Generative AI Leader Fundamentals of Generative AI — Timed 50 Questions

Question 1

A data scientist is fine-tuning a large language model using Vertex AI. The training job fails with an out-of-memory error. Which action should they take to resolve this issue?

Accepted Answer

Reduce the batch size. Reducing the batch size decreases the memory footprint per training step, allowing the model to fit within the available GPU or TPU memory. Out-of-memory errors during fine-tuning on Vertex AI typically occur when the batch size is too large for the allocated accelerator memory, and lowering it directly resolves the issue without changing the model architecture or hardware.

Answer

Change the accelerator to TPU

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Use a larger model

Answer

Increase the batch size

Question 2

A retail company is building a generative AI chatbot to assist customers with product recommendations and order tracking. The chatbot uses Vertex AI with Gemini 1.5 Pro, and the development team has implemented a Retrieval-Augmented Generation (RAG) pipeline using Vertex AI Search for grounding. The pipeline uses a vector store containing product descriptions and order history. During testing, the team observes that the chatbot sometimes provides incorrect order statuses—for example, claiming an order is 'shipped' when it is actually 'pending'. The team suspects the issue is related to how context is retrieved and used. The RAG pipeline currently retrieves the top 5 chunks based on cosine similarity from the vector store, and passes them as context to the model. The team is considering several changes to improve factual accuracy. Which single action would most effectively reduce hallucinations in this scenario?

Accepted Answer

Increase the similarity score threshold for retrieval to 0.85 to filter out less relevant chunks.. Increasing the similarity score threshold to 0.85 ensures that only highly relevant chunks are passed to the Gemini 1.5 Pro model, directly reducing the risk of the model generating responses based on irrelevant or low-confidence context. In a RAG pipeline using Vertex AI Search, low-similarity chunks can contain order statuses from different customers or products, leading to hallucinations like incorrect order statuses. Filtering out these less relevant chunks improves the factual grounding of the model's output.

Answer

Switch from Vertex AI Search to a different vector database like Pinecone.

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Reduce the model temperature to 0.0 to make outputs more deterministic.

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Increase the top-K retrieval value to 10 to provide more context to the model.

Question 3

A developer runs this command: `gcloud ai models upload --region=us-central1 --display-name=my-model --artifact-uri=gs://my-bucket/model.pkl`. What is the primary purpose?

Accepted Answer

Upload a model artifact to Model Registry. The command `gcloud ai models upload` uploads a local model artifact (model.pkl stored in Cloud Storage) to the Vertex AI Model Registry. This is used for versioning and managing trained models, not for initiating training, deployment, or pipeline creation. The Model Registry stores the model artifact for later use in deployments or predictions.

Answer

Create a training pipeline

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Deploy a model to an endpoint

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Train a model

Question 4

A developer wants to quickly experiment with different foundation models available in Google Cloud. Which tool should they use?

Accepted Answer

Gen AI Studio in Vertex AI. Gen AI Studio in Vertex AI is the correct tool because it provides a unified interface for discovering, testing, and customizing a wide range of foundation models (e.g., PaLM 2, Gemini, Codey, Imagen) directly from Google Cloud. It allows developers to quickly experiment with different models via a web UI or API without provisioning any infrastructure, making it ideal for rapid prototyping and prompt engineering.

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BigQuery ML

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Cloud Console Compute Engine

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Vertex AI Model Registry

Question 5

Refer to the exhibit. A developer executed the command to list endpoints. They notice that two models are deployed to the same endpoint. What is the most likely reason for this configuration?

Accepted Answer

It is a canary deployment with traffic splitting. A is correct because deploying two models to the same endpoint with traffic splitting is a standard canary deployment strategy. In this configuration, a small percentage of inference requests are routed to the new model while the majority go to the stable model, allowing validation of the new model's performance before full rollout. This is commonly supported by Google Cloud's Vertex AI, where you can deploy multiple models to an endpoint and assign traffic percentages to each model variant (e.g., 90% to the stable model and 10% to the canary model).

Answer

The endpoint is misconfigured and will cause conflicts

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The models are from different frameworks

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It is a batch prediction endpoint

Question 6

During fine-tuning a model on Vertex AI, the job fails with error 'ResourceExhausted: Out of memory'. What is the most likely cause?

Accepted Answer

Wrong machine type with insufficient memory. The error 'ResourceExhausted: Out of memory' during fine-tuning on Vertex AI indicates that the allocated memory on the chosen machine type is insufficient to hold the model weights, gradients, optimizer states, and the input batch simultaneously. Option D is correct because selecting a machine type with inadequate RAM (e.g., using a standard n1-standard-4 instead of a high-memory machine like n1-highmem-16) directly causes this out-of-memory condition, especially for large foundation models like PaLM or Llama 2.

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Too few training steps

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Batch size too large

Answer

Dataset too small

Question 7

Which THREE of the following are potential risks when deploying generative AI?

Accepted Answer

Hallucinations. Generative AI models pose several risks. Hallucinations (A) occur when models generate plausible but incorrect information. Memorization of sensitive training data (B) can leak private information from the training set. Bias and fairness issues (C) arise from biased training data or model behavior. Toxic or harmful content generation (E) can produce offensive or dangerous outputs. Option D (increased model accuracy) is a benefit, not a risk.

Answer

Increased model accuracy

Question 8

A company is developing a code generation assistant and wants to ensure the model respects access control policies, e.g., it should not generate code that uses internal APIs that the user is not authorized to access. Which technique is most effective for embedding such policy constraints into the model's behavior?

Accepted Answer

Fine-tune the model on a dataset of code snippets that follow access control policies, including negative examples of disallowed API usage.. Fine-tuning on a curated dataset that includes both positive examples (compliant code) and negative examples (disallowed API usage) directly adjusts the model's weights to internalize access control policies. This method is more effective than prompt-based approaches because it modifies the model's underlying behavior rather than relying on fragile instructions that can be ignored or overridden, especially in complex code generation tasks.

Answer

Include a system prompt that instructs the model to never generate code using internal APIs.

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Use a retriever to fetch policy documents and prepend them to each prompt.

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Train a separate classifier to rerank model outputs and reject non-compliant generations.

Question 9

An organization wants to use a generative model to automatically generate legal contracts. The model must produce clauses that are not only grammatically correct but also legally enforceable and consistent with current jurisdiction laws. Which combination of techniques best ensures legal compliance?

Accepted Answer

Fine-tune a model on a diverse set of enforceable contracts and incorporate an external compliance verifier that uses rule-based checks.. Fine-tuning on a diverse set of enforceable contracts teaches domain-specific language and legal nuances, while incorporating an external compliance verifier with rule-based checks ensures that generated clauses are legally enforceable and consistent with current jurisdiction laws. Option A is incorrect because fine-tuning a small model on a single-jurisdiction dataset may produce narrow legal knowledge and lacks a compliance verification mechanism. Option B is incorrect because RAG retrieves relevant laws but does not verify the enforceability of generated clauses; it requires an additional validation step. Option D is incorrect because prompt engineering alone is unreliable for precise legal reasoning and may not guarantee compliance with specific laws.

Answer

Fine-tune a small model exclusively on legal contracts from a single jurisdiction and use it for generation.

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Implement retrieval-augmented generation (RAG) with a vector database of all relevant laws.

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Use a large instruction-tuned model with carefully engineered prompts describing jurisdiction details.

Question 10

A retail company uses the Vertex AI Gemini API to generate product descriptions. Recently, the model started producing factually incorrect statements about product specifications, such as wrong dimensions and materials. Which strategy should be implemented to improve factual accuracy?

Accepted Answer

Use grounding with Vertex AI Search to retrieve verified product data. Grounding with Vertex AI Search connects the Gemini API to a verified, structured data source (e.g., product catalog), enabling the model to retrieve and cite factual specifications rather than relying solely on its training data. This directly addresses the hallucination of wrong dimensions and materials by providing a retrieval-augmented generation (RAG) mechanism that overrides the model's parametric knowledge with authoritative, up-to-date information.

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Enable model versioning to automatically roll back to a previous version

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Fine-tune the model on a dataset of product images and descriptions

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Increase the temperature parameter to 0.9

Question 11

A company is deploying a large language model (LLM) for customer support using Vertex AI. Which TWO best practices should they follow to ensure high-quality and cost-effective responses?

Accepted Answer

Implement prompt optimization techniques to tailor responses. The correct answers are C (implement prompt optimization techniques) and D (use Vertex AI Model Monitoring to track input drift and response quality). Prompt optimization techniques, such as careful engineering and tuning of prompts, help tailor the LLM's responses to be more accurate and relevant, improving overall quality. Vertex AI Model Monitoring detects input drift and measures response quality over time, allowing proactive adjustments to maintain performance and cost-effectiveness. Option A (Spot VMs) is not a best practice for production LLM workloads due to potential interruptions. Option B (plain text prompts) lacks security and version control best practices. Option E (single large model for all queries) can lead to high latency and cost, and using specialized models or routing is more efficient.

Answer

Deploy the model on Spot VMs to reduce infrastructure costs

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Store prompts in plain text files for easy version control

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Use a single large model for all query types to maintain consistency

Question 12

A startup is building a customer support chatbot using Vertex AI and wants to ground responses in their product documentation to reduce hallucinations. Which approach should they use?

Accepted Answer

Enable Vertex AI Grounding with a custom enterprise data store containing the documentation.. Vertex AI Grounding with a custom enterprise data store is the correct approach because it allows the chatbot to retrieve and cite specific chunks from the product documentation in real time, directly reducing hallucinations by constraining responses to verified content. This method uses the underlying grounding service to query a vector-based data store (powered by Vertex AI Search) and append source references to the model's output, ensuring factual accuracy without retraining.

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Use the Codey API for text generation.

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Use the base model without any grounding to maximize flexibility.

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Fine-tune the model on the documentation and deploy.

Question 13

A data scientist notices that a text generation model deployed on Vertex AI returns repetitive outputs after a few turns in a chat application. What is the most likely cause and the best parameter adjustment?

Accepted Answer

The top_p value is too high; reduce top_p to limit token sampling.. Repetitive outputs in a chat application after a few turns are typically caused by the model getting stuck in a loop due to high cumulative probability from top-p sampling. Reducing top_p limits the set of tokens considered at each step, forcing the model to explore less likely tokens and breaking the repetition cycle. This directly addresses the issue without sacrificing coherence, unlike temperature adjustments which affect randomness globally.

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The max_output_tokens is too low; increase it to allow more diverse output.

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The model is overfitted; switch to a smaller model.

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The temperature is too low; increase temperature to add randomness.

Question 14

A financial services company wants to use generative AI to generate personalized investment advice. They must ensure responses comply with regulatory requirements (e.g., no guarantees of returns). Which Vertex AI safety feature should they primarily use?

Accepted Answer

Safety filters with a custom blocklist that includes phrases like 'guaranteed return'.. Safety filters with a custom blocklist allow the company to define specific prohibited phrases (e.g., 'guaranteed return') that the model must avoid generating. This provides a deterministic, rule-based enforcement layer that directly addresses regulatory compliance by blocking disallowed content at inference time, without relying on the model's probabilistic behavior.

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Vertex AI Grounding with their compliance database.

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Prompt engineering with instructions to avoid guarantees.

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Reinforcement learning from human feedback (RLHF) on the model.

Question 15

A company is using Vertex AI to generate marketing copy. They notice that the output sometimes contains factual inaccuracies. Which parameter adjustment is most likely to improve factual accuracy?

Accepted Answer

Decrease the temperature parameter.. Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and less likely to generate creative but factually incorrect content. Lower temperature (e.g., 0.1) forces the model to choose higher-probability tokens, which aligns with more factual and consistent responses, especially in tasks like marketing copy where accuracy is critical.

Answer

Increase the max_output_tokens parameter.

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Increase the top_p parameter.

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Add a post-processing step to verify facts using a database.

Question 16

A team is fine-tuning a large language model on custom data using Vertex AI. They find that the training loss decreases but validation loss increases. What is the best course of action?

Accepted Answer

Reduce the model size or add dropout regularization.. The increasing validation loss while training loss decreases is a classic sign of overfitting, where the model memorizes the training data but fails to generalize. Reducing model size or adding dropout regularization directly combats overfitting by limiting the model's capacity or introducing noise during training, which forces the model to learn more robust features. This is the best course of action because it addresses the root cause without further exacerbating the problem.

Answer

Increase the number of training epochs.

Answer

Increase the learning rate.

Answer

Switch to a smaller batch size.

Question 17

A developer wants to generate product descriptions from a list of features using Vertex AI. Which model type is best suited for this task?

Accepted Answer

A text generation model (e.g., text-bison@001).. Text-bison@001 is a dedicated text generation model optimized for tasks like summarization, translation, and content creation from structured inputs. It can take a list of features as a prompt and generate coherent, descriptive product descriptions without needing conversational context or code-specific outputs.

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An embedding model (e.g., textembedding-gecko@001).

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A chat model (e.g., chat-bison@001).

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A code generation model (e.g., code-bison@001).

Question 18

A company is using Vertex AI to generate email responses. They want to ensure sensitive customer data (PII) is not included in the output. What is the most effective approach?

Accepted Answer

Configure safety filters to block PII categories.. Safety filters in Vertex AI are specifically designed to block categories of harmful content, including PII, at the model's output layer. This provides a deterministic, automated guardrail that prevents sensitive data from being generated, unlike prompt-based instructions which can be overridden by the model's training. Safety filters operate on the model's response before it is returned, ensuring PII is caught even if the model attempts to generate it.

Answer

Use a system prompt instructing the model to avoid PII.

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Fine-tune the model on a dataset that excludes PII.

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Manually review each output before sending.

Question 19

Which TWO statements are true about generative AI models?

Accepted Answer

They are typically pre-trained on large datasets.. Generative AI models, such as GPT-4 or DALL-E, are typically pre-trained on vast, diverse datasets (e.g., terabytes of text or images) using unsupervised or self-supervised learning. This pre-training phase allows the model to learn statistical patterns, grammar, and world knowledge, which is then fine-tuned for specific tasks. Without this large-scale pre-training, the model would lack the foundational understanding needed to generate coherent and contextually relevant outputs.

Answer

They are deterministic by design.

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They always produce the same output for the same input.

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They require no data for training.

Question 20

A company is deploying a generative AI model for medical diagnosis support. Which THREE considerations are critical for responsible AI?

Accepted Answer

Ensure the training data is diverse and representative.. Diverse and representative training data is critical for responsible AI in medical diagnosis. If the data lacks diversity, the model may exhibit bias, leading to inaccurate or harmful diagnoses for underrepresented groups. This directly impacts fairness, safety, and regulatory compliance in healthcare AI.

Answer

Maximize model throughput to handle high volumes.

Answer

Use the cheapest model to reduce costs.