Generative AI Leader Generative AI Concepts and Technologies Practice Test 7 — 15 Questions

Question 1

An organization needs to deploy a generative AI application with strict compliance requirements, including data residency and auditability of model decisions. Which Google Cloud feature should they prioritize?

Accepted Answer

Vertex AI. Vertex AI provides enterprise controls including data residency options, audit logs, and model monitoring for compliance. Other options are important but do not directly address data residency and auditability comprehensively.

Answer

Colab Enterprise

Answer

Gemini API

Answer

Model Garden

Question 2

A developer wants to generate Python code to extract data from a CSV file using a generative AI model on Google Cloud. Which model is specifically designed for code generation?

Accepted Answer

Codey. Codey is the correct choice because it is Google Cloud's family of models specifically designed and fine-tuned for code generation tasks, including generating Python code from natural language prompts. Unlike general-purpose language models, Codey is trained on a large corpus of source code and can produce syntactically correct code snippets, making it ideal for extracting data from a CSV file.

Answer

Gemini

Answer

Imagen

Answer

PaLM 2

Question 3

A company is building a multilingual customer support chatbot that needs to understand and respond in 20 languages. Which Google model is most suitable for this task?

Accepted Answer

Gemini. Gemini is a multimodal large language model (LLM) designed for understanding and generating text across multiple languages, making it the most suitable choice for a multilingual customer support chatbot. Unlike specialized models, Gemini's architecture supports over 100 languages natively, enabling it to handle the 20-language requirement without needing separate language-specific models.

Answer

Chirp

Answer

Imagen

Answer

Codey

Question 4

Which parameter controls the randomness of a language model's output?

Accepted Answer

Temperature. Temperature is the parameter that directly controls the randomness of a language model's output by scaling the logits before applying the softmax function. A higher temperature (e.g., >1.0) makes the probability distribution more uniform, increasing randomness, while a lower temperature (e.g., <1.0) sharpens the distribution, making the model more deterministic and focused on high-probability tokens.

Answer

Embedding dimension

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Context window

Answer

Top-k

Question 5

A machine learning engineer wants to convert text into numerical vectors for similarity search. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Embeddings API. The Vertex AI Embeddings API is the correct choice because it is specifically designed to convert text (and other data types) into dense numerical vectors (embeddings) that capture semantic meaning. These embeddings are the fundamental input for similarity search, enabling efficient comparison of text based on conceptual closeness rather than exact keyword matching.

Answer

Natural Language API

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Vector Search

Answer

Gemini API

Question 6

A team is using a generative AI model to create marketing copy. They want the responses to be more focused and less random. Which parameter should they adjust?

Accepted Answer

Decrease temperature. Decreasing the temperature parameter reduces the randomness of the model's token selection by lowering the probability of sampling less likely tokens. This makes the output more focused and deterministic, which is ideal for marketing copy that needs to stay on-brand and consistent.

Answer

Increase top-k

Answer

Decrease context window

Answer

Increase temperature

Question 7

An enterprise wants to use Gemini 1.5 Flash for a real-time chat application with low latency. Which trade-off should they expect compared to Gemini 1.5 Pro?

Accepted Answer

Lower latency but potentially lower quality. Gemini 1.5 Flash is specifically optimized for lower latency and cost efficiency, making it ideal for real-time chat applications. However, this optimization comes at the cost of reduced model capacity and reasoning depth compared to Gemini 1.5 Pro, which prioritizes higher quality and accuracy over speed. Therefore, the expected trade-off is lower latency but potentially lower quality.

Answer

Higher quality and lower latency

Answer

Lower cost but longer context window

Answer

Higher accuracy but slower responses

Question 8

Which Google AI milestone introduced the Transformer architecture that underpins modern LLMs?

Accepted Answer

Transformer paper. The Transformer architecture, which is the foundational technology behind modern large language models (LLMs) like GPT and BERT, was introduced in the 2017 paper 'Attention Is All You Need' by Vaswani et al. This paper proposed the self-attention mechanism and the encoder-decoder structure that replaced recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, enabling parallelized training and superior handling of long-range dependencies in sequence data.

Answer

AlphaGo

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AlphaFold

Answer

BERT

Question 9

A company wants to generate a video from a text description using Google Cloud. Which service is designed for this?

Accepted Answer

Veo. Veo is Google Cloud's generative AI model specifically designed for creating high-quality videos from text or image prompts. It leverages advanced diffusion and transformer architectures to generate coherent video sequences, making it the correct choice for text-to-video generation.

Answer

Codey

Answer

Chirp

Answer

Imagen

Question 10

A data scientist is building a RAG pipeline for a legal document retrieval system. Which TWO components are essential for this system? (Select two.)

Accepted Answer

A vector database for similarity search. Option B is correct because a vector database is essential for storing and efficiently retrieving document embeddings via similarity search (e.g., cosine similarity or Euclidean distance), which is the core retrieval mechanism in a RAG pipeline. Without it, the system cannot quickly find the most relevant document chunks to augment the LLM's context.

Answer

A large language model for final response generation

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A fine-tuned LLM for generation

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A diffusion model for document generation

Question 11

A company needs to deploy a generative AI application on Google Cloud that meets data residency requirements. Which THREE features should they enable? (Select three.)

Accepted Answer

Enable VPC Service Controls. To satisfy data residency, the company must control where data is stored (region selection), ensure no data leaves that region (data boundaries), and use a service that supports regional endpoints (Vertex AI).

Answer

Use a multi-region storage bucket

Answer

Use the global endpoint for low latency

Question 12

A team is evaluating whether to use reinforcement learning from human feedback (RLHF) or in-context learning for a chatbot. Which TWO statements correctly describe trade-offs? (Select two.)

Accepted Answer

In-context learning is generally more cost-effective than RLHF for one-off tasks.. Option B is correct because in-context learning (ICL) uses examples provided in the prompt at inference time, requiring no additional training or data collection, making it far more cost-effective for one-off tasks compared to RLHF, which demands a large dataset of human preference labels and a full fine-tuning pipeline. RLHF's upfront cost in data labeling and compute is only justified when the model needs persistent alignment across many interactions.

Answer

RLHF can align model behavior without any human input.

Answer

In-context learning permanently modifies model weights.

Answer

In-context learning can handle longer contexts than RLHF fine-tuned models.

Question 13

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 14

What is the primary purpose of the temperature parameter when configuring a generative AI model?

Accepted Answer

Controls the diversity of the output by scaling the log probabilities before sampling. Temperature controls the randomness of token selection. Higher temperature increases creativity/diversity; lower temperature produces more deterministic and focused responses.

Answer

Adjusts the number of highest-probability tokens considered at each step

Answer

Specifies the minimum probability threshold for token selection

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Sets the maximum number of tokens in the response

Question 15

A research team is using Imagen to generate images for a marketing campaign. They notice that the generated images sometimes contain distorted faces or unnatural object placements. They want to improve consistency without sacrificing image diversity. Which approach should they try first?

Accepted Answer

Reduce the top-p value (e.g., from 1.0 to 0.9).. Adjusting top-p (nucleus sampling) reduces the set of possible tokens to the most probable ones, which often removes low‑probability artifacts while preserving diversity. Prompt engineering and fine‑tuning can also help but are more involved. Temperature reduction may lower diversity too much.

Answer

Increase the temperature to 1.5

Answer

Switch from Imagen to Gemini Pro for image generation

Answer

Set top-k to 1