Generative AI Leader Fundamentals of Generative AI Practice Test 7 — 15 Questions

Question 1

A developer is using Vertex AI Generative AI Studio to fine-tune a PaLM 2 model for code generation. After training, they notice the model generates plausible but incorrect code. What is the most likely cause?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Overfitting to training data. When a PaLM 2 model generates plausible but incorrect code after fine-tuning, the most likely cause is overfitting to the training data. Overfitting occurs when the model memorizes specific code patterns, syntax, or even bugs from the fine-tuning dataset rather than learning generalizable programming logic. This results in outputs that look syntactically correct and contextually relevant but fail to execute properly or solve the intended problem, because the model has not learned the underlying algorithmic principles.

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Insufficient training steps

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Hallucination due to lack of grounding

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Prompt format mismatch

Question 2

A company wants to build a chatbot that answers questions using their internal knowledge base. Which approach is most suitable?

Accepted Answer

Use Retrieval-Augmented Generation (RAG). Retrieval-Augmented Generation (RAG) combines retrieval of relevant documents from a knowledge base with generative responses, making it ideal for this use case.

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Fine-tune a model on the knowledge base

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Train a new model from scratch

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Use zero-shot prompting with no context

Question 3

A company is deploying a generative AI model for medical advice. What is the most important consideration?

Accepted Answer

Safety and fairness. In medical advice applications, a generative AI model's outputs can directly impact patient health, making safety and fairness the paramount consideration. Incorrect or biased advice could lead to misdiagnosis or harm, outweighing performance metrics like latency or cost. Regulatory frameworks such as HIPAA and FDA guidelines for clinical decision support further mandate rigorous validation of model safety and fairness before deployment.

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Model latency

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Model size

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Cost of inference

Question 4

A team uses Vertex AI to host a large language model. They want to reduce latency for real-time applications. What is the best strategy?

Accepted Answer

Use model quantization. Model quantization reduces the precision of the model's weights (e.g., from FP32 to INT8), which decreases memory footprint and computational requirements, directly lowering inference latency for real-time applications on Vertex AI. This is a standard optimization technique for deploying large language models with minimal accuracy loss while meeting latency SLAs.

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Increase number of replicas

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Switch to a smaller model

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Use batch prediction instead of online

Question 5

A company has fine-tuned a foundation model on proprietary data. During evaluation, they find the model performs well on seen examples but poorly on unseen but similar tasks. What is the problem?

Accepted Answer

Domain shift between fine-tuning and deployment. The model performs well on seen examples (fine-tuning distribution) but poorly on unseen but similar tasks (deployment distribution), which is a classic symptom of domain shift. This occurs when the fine-tuning data does not fully represent the deployment environment, causing the model to fail on inputs that differ in subtle but systematic ways from the training distribution. The model has not generalized to the target domain despite being well-fitted to the source domain.

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Underfitting

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Catastrophic forgetting

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Distribution shift

Question 6

Which Google Cloud service provides a managed environment for prompt engineering and model evaluation?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio is the correct answer because it is a managed service within Vertex AI specifically designed for prompt engineering, model tuning, and evaluation of generative AI models. It provides a no-code interface for testing prompts, comparing model outputs, and iterating on prompt design, directly supporting the workflow described in the question.

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AI Platform Notebooks

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Dialogflow CX

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Cloud Composer

Question 7

A company wants to generate images from text descriptions. Which model in Vertex AI Model Garden should they use?

Accepted Answer

Imagen. Imagen is the correct choice because it is a text-to-image model in Vertex AI Model Garden specifically designed to generate high-fidelity images from natural language descriptions. Unlike the other options, Imagen uses a diffusion-based architecture to create photorealistic visuals, making it the only option that directly addresses the requirement of generating images from text.

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Chirp

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Codey

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PaLM 2

Question 8

Which TWO of the following are best practices for prompt engineering?

Accepted Answer

Provide context and examples in the prompt. Clear and specific instructions help guide the model, and providing context and examples improves output quality. Options B, D, and E are not recommended.

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Append random noise to prompts to improve creativity

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Always use the maximum possible number of tokens

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Use negative prompts to discourage undesired outputs

Question 9

Which TWO are benefits of using pre-trained foundation models instead of training from scratch?

Accepted Answer

Lower training cost. Pre-trained foundation models have already been trained on vast datasets, so you only need to fine-tune them for your specific task. This dramatically reduces the compute resources, time, and data required compared to training from scratch, directly lowering training cost.

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Complete control over model architecture

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Eliminates the need for prompt engineering

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Guaranteed absence of bias

Question 10

Refer to the exhibit. A team has deployed a model to an endpoint with the configuration shown. They notice that during peak traffic, the endpoint frequently returns 429 (Too Many Requests) errors. Which action should they take to resolve this issue?

Accepted Answer

Increase MIN_REPLICA_COUNT to 5. Increasing MIN_REPLICA_COUNT ensures a minimum number of replicas are always available to handle traffic bursts, reducing 429 errors. Other options would not help or would worsen the problem.

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Change MACHINE_TYPE to n1-highmem-4

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Decrease MAX_REPLICA_COUNT to 1

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Disable autoscaling by setting MIN_REPLICA_COUNT equals MAX_REPLICA_COUNT

Question 11

Refer to the exhibit. A company has this IAM policy on a Vertex AI project. Alice complains she cannot create a new model. What is the most likely reason?

Accepted Answer

She needs the roles/aiplatform.admin role. The IAM policy shown in the exhibit likely grants only basic permissions (e.g., roles/aiplatform.user) which do not include the ability to create models. The roles/aiplatform.admin role provides full administrative access, including model creation, deletion, and management across the Vertex AI project. Without this role, Alice lacks the necessary aiplatform.models.create permission, which is why she cannot create a new model.

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She needs the roles/aiplatform.modelUser role

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She needs the roles/aiplatform.modelAdmin role

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She needs the roles/aiplatform.modelCreator role

Question 12

Refer to the exhibit. A data scientist is fine-tuning a model. The training loss and accuracy are improving each epoch. However, after training, the model performs poorly on a held-out validation set. What is the most likely issue?

Accepted Answer

Overfitting. The model's training loss and accuracy improve each epoch, but performance on the validation set is poor. This classic symptom indicates overfitting, where the model memorizes the training data (including noise) rather than learning generalizable patterns. In fine-tuning, this often occurs when the model is trained for too many epochs or the dataset is too small relative to model capacity.

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Underfitting

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Inappropriate learning rate

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Data leakage

Question 13

A startup is building a customer service chatbot that generates responses in real-time. They want the model to have up-to-date information on the latest product catalog but cannot afford frequent fine-tuning. Which technique should they use to inject current data into the model without retraining?

Accepted Answer

Use retrieval-augmented generation (RAG) to fetch relevant documents from a vector database at inference time.. Retrieval-Augmented Generation (RAG) is the correct technique because it allows the chatbot to fetch the most current product catalog entries from an external vector database at inference time, without requiring any model retraining. This keeps responses grounded in up-to-date information while avoiding the cost and latency of frequent fine-tuning.

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Rely on the model's zero-shot capabilities to infer product details.

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Craft detailed system prompts that include the entire product catalog in the prompt.

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Fine-tune the base model weekly on the latest product catalog.

Question 14

A medical imaging team wants to generate synthetic X-ray images to augment a training dataset for a rare disease. Which type of generative model is most suitable for generating high-fidelity, realistic medical images?

Accepted Answer

Diffusion model. Diffusion models are the most suitable for generating high-fidelity, realistic medical images because they iteratively denoise random noise into a coherent image through a learned reverse diffusion process, which produces superior sample quality and diversity compared to GANs, especially for complex, high-dimensional data like X-rays. Their training stability and ability to model fine-grained anatomical details without mode collapse make them the current state-of-the-art for medical image synthesis.

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Generative Adversarial Network (GAN)

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Variational Autoencoder (VAE)

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Autoregressive transformer (e.g., PixelCNN)

Question 15

An enterprise deploys a large language model (LLM) for internal document summarization. Users complain that summaries sometimes include statements not present in the original document. Which mitigation strategy should the team prioritize to address this hallucination issue?

Accepted Answer

Implement retrieval-augmented generation (RAG) to ground the model in the original documents and require citations.. Retrieval-Augmented Generation (RAG) is the most direct and effective mitigation for hallucination in document summarization because it forces the LLM to base its output on retrieved chunks of the original document. By requiring citations, the model must reference specific passages, making it verifiable and reducing the likelihood of fabricating content. This grounds the generation in the source material, addressing the root cause of hallucination—lack of factual grounding—rather than relying on post-hoc correction or output tuning.

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Train a discriminator model to detect hallucinations and perform adversarial training.

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Apply reinforcement learning from human feedback (RLHF) using a reward model that penalizes hallucinations.

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Reduce the model's temperature parameter to 0 to make outputs deterministic.