Generative AI Leader Generative AI Concepts and Technologies — 15 Questions

Question 1

A data scientist needs to generate high-quality images from text prompts using Google Cloud. Which service should they use?

Accepted Answer

Imagen. Imagen is Google Cloud's text-to-image diffusion model. PaLM 2 and Gemini are primarily text models; Codey is for code generation.

Answer

PaLM 2

Answer

Gemini Pro Vision

Answer

Codey

Question 2

A company is building a chatbot that must answer questions based on a large internal knowledge base that is updated weekly. They want to avoid retraining the model frequently. Which technique should they use?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with a vector database. RAG retrieves relevant documents at inference time, keeping answers up-to-date without retraining. Fine-tuning would require frequent retraining; prompt engineering alone cannot incorporate new knowledge.

Answer

Use prompt engineering to instruct the model to ignore outdated information

Answer

Increase the model's context window and include all documents in the prompt

Answer

Fine-tune the model weekly on the updated knowledge base

Question 3

A financial services firm wants to use Gemini to analyze customer support transcripts and generate summaries. Compliance requires that the model never output any personally identifiable information (PII). Which combination of techniques should they implement?

Accepted Answer

Configure Gemini safety settings to block PII and use a separate PII detection API for post-processing. Using Gemini with safety filters and a post-processing step to redact PII provides defense in depth. Fine-tuning on redacted data might not cover all cases, and prompt instructions alone are not reliable.

Answer

Fine-tune Gemini on redacted transcripts and rely on the model to not generate PII

Answer

Use a smaller model that has never seen PII in training

Answer

Only use prompt instructions telling the model to avoid PII

Question 4

A developer is building a code generation assistant using Codey. They notice that the generated code sometimes contains deprecated API calls. What is the most likely cause?

Accepted Answer

Codey's training data has a knowledge cutoff date before the deprecation. Option D is correct because Codey, like all large language models, is trained on a static dataset with a specific knowledge cutoff date. If the training data predates the deprecation of certain APIs, the model will not be aware of the newer, recommended alternatives and will continue to generate code using the deprecated calls. This is a fundamental limitation of the model's training data recency, not a parameter tuning issue.

Answer

The top-p sampling is too low, limiting the model's vocabulary

Answer

The temperature setting is too high, causing creative but incorrect outputs

Answer

The context window is too short to include relevant API documentation

Question 5

A company wants to use Gemini to process invoices that contain both text and images (scanned documents). The invoices vary in layout. Which Gemini model version should they use?

Accepted Answer

Gemini 1.5 Pro. Gemini 1.5 Pro is the correct choice because it is a multimodal model capable of processing both text and images (scanned documents) natively, and its long context window (up to 1 million tokens) allows it to handle invoices with varying layouts without requiring preprocessing or layout-specific training. This version excels at understanding mixed-format documents, making it ideal for invoice processing where text and visual elements like tables and logos must be interpreted together.

Answer

PaLM 2

Answer

Gemini 1.0 Pro

Answer

Gemini Nano

Question 6

A startup wants to build a text-to-speech application for generating audiobooks. Which Google Cloud generative AI service is best suited for this task?

Accepted Answer

Chirp. Chirp is Google Cloud's text-to-speech AI model. Imagen generates images, Codey generates code, and Gemini is a multimodal model not specialized for high-quality speech synthesis.

Answer

Imagen

Answer

Codey

Answer

Gemini

Question 7

A machine learning engineer is fine-tuning a large language model using LoRA (Low-Rank Adaptation) to reduce memory usage. During training, they notice that the model's performance on the downstream task is not improving. What is the most likely issue?

Accepted Answer

The rank r of the LoRA adapter is too low to capture the task complexity. When fine-tuning with LoRA, the rank r determines the expressiveness of the low-rank adaptation matrices. If r is too low, the adapter lacks the capacity to learn the necessary task-specific features, causing the model's performance to stagnate. This is the most likely issue because the engineer observes no improvement, indicating the adapter's representational power is insufficient for the downstream task complexity.

Answer

The learning rate is too high, causing the loss to oscillate

Answer

The LoRA adapter is applied to all layers, which slows convergence

Answer

The base model is too large for the dataset, causing overfitting

Question 8

A company is using Gemini to generate marketing copy. They want the outputs to be more creative and varied. Which generation parameters should they adjust?

Accepted Answer

Increase temperature and adjust top-p to a higher value. Option C is correct because increasing temperature raises the randomness of token selection, making outputs more creative and varied, while adjusting top-p to a higher value (e.g., 0.9) allows the model to sample from a larger cumulative probability mass of likely tokens, further increasing diversity. Together, these parameters directly control the stochasticity of generation, which is essential for creative marketing copy.

Answer

Set temperature to 0 and top-k to 1

Answer

Decrease temperature and increase top-k

Answer

Increase the context window length

Question 9

What is the primary benefit of using embeddings and vector search in a generative AI application?

Accepted Answer

They enable efficient retrieval of semantically similar content. Option C is correct because embeddings convert text into dense vector representations that capture semantic meaning, and vector search enables efficient retrieval of semantically similar content by finding nearest neighbors in vector space. This retrieval-augmented generation (RAG) approach grounds the generative AI model in relevant external knowledge, improving accuracy and reducing hallucinations without retraining.

Answer

They improve the model's ability to generate code

Answer

They reduce the size of the model by compressing weights

Answer

They allow the model to process images directly

Question 10

A developer is using Gemini 1.5 Pro and needs to process a 2-hour video to answer questions about its content. The video is stored in Cloud Storage. What is the most efficient approach?

Accepted Answer

Send the video file as part of the prompt to Gemini 1.5 Pro. Gemini 1.5 Pro supports video input natively; you can pass the video directly (via GCS URI) and ask questions. Transcribing first adds latency and loses visual context.

Answer

Extract frames using Video Intelligence API and then send them as images to Gemini

Answer

Transcribe the video using Chirp, then analyze the text with Gemini

Answer

Use a custom model fine-tuned on video understanding tasks

Question 11

A company wants to use generative AI to create short product videos from text descriptions. Which Google Cloud service should they consider?

Accepted Answer

Veo. Veo is Google's video generation model that creates videos from text. Imagen creates images, Chirp creates audio, and Gemini is multimodal but not specialized for video generation.

Answer

Chirp

Answer

Imagen

Answer

Gemini

Question 12

An AI team is choosing between supervised fine-tuning and reinforcement learning from human feedback (RLHF) for a chatbot. They want the model to follow instructions closely and avoid toxic outputs. Which statement correctly compares these approaches?

Accepted Answer

RLHF is more effective at aligning the model with human values, including reducing toxicity. RLHF aligns the model with human preferences by rewarding desired behaviors (e.g., non-toxic, helpful). Supervised fine-tuning teaches format but not safety; in-context learning can steer but is less reliable for safety.

Answer

Supervised fine-tuning and RLHF achieve the same results, but RLHF is faster

Answer

Supervised fine-tuning is better for reducing toxicity because it uses labeled safe examples

Answer

In-context learning is always superior to both for safety, as it can adapt on the fly

Question 13

A data scientist wants to build a question-answering system over a large corpus of scientific papers. They want to minimize hallucinations and keep the knowledge current. Which TWO techniques should they combine?

Accepted Answer

Fine-tuning the model on the corpus. Fine-tuning the model on the corpus (A) adapts the model's weights to the specific domain and style of scientific papers, improving relevance and reducing factual errors. Retrieval-Augmented Generation (RAG) (B) grounds each answer in retrieved, up-to-date passages from the corpus, directly countering hallucinations and enabling knowledge updates without retraining. Together, they combine domain adaptation with dynamic retrieval for accurate, current responses.

Answer

Using a zero-shot prompt

Answer

Using only the largest Gemini model with no retrieval

Answer

Increasing the model's temperature to 1.5

Question 14

A company is deploying a generative AI application that must comply with GDPR. They need to ensure user data is not used for model training and that responses do not contain personal data. Which THREE measures should they implement?

Accepted Answer

Implement output filtering with a PII detection API (e.g., DLP). Data retention policies prevent storing user data for training; output filtering redacts personal data; using a dedicated instance isolates the model from other tenants. Prompt engineering is not reliable for compliance.

Answer

Use prompt engineering to instruct the model not to output personal data

Answer

Set temperature to 0 to ensure deterministic outputs

Question 15

A developer wants to use Google Cloud generative AI to build a multimodal application that can answer questions about images and text. Which TWO services are most appropriate?

Accepted Answer

Vertex AI. Vertex AI is correct because it provides a unified platform for building, deploying, and managing multimodal generative AI models, including support for both image and text inputs. It offers access to foundational models like Gemini Pro Vision, enabling developers to create applications that answer questions about images and text through a single API endpoint.

Answer

Chirp

Answer

Imagen

Answer

Codey