Generative AI Leader Practice Exam 4 — 20 Questions

Question 1

An organization is deploying a summarization model on Vertex AI and needs to ensure that the model's responses are consistent and avoid hallucinations. They have a labeled dataset of source documents and human-written summaries. Which approach would best align the model with their quality requirements?

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

Accepted Answer

Perform supervised fine-tuning using their labeled dataset. Supervised fine-tuning (D) directly optimizes the model's weights using the labeled dataset of source documents and human-written summaries, which teaches the model to produce consistent, factual outputs and reduces hallucinations. This approach aligns the model's behavior with the specific quality requirements by learning from ground-truth examples, unlike prompt engineering or parameter adjustments that do not modify the underlying model.

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Deploy the model with a larger max_output_tokens

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Use prompt engineering with few-shot examples

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Increase the temperature to 0.9

Question 2

A company is deploying a generative AI system for medical diagnosis. Which TWO measures are essential for responsible AI in this high-stakes domain?

Accepted Answer

Ensure a human medical professional reviews all AI-generated diagnoses. High-stakes domains require human oversight and transparency. Human review ensures accountability, and Model Cards communicate capabilities and limitations to stakeholders.

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Allow the AI to make autonomous decisions in time-sensitive emergencies

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Publish all patient data used for training to ensure transparency

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Use the AI only for administrative tasks, not diagnosis

Question 3

A team deployed a custom generative AI model using KServe on Google Kubernetes Engine (GKE) with the above configuration. They notice that the model is taking longer than expected to respond. What is the most likely cause?

Accepted Answer

The model requires more than 1 GPU for acceptable performance. The configuration specifies 1 GPU, but the model requires more than 1 GPU for acceptable performance. KServe on GKE allocates GPU resources based on the `limits` field; if the model's inference workload exceeds the memory bandwidth or compute capacity of a single GPU, latency increases due to queuing and serialization. This is the most likely cause of the slow response time, as GPU-bound models are sensitive to under-provisioning.

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The CPU resource limits are too low

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The model is crashing due to insufficient memory

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The container image is too large and takes time to pull

Question 4

A company uses a generative model to produce product descriptions. The descriptions are factually inconsistent with the product specs. Which technique would best ensure factual accuracy?

Accepted Answer

Implement retrieval-augmented generation (RAG) with product database. Retrieval-augmented generation (RAG) is the best technique because it dynamically retrieves relevant, up-to-date product specifications from a trusted database at inference time, grounding the model's output in verified facts. This directly addresses factual inconsistency by ensuring the generated description is based on authoritative source data rather than relying solely on the model's parametric memory.

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Enhance the system prompt with product details

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Lower the temperature to 0.0

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Fine-tune the model on product descriptions

Question 5

A financial services firm needs to deploy a large language model (LLM) for analyzing sensitive client documents. They require the model to run within their Virtual Private Cloud (VPC) with no internet access and must comply with data residency regulations. Which Google Cloud generative AI offering should they use?

Accepted Answer

Vertex AI Model Garden with private endpoints and VPC Service Controls. Vertex AI Model Garden with private endpoints and VPC Service Controls allows the LLM to be deployed entirely within the customer's VPC, with no internet egress, and enforces data residency by restricting data movement to the configured VPC boundary. Private endpoints use Private Service Connect to route inference traffic through internal IPs, while VPC Service Controls prevent data exfiltration and ensure compliance with residency regulations.

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Vertex AI Search

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Cloud Run

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Vertex AI Workbench

Question 6

A retail company wants to build a customer service chatbot that can handle returns, order status, and FAQs. They need to integrate with their existing backend systems. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Agent Builder. Vertex AI Agent Builder is the correct choice because it provides a low-code platform specifically designed for building conversational AI agents (chatbots) that can be integrated with enterprise backend systems via APIs, connectors, and custom tools. It supports grounding in enterprise data, multi-turn dialogue management, and seamless integration with existing systems for handling returns, order status, and FAQs, making it the most suitable service for this use case.

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Vertex AI Model Garden

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Vertex AI Search

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Vertex AI Codey API

Question 7

Refer to the exhibit. The endpoint is experiencing high latency during traffic spikes. The team wants to improve response time by reducing queueing. Which change to the configuration would be most effective?

Accepted Answer

Increase maxReplicaCount to 10. Increasing maxReplicaCount to 10 allows the autoscaler to provision more replicas during traffic spikes, distributing the incoming requests across additional endpoints. This directly reduces queueing at each replica because the load is spread over more instances, lowering per-instance latency. The change targets the root cause—insufficient capacity to handle peak load—rather than adjusting thresholds or model versions.

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Decrease minReplicaCount to 0

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Change the model version to '2'

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Decrease the target value in autoscaling metric to 50

Question 8

A retail company wants to deploy a generative AI chatbot to assist customers with product recommendations. The chatbot must align with the company's brand voice and provide accurate, up-to-date information. Which strategy should the company prioritize when developing this solution?

Accepted Answer

Ground the model with proprietary product data and brand guidelines in a retrieval-augmented generation (RAG) architecture.. Retrieval-augmented generation (RAG) allows the chatbot to ground its responses in the company's proprietary product data and brand guidelines, ensuring factual accuracy and brand consistency. By retrieving relevant information from a curated knowledge base at inference time, the model can provide up-to-date recommendations without requiring retraining, which is critical for a retail environment with frequently changing inventory.

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Use a generic pre-trained model without customization to reduce development time.

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Deploy a large language model with a feedback loop to iteratively improve responses.

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Train the model on public customer reviews to capture common preferences.

Question 9

A data analyst wants to experiment with ML models using a free, cloud-based Jupyter notebook environment with GPU support. Which Google tool should they use?

Accepted Answer

Google Colab. Google Colab is a free, cloud-based Jupyter notebook environment that provides access to GPUs (e.g., NVIDIA T4 or V100) without requiring any setup or billing. It is specifically designed for experimentation and learning with ML models, making it the correct choice for a data analyst seeking a free, GPU-enabled notebook environment.

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Cloud Shell

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BigQuery Studio

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Vertex AI Workbench

Question 10

Which TWO Google Cloud services can be used together to implement a RAG (retrieval-augmented generation) pipeline? (Select 2)

Accepted Answer

Vertex AI Vector Search. Vertex AI Vector Search (option B) is correct because it provides a managed vector database for storing and querying embeddings, which is essential for the retrieval step in a RAG pipeline. It enables semantic similarity search over large datasets, allowing the system to fetch relevant context documents based on a user query.

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Cloud SQL

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Bigtable

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Cloud Functions

Question 11

A media company uses generative AI to produce personalized news summaries for subscribers. They notice that the summaries sometimes contain factual inaccuracies, leading to customer complaints. The team needs to improve accuracy without slowing down the generation speed. They are using a pre-trained model via Vertex AI. What strategy should they implement?

Accepted Answer

Implement retrieval-augmented generation (RAG) with a trusted knowledge base. Retrieval-augmented generation (RAG) grounds the model's output in a trusted, external knowledge base, allowing it to retrieve verified facts in real time without retraining. This directly addresses factual inaccuracies while maintaining generation speed, as the pre-trained model remains unchanged and only the retrieval step is added. RAG avoids the latency of human review and the computational cost of fine-tuning or switching models.

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Switch to a larger, more accurate foundation model

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Fine-tune the model on a dataset of verified news articles

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Add a human-in-the-loop review for every summary

Question 12

An enterprise wants to adopt GenAI across departments but faces resistance from legal and compliance. Which strategy should the AI leader prioritize?

Accepted Answer

Build a comprehensive governance framework covering data use, review, and monitoring. Legal and compliance resistance stems from concerns about data privacy, regulatory adherence, and model accountability. A comprehensive governance framework directly addresses these by defining data usage policies, implementing review mechanisms for model outputs, and establishing continuous monitoring to detect drift or bias, which is essential for enterprise-grade GenAI deployment.

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Outsource the entire initiative to a consulting firm

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Deploy a single pilot in a low-risk department to demonstrate value

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Mandate use of GenAI through executive order

Question 13

A financial institution wants to deploy a gen AI model for fraud detection but must comply with strict regulations regarding explainability. What is the best strategy?

Accepted Answer

Implement a smaller interpretable model with acceptable accuracy. Regulatory compliance for fraud detection demands explainability, which complex black-box models cannot provide. A smaller interpretable model (e.g., logistic regression or decision tree) offers transparency into decision factors, satisfying regulations like GDPR's right to explanation while maintaining acceptable accuracy for the use case.

Answer

Use Vertex AI Explainable AI with a complex model

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Deploy multiple models and ensemble

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Use a large black-box model and rely on external auditing

Question 14

A company is choosing between Gemini Pro and Gemini Ultra for a document summarization task. Which THREE factors should they consider when deciding between the two model variants? (Choose THREE)

Accepted Answer

Model capability and accuracy. Model capability and accuracy (A) is correct because Gemini Pro is optimized for high-throughput, cost-efficient tasks with lower accuracy demands, while Gemini Ultra is designed for complex, high-accuracy reasoning. The choice directly impacts the quality of the summarization output, as Ultra uses a larger parameter count and more advanced attention mechanisms to handle nuanced context, whereas Pro may struggle with ambiguous or lengthy documents.

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Availability in Google AI Studio

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Multimodal support

Question 15

A team is building a multilingual customer support chatbot using Gemini. They notice that for low-resource languages, the model frequently produces grammatically incorrect responses. Which strategy would MOST effectively improve quality for these languages without sacrificing latency?

Accepted Answer

Use few-shot prompting with high-quality examples in the target language. Few-shot prompting with high-quality examples in the target language directly conditions the model to produce grammatically correct responses for low-resource languages without adding inference latency. This approach leverages Gemini's in-context learning capability, allowing it to adapt to the target language's grammar patterns without requiring additional model training or translation round-trips.

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Implement a separate translation step: translate user input to English, generate response in English, then translate back

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Fine-tune the model on a large parallel corpus for each low-resource language

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Increase temperature to 1.5 to encourage more diverse grammar

Question 16

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.

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Local on-premises execution

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Manual data labeling only

Question 17

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.

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Dialogflow CX

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Document AI

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AutoML Tables

Question 18

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.

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Use a negative prompt to exclude blue

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Upscale the image resolution to 1024x1024

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Increase the guidance scale to 20

Question 19

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.

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Be built and tested for safety

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Incorporate privacy design principles

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Be socially beneficial

Question 20

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