Generative AI Leader Generative AI Concepts and Technologies Practice Test 2 — 15 Questions

Question 1

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. Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions by retrieving relevant chunks from the policy documents stored in a vector store, without requiring model retraining. When documents are updated monthly, only the vector index needs to be refreshed, which is far more cost-effective and faster than fine-tuning or retraining a model. This approach leverages the base LLM's language understanding while grounding responses in the latest policy content.

Answer

Fine-tune a base LLM on the policy documents monthly

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

Question 2

What is the primary purpose of the temperature parameter when using a generative language model?

Accepted Answer

It controls the randomness of token selection; lower values make output more deterministic. Temperature controls the randomness of token selection. Lower values produce more deterministic and conservative outputs, while higher values increase diversity and creativity.

Answer

It sets the maximum number of tokens the model can generate in a single response

Answer

It determines the number of candidate tokens considered at each step

Answer

It adjusts the similarity threshold for vector search retrieval

Question 3

A developer is using Gemini 1.5 Flash for a real-time chat application and notices that responses are sometimes too slow. Which model parameter or configuration change would MOST likely reduce latency without significantly harming quality?

Accepted Answer

Decrease the max output tokens setting. Lowering max output tokens reduces the number of tokens generated, directly decreasing response time. Temperature and top-p affect creativity, not latency. Reducing top-k may slightly speed up sampling but has a minimal effect compared to output length.

Answer

Decrease the top-k value to 10

Answer

Increase the temperature to 1.5

Answer

Increase the top-p value to 1.0

Question 4

A financial services firm needs to deploy a generative AI model that generates reports from structured and unstructured data. The solution must ensure that outputs never contain sensitive customer information. Which combination of Google Cloud services should they use?

Accepted Answer

Vertex AI Gemini API with DLP integration + IAM roles + VPC Service Controls. Vertex AI's DLP integration can inspect and redact PII from prompts and responses. Combined with IAM roles and VPC-SC, this provides comprehensive data protection. The other options either lack DLP integration or do not cover both prompt and response inspection.

Answer

Vertex AI Gemini API + Cloud Key Management Service (KMS) + Secret Manager

Answer

Vertex AI Gemini API + Cloud Data Loss Prevention (DLP) standalone + Cloud NAT

Answer

Vertex AI Gemini API + Cloud DLP + Cloud Armor

Question 5

A research team wants to generate high-quality images from text descriptions for a marketing campaign. They need the ability to edit specific regions of generated images while preserving the rest. Which Google Cloud AI service should they use?

Accepted Answer

Imagen on Vertex AI. Imagen supports inpainting (region editing via masks) natively. Gemini is multimodal but not primarily for image generation. Veo generates video, not images. Codey is for code.

Answer

Gemini 1.5 Pro with image input

Answer

Codey on Vertex AI

Answer

Veo on Vertex AI

Question 6

A data scientist is fine-tuning a foundation model for a specialized legal document summarization task. The labeled dataset is only 5,000 examples. Which fine-tuning technique would be MOST efficient to adapt the model without catastrophic forgetting and with minimal computational cost?

Accepted Answer

Low-Rank Adaptation (LoRA). LoRA (Low-Rank Adaptation) is an adapter-based method that trains only a small number of added parameters, making it efficient and less prone to catastrophic forgetting compared to full fine-tuning. Supervised fine-tuning full model is expensive; RLHF is for alignment after fine-tuning; in-context learning requires no training but may not suffice.

Answer

Reinforcement Learning from Human Feedback (RLHF)

Answer

Full supervised fine-tuning of all model parameters

Answer

In-context learning with few-shot examples

Question 7

What is the fundamental difference between a foundation model and a fine-tuned model?

Accepted Answer

A foundation model is pre-trained on a large, diverse corpus; a fine-tuned model is adapted from a foundation model on a specific domain or task. A foundation model is pre-trained on broad data for general tasks, while a fine-tuned model is further trained on a specific dataset to specialize for a particular use case.

Answer

Foundation models only generate text; fine-tuned models can generate images

Answer

Foundation models are open-source; fine-tuned models are proprietary

Answer

Foundation models require no inference infrastructure; fine-tuned models do

Question 8

A company uses Gemini 1.5 Pro to analyze customer call transcripts and generate summaries. They notice that the summaries occasionally include fabricated details that were not in the transcript. Which technique is specifically designed to reduce such hallucinations?

Accepted Answer

Ground the model responses by implementing Retrieval-Augmented Generation (RAG) with the transcript as a source. Grounding with RAG retrieves factual data from trusted sources to condition the model's response, directly reducing hallucinations. Prompt engineering can help but does not guarantee factual accuracy; fine-tuning may reduce but not eliminate; temperature reduction makes output more deterministic but does not address factuality from external data.

Answer

Decrease the temperature to 0.0

Answer

Use a system prompt that instructs the model not to make up information

Answer

Fine-tune the model on a larger dataset of call transcripts

Question 9

A developer is building a multimodal application that needs to analyze images, understand spoken language, and generate text responses. Which Google Cloud generative AI model is BEST suited for this task?

Accepted Answer

Gemini 1.5 Pro. Gemini models are natively multimodal, accepting text, image, audio, and video inputs. PaLM 2 is text-only. Imagen is for image generation. Chirp is for speech recognition/synthesis.

Answer

PaLM 2 for text generation

Answer

Imagen for image generation

Answer

Chirp for speech recognition

Question 10

An organization wants to use generative AI to automatically generate code snippets from natural language descriptions. The solution must be integrated into their existing CI/CD pipeline on Google Cloud. Which service should they use?

Accepted Answer

Codey on Vertex AI. Codey (Codey for code generation) is designed for code generation and is available via Vertex AI. Gemini can also generate code but Codey is specialized. Imagen and Chirp are for images and speech, respectively.

Answer

Chirp on Vertex AI

Answer

Imagen on Vertex AI

Answer

Gemini 1.5 Flash on Vertex AI

Question 11

Which of the following best describes how large language models (LLMs) generate text?

Accepted Answer

They predict the next token in a sequence based on the preceding tokens. LLMs are trained to predict the next token given the preceding tokens. During inference, they generate one token at a time autoregressively.

Answer

They retrieve the most similar text from a database and return it

Answer

They use a rule-based grammar engine to construct sentences

Answer

They randomly select words from a fixed vocabulary

Question 12

A company is deploying a generative AI application that must comply with GDPR's right to explanation. The application must be able to justify its decisions. Which model or approach provides the MOST inherent interpretability?

Accepted Answer

Use a smaller, simpler model that is inherently more interpretable, such as a logistic regression or decision tree. Smaller, simpler models are inherently more interpretable. Large black-box models (Gemini, PaLM) are difficult to explain. RAG improves factual grounding but not interpretability of the model's reasoning. Prompting does not make the model's internal logic transparent.

Answer

Use Gemini 1.5 Pro with a system prompt asking for explanations

Answer

Use Retrieval-Augmented Generation (RAG) to ground responses in source documents

Answer

Use PaLM 2 with prompt engineering to provide step-by-step reasoning

Question 13

A company is deploying a chatbot using Gemini 1.5 Pro. They want to reduce the risk of the chatbot generating toxic or harmful content. Which TWO techniques should they implement? (Choose two.)

Accepted Answer

Configure Google's safety filters and thresholds in Vertex AI. Safety filters (e.g., Google's safety settings) block harmful content. Fine-tuning with curated safe examples reduces the likelihood of generating harmful outputs. Prompt engineering alone is insufficient, RLHF is post-training and may not catch all cases, and RAG is for grounding, not safety.

Answer

Apply Reinforcement Learning from Human Feedback (RLHF) after deployment

Answer

Include a system prompt that instructs the model to be helpful and harmless

Answer

Use a RAG system to ground responses in a knowledge base

Question 14

A data scientist is evaluating the output quality of a text generation model. They observe that the model often repeats phrases and produces very generic responses. Which THREE parameter adjustments could help increase diversity and reduce repetition? (Choose three.)

Accepted Answer

Increase the top-k value from 40 to 100. Higher temperature increases randomness, reducing repetition. Higher top-k and top-p allow a wider set of tokens, increasing diversity. Reducing max output tokens does not affect diversity; increasing frequency penalty reduces repetition directly.

Answer

Decrease the max output tokens from 1024 to 512

Answer

Decrease the frequency penalty to 0.0

Question 15

An enterprise wants to use generative AI to help employees search through internal documents, including text, scanned PDFs, and images. They need to index the content and enable semantic search. Which TWO Google Cloud services should they use? (Choose two.)

Accepted Answer

Vertex AI Vector Search. Vertex AI Embeddings API generates vector embeddings from text and images. Vertex AI Vector Search allows semantic search over those embeddings. Document AI is for document processing but not for embeddings or search. Gemini API is for generation, not indexing. Cloud Storage is for storage, not search.

Answer

Document AI

Answer

Cloud Storage with Object Versioning

Answer

Gemini API for content generation