Generative AI Leader Practice Test 4 — 25 Questions

Question 1

A company is migrating an on-premises NLP pipeline to Vertex AI. Which three capabilities of Vertex AI align with common MLOps best practices for generative AI? (Choose THREE)

Accepted Answer

Automatic model retraining based on performance degradation. Vertex AI's model monitoring can automatically trigger retraining when performance metrics (e.g., prediction drift or data drift) degrade below a threshold. This aligns with MLOps best practices for maintaining generative AI model quality over time without manual intervention.

Answer

Local on-premises execution

Answer

Manual data labeling only

Question 2

A company wants to use generative AI to summarize customer support tickets. Which Google Cloud tool is best suited for this task?

Accepted Answer

Vertex AI Text Generation (Gemini). Vertex AI Text Generation (Gemini) is the correct choice because it is a generative AI service specifically designed for natural language understanding and generation tasks, such as summarizing customer support tickets. Gemini models can process long-form text and produce concise, coherent summaries by leveraging transformer-based architectures fine-tuned for instruction-following and text completion. This makes it ideal for extracting key information from support conversations and generating actionable summaries.

Answer

Dialogflow CX

Answer

Document AI

Answer

AutoML Tables

Question 3

A company uses a text-to-image model to generate marketing visuals. The results often misinterpret the prompt, e.g., 'a red car' generates a blue car. Which technique should they try first to align the output with the prompt?

Accepted Answer

Refine the prompt with more adjectives and context, e.g., 'bright red sports car'. Refining the prompt with more adjectives and context directly addresses the root cause of misalignment: insufficient specificity in the text description. Text-to-image models rely on the semantic richness of the prompt to guide the latent diffusion process; adding 'bright red sports car' provides stronger conditioning signals that steer the model's cross-attention layers toward the intended color and object attributes. This is the most efficient first step before adjusting hyperparameters like guidance scale.

Answer

Use a negative prompt to exclude blue

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Upscale the image resolution to 1024x1024

Answer

Increase the guidance scale to 20

Question 4

A data scientist is building a text generation model and wants to evaluate bias using diverse test sets. Which Google AI Principle is directly relevant to this practice?

Accepted Answer

Avoid creating or reinforcing unfair bias. The principle of avoiding creating or reinforcing unfair bias directly calls for evaluating and mitigating bias, including using diverse test sets.

Answer

Be built and tested for safety

Answer

Incorporate privacy design principles

Answer

Be socially beneficial

Question 5

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.

Answer

Complete control over model architecture

Answer

Eliminates the need for prompt engineering

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Guaranteed absence of bias

Question 6

A global company deploys a generative AI chatbot in the European Union. They must ensure compliance with GDPR regarding user data used for fine-tuning the model. What is the MOST important requirement they must fulfill?

Accepted Answer

Obtain explicit consent from data subjects for using their data in fine-tuning. Under GDPR, using personal data for fine-tuning a generative AI model constitutes a new processing purpose that requires a lawful basis. Explicit consent (Article 7 and Article 9) is the most robust basis when relying on consent, as it must be freely given, specific, informed, and unambiguous. Without explicit consent, the company risks violating data minimization and purpose limitation principles, even if other anonymization or storage measures are applied.

Answer

Use only synthetic data for fine-tuning to avoid GDPR issues

Answer

Store all fine-tuned models only on US-based servers

Answer

Anonymize all data before fine-tuning, regardless of consent

Question 7

A company wants to generate images from text descriptions using Google Cloud. Which service should they use?

Accepted Answer

Vertex AI Imagen. Vertex AI Imagen is Google Cloud's purpose-built service for generating high-fidelity images from text descriptions using diffusion models. It directly addresses the requirement of text-to-image generation, offering capabilities like image editing, upscaling, and style transfer, which are not available in other Vertex AI or Vision services.

Answer

Vertex AI Gemini

Answer

Cloud Vision API

Answer

AutoML Vision

Question 8

A company wants to use a pre-trained language model for customer support summarization. They need to ensure responses are concise and accurate. Which prompt engineering technique is most effective?

Accepted Answer

Few-shot prompting with examples. Few-shot prompting (B) is most effective because it provides the model with a small set of example input-output pairs (e.g., a customer query and its concise summary), which guides the model to produce outputs that match the desired format, length, and accuracy. This technique is particularly useful for summarization tasks where consistency and adherence to a specific style are critical, as it reduces ambiguity without requiring fine-tuning.

Answer

Zero-shot prompting

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Chain-of-thought prompting

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Negative prompting

Question 9

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.

Answer

AI Platform Notebooks

Answer

Dialogflow CX

Answer

Cloud Composer

Question 10

During model evaluation, a team observes good performance on training data but poor on validation data. Which regularization technique is most appropriate to address this?

Accepted Answer

Apply dropout. The scenario describes overfitting, where the model memorizes training data but fails to generalize to unseen validation data. Dropout is a regularization technique that randomly deactivates a fraction of neurons during training, forcing the network to learn more robust features and reducing co-adaptation, which directly mitigates overfitting.

Answer

Add more training data

Answer

Increase the learning rate

Answer

Use a larger batch size

Question 11

A startup develops a generative AI tool for legal document review. To ensure explainability, they want the model to cite specific clauses from source documents when making assertions. Which technique should they use?

Accepted Answer

Grounding using a retrieval system that provides source documents. Grounding in generative AI means the model cites verifiable sources for its outputs, which is essential for explainability in domains like law.

Answer

Fine-tuning on legal documents with citation examples

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Chain-of-thought prompting

Answer

Prompt engineering to ask for citations

Question 12

You are a generative AI lead at a healthcare startup developing a system to summarize patient medical records for quick review by doctors. The system uses a fine-tuned LLM. After deployment, doctors report that the summaries often miss critical details like medication dosages and allergy information. The current pipeline preprocesses patient records by extracting text from EHR, feeding it to the LLM, and outputting a summary. The team has limited time and budget. They cannot retrain the model because it is hosted as a managed API. Which action should you take to most effectively improve the summarization quality without changing the model?

Accepted Answer

Revise the prompt to explicitly ask for medication dosages and allergies, and format the input text by adding headings (e.g., '### Medications') to emphasize important sections.. Prompt engineering is the most effective and cost-efficient way to improve LLM output without retraining or changing the model. By explicitly instructing the model to include medication dosages and allergies, and by structuring the input with clear headings, you guide the model's attention to critical sections, directly addressing the missing details. This approach leverages the LLM's existing capabilities and requires no changes to the hosted API or additional infrastructure.

Answer

Increase the maximum output token limit to force the model to include more details.

Answer

Replace the LLM with a simpler extractive summarization model that selects sentences from the original document.

Answer

Implement a retrieval-augmented generation (RAG) system that pulls supplementary data from external drug databases.

Question 13

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 14

A company is fine-tuning a Gemma model using Vertex AI. They observe that the model overfits. Which TWO actions should they take to mitigate overfitting?

Accepted Answer

Use more diverse data. Introducing more diverse data helps the model generalize better by exposing it to a wider variety of patterns, reducing the risk of memorizing noise from a limited dataset. Option E is correct because dropout randomly deactivates a fraction of neurons during fine-tuning, which prevents co-adaptation and acts as a regularization technique to combat overfitting in transformer-based models like Gemma.

Answer

Use a larger batch size

Answer

Increase the number of training epochs

Answer

Reduce the learning rate

Question 15

During a RAG pipeline implementation, the retrieval system frequently returns irrelevant documents, causing the generator to produce incorrect answers. Which change is most likely to improve the relevance of retrieved documents?

Accepted Answer

Add a re-ranking step using a cross-encoder model to refine the top results.. Re-ranking with a cross-encoder model evaluates query-document pairs more deeply, improving precision at the cost of latency. Increasing the number of documents may introduce more noise. Changing embedding dimension or chunk size may help but are less targeted.

Answer

Increase the number of documents retrieved from the vector store.

Answer

Use a different embedding model with higher vector dimension.

Answer

Decrease the chunk size of documents to reduce noise.

Question 16

Which TWO are components of the Vertex AI Generative AI Studio?

Accepted Answer

Model Garden. Model Garden is a core component of Vertex AI Generative AI Studio that provides a curated repository of foundation models, including Google's PaLM and Gemini models, as well as third-party models. It allows users to discover, compare, and deploy these models directly within the studio environment, making it essential for generative AI workflows.

Answer

Dataflow

Answer

Pipeline templates

Answer

Cloud Functions

Question 17

You are a machine learning engineer at a healthcare startup. Your team has developed a generative AI model that summarizes patient medical records. The model is deployed on Vertex AI Endpoints using a custom container. You have configured the endpoint with a single n1-standard-4 machine (4 vCPUs, 15 GB memory) without accelerators. The model uses a small transformer architecture. During load testing with 50 concurrent requests, you observe that the average latency is 8 seconds, which exceeds the requirement of 2 seconds. Additionally, some requests time out after 10 seconds. You suspect the CPU is the bottleneck. You also notice that the model inference code uses TensorFlow but is not optimized for inference. Which action should you take to reduce latency?

Accepted Answer

Deploy the model on a machine with a GPU and use TensorRT for inference optimization.. The CPU is identified as the bottleneck, and deploying on a GPU with TensorRT optimization directly addresses this by accelerating the TensorFlow inference. TensorRT optimizes the model graph and fuses layers, significantly reducing latency for transformer-based models, which is essential to meet the 2-second requirement.

Answer

Reduce the model size by pruning and quantization, then redeploy.

Answer

Increase the request timeout to 30 seconds to accommodate the latency.

Answer

Enable autoscaling to handle the load with multiple replicas.

Question 18

A machine learning engineer is building a text-to-image model using Vertex AI. They want to reduce inference latency. Which strategy is most effective?

Accepted Answer

Use a smaller model variant. Using a smaller model variant directly reduces the number of parameters and computational operations required per inference pass, which lowers latency. In text-to-image models like Imagen or Stable Diffusion, the model size is the primary driver of forward-pass time, so a smaller variant (e.g., fewer layers or reduced latent dimensions) yields faster generation.

Answer

Use a larger image resolution

Answer

Enable batch processing

Answer

Increase the number of inference steps

Question 19

A startup wants to quickly integrate a generative AI chatbot into their customer support platform. They need a solution that can answer questions based on their internal knowledge base with minimal setup. Which Google Cloud service should they use?

Accepted Answer

Use Vertex AI Agent Builder to create a conversational agent grounded in their data. Vertex AI Agent Builder (now part of Vertex AI Agent Platform) provides a low-code, out-of-the-box solution for building conversational agents that are grounded in enterprise data. It automatically handles retrieval-augmented generation (RAG) by indexing the startup's internal knowledge base into a vector store and orchestrating the LLM to answer questions using only that data, requiring minimal setup compared to manual integration or fine-tuning.

Answer

Use Model Garden to deploy a pre-built Q&A model

Answer

Call the Gemini API directly and implement grounding logic manually

Answer

Use Cloud AI Notebooks to fine-tune a model on their knowledge base

Question 20

A retail company wants to integrate generative AI into its customer service chatbot to handle routine inquiries. They have a limited budget and want to launch quickly. Which strategy is most appropriate?

Accepted Answer

Use pre-trained models via Google Cloud's Generative AI Studio API. Using pre-trained models via Google Cloud's Generative AI Studio API allows the company to leverage existing, powerful models without the high cost and time investment of custom development or fine-tuning. This approach enables rapid deployment on a limited budget by simply integrating the API into their chatbot, handling routine inquiries effectively without requiring extensive machine learning expertise or infrastructure.

Answer

Partner with a generative AI vendor for a custom solution

Answer

Fine-tune an open-source model on their customer service logs

Answer

Build a custom LLM from scratch using the company's own data