerce company wants to generate realistic product images from text descriptions using Google Cloud AI. Which service should they use?

Accepted Answer

Imagen on Vertex AI. Imagen on Vertex AI is the correct service because it is specifically designed for text-to-image generation, allowing the e-commerce company to create realistic product images from textual descriptions. It leverages Google's advanced diffusion models to produce high-fidelity visuals, making it the ideal choice for this use case.

Answer

Gemini 1.5 Pro

Answer

Chirp

Answer

Vertex AI Codey

Question 5

A team is building a multi-modal agent that needs to accept a user's image of a handwritten note, convert it to text, and then run a sentiment analysis. They want to minimize latency and cost. Which approach is best?

Accepted Answer

Use Gemini 1.5 Flash with a prompt that includes the image and asks for sentiment analysis in one call. Option C is correct because Gemini 1.5 Flash is a multimodal model that can directly process images and perform sentiment analysis in a single API call, eliminating the need for separate OCR and NLP services. This minimizes both latency (by reducing the number of sequential calls) and cost (by using a single, efficient model instead of multiple specialized services).

Answer

Fine-tune Gemini Pro on handwritten notes and sentiment labels

Answer

Use Document AI for OCR and then call Codey for sentiment analysis

Answer

Use Cloud Vision API for OCR, then feed the text to a sentiment analysis model via Vertex AI

Question 6

What is the primary advantage of using embeddings and vector search for semantic search over traditional keyword search?

Accepted Answer

Ability to find documents with similar meaning even without exact keyword matches. Option D is correct because embeddings and vector search capture semantic meaning by converting text into high-dimensional vectors, enabling retrieval of documents with similar meaning even when they lack exact keyword matches. This is the primary advantage over traditional keyword search, which relies on literal term matching and fails with synonyms, paraphrases, or conceptual similarity.

Answer

Faster retrieval speed

Answer

Lower storage requirements

Answer

No need for indexing

Question 7

A company has a Gemini-based application that sometimes produces factually incorrect answers. They want to improve accuracy without retraining the model. Which technique should they implement?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) with a curated knowledge base. Retrieval-Augmented Generation (RAG) is the correct technique because it grounds the model's responses in a curated, external knowledge base, providing factual context that reduces hallucinations without modifying the model's weights. This directly addresses the need for improved accuracy in a Gemini-based application while avoiding costly retraining.

Answer

Reduce the top-k value to 10

Answer

Use prompt engineering to instruct the model to 'be more accurate'

Answer

Increase the temperature to 1.0 for more diverse outputs

Question 8

A researcher wants to use Google's AlphaFold for a project. What is the primary capability of AlphaFold?

Accepted Answer

Predicting 3D protein structures from amino acid sequences. AlphaFold, developed by Google DeepMind, is specifically designed to predict the 3D structure of proteins from their amino acid sequences. This capability solves a fundamental challenge in biology, as the function of a protein is largely determined by its 3D shape, and experimental methods like X-ray crystallography are time-consuming and expensive. AlphaFold achieves this using a deep learning architecture that integrates multiple sequence alignment (MSA) and pairwise distance predictions to model the spatial coordinates of atoms.

Answer

Generating realistic human speech

Answer

Playing the game of Go at superhuman level

Answer

Generating code from natural language descriptions

Question 9

A startup wants to integrate a GenAI assistant into Google Workspace (Docs, Gmail, Sheets) to help employees draft emails and create charts. Which Google AI offering is designed for this purpose?

Accepted Answer

Gemini for Workspace. Gemini for Workspace (formerly Duet AI) is Google's AI assistant embedded across Workspace apps for tasks like drafting and analysis.

Answer

Colab Enterprise

Answer

Vertex AI Agent Builder

Answer

NotebookLM

Question 10

A company is building a customer support agent that can answer questions about product manuals and also generate images of the products from descriptions. Which TWO Google Cloud services should they combine? (Select 2)

Accepted Answer

Imagen on Vertex AI. Imagen on Vertex AI is correct because it is Google Cloud's text-to-image generation service, capable of creating product images from textual descriptions. This directly fulfills the requirement to 'generate images of the products from descriptions'.

Answer

Codey

Answer

Chirp

Answer

Cloud Vision API

Question 11

A company is deploying a Gemini-based application and needs to ensure low latency for real-time user interactions. They also want to reduce cost. Which THREE strategies should they consider? (Select 3)

Accepted Answer

Use Gemini 1.5 Flash instead of Pro. Option A is correct because Gemini 1.5 Flash is a lighter, distilled version of the Pro model, designed for lower latency and reduced computational cost while still maintaining strong performance for real-time interactions. Flash models use fewer parameters and optimized inference paths, making them ideal for latency-sensitive applications where cost efficiency is critical.

Answer

Increase the model's max output tokens to ensure comprehensive answers

Answer

Use full fine-tuning to make the model faster

Question 12

A data scientist wants to apply reinforcement learning from human feedback (RLHF) to improve a chatbot's helpfulness. Which TWO steps are part of the RLHF process? (Select 2)

Accepted Answer

Collect human rankings or preferences on multiple model outputs. RLHF typically involves collecting human rankings of model outputs and then training a reward model to score outputs, which is used to fine-tune the model via PPO.

Answer

Use prompt engineering to tune the model without retraining

Answer

Collect human-annotated demonstrations of ideal responses

Answer

Deploy the model in A/B testing to gather implicit feedback

Question 13

A developer wants to generate high-quality images from text descriptions using Google Cloud. Which service should they use?

Accepted Answer

Imagen. Imagen is Google Cloud's text-to-image diffusion model, specifically designed to generate high-quality images from natural language descriptions. It leverages deep learning to produce photorealistic outputs, making it the correct choice for this use case.

Answer

Chirp

Answer

Codey

Answer

Gemini

Question 14

A company is using a large language model for a customer-facing chat application. They notice that the model sometimes generates plausible-sounding but incorrect information. Which strategy is most effective to reduce this issue?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) to retrieve relevant documents before generating a response.. RAG grounds responses by retrieving relevant, verified information from a trusted source, reducing hallucinations. Fine-tuning on incorrect data would not help; increasing temperature increases randomness; reducing context window may lose relevant info.

Answer

Fine-tune the model on a dataset of corrected conversations.

Answer

Reduce the context window to limit the information the model considers.

Answer

Increase the temperature parameter to make the model more deterministic.

Question 15

A data scientist is fine-tuning a large language model for a specialized domain using limited labeled data. To avoid catastrophic forgetting and reduce computational cost, which approach is recommended?

Accepted Answer

Adapter-based fine-tuning using LoRA. Adapter-based fine-tuning using LoRA (Low-Rank Adaptation) is recommended because it freezes the pre-trained model weights and injects trainable low-rank matrices into the transformer layers. This approach drastically reduces the number of parameters to update (often by 10,000x), lowering memory and compute requirements, while preserving the original knowledge to prevent catastrophic forgetting on limited domain data.

Answer

Using prompt engineering with in-context learning

Answer

Full fine-tuning of all model parameters

Answer

Training a new model from scratch on the domain data