Generative AI Leader Practice Test 2 — 25 Questions

Question 1

A startup wants to use a pre-trained model to generate product descriptions without training. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio is the correct service because it provides a no-code interface to access and experiment with pre-trained generative models, including text generation for product descriptions, without requiring any training or custom model development. It allows users to directly prompt models like PaLM 2 or Gemini for inference tasks, making it ideal for generating content from a pre-trained model without training.

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Vertex AI Prediction

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AI Platform Training

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Cloud AutoML

Question 2

A healthcare company is using Vertex AI to build a generative AI assistant that helps doctors draft clinical notes. The assistant uses a fine-tuned PaLM 2 model deployed on a private endpoint. Recently, doctors have reported that the assistant takes over 30 seconds to respond, causing workflow delays. Additionally, the monthly Vertex AI costs have increased by 40% without a proportional increase in usage. The model responses are generally accurate but sometimes include irrelevant details. The company wants to improve response time and cost while maintaining acceptable quality. A review of logs shows that most requests are for similar note types (e.g., progress notes, discharge summaries) and that the same prompt is used repeatedly with minor variations. What should the company do first?

Accepted Answer

Implement response caching for common queries and batch process similar requests. The logs show that most requests are for similar note types with repeated prompts, making response caching ideal for reducing latency and cost. Caching stores responses for identical or near-identical queries, eliminating redundant inference calls, which directly addresses the 30-second response time and 40% cost increase without sacrificing quality.

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Switch to a larger model (e.g., Gemini 1.5 Pro) to improve response quality and reduce irrelevant details

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Increase the Vertex AI endpoint's maximum request quota to handle concurrent requests

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Apply model quantization (e.g., INT8) to reduce model size and inference time

Question 3

A healthcare organization is developing a generative AI system to assist doctors with clinical decision support. They are concerned about regulatory compliance (e.g., HIPAA) and potential liability. What is the most important business strategy to mitigate these risks?

Accepted Answer

Implement a human-in-the-loop review process with clear accountability for AI-generated recommendations.. A human-in-the-loop (HITL) review process ensures that all AI-generated recommendations are verified by a qualified clinician before action, directly addressing HIPAA accountability requirements and reducing liability by maintaining a clear chain of responsibility. This strategy aligns with regulatory frameworks that mandate human oversight for high-risk clinical decisions, as the AI system itself cannot be held liable under current laws.

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Limit the system to non-critical administrative tasks only.

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Use an open-source model to avoid vendor lock-in and reduce costs.

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Fully automate the system to reduce human error.

Question 4

A developer wants to use Gemini 1.5 Pro to analyze hour-long video content and generate a summary. Which feature of Gemini 1.5 Pro is most suitable for this task?

Accepted Answer

Long context window (up to 1 million tokens). Gemini 1.5 Pro's long context window of up to 1 million tokens allows it to process and analyze hour-long video content in a single pass, including both audio and visual frames. This capability is essential for generating a coherent summary of long-form video, as it can retain and reason over the entire video's context without needing to chunk or downsample the content.

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Multimodal generation from text and images

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Code generation and debugging

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Function calling to retrieve external data

Question 5

An organization is using Vertex AI to fine-tune a large language model. They notice training is taking longer than expected and cost is increasing. Which action is most likely to reduce training time and cost without significantly impacting model quality?

Accepted Answer

Enable mixed-precision training (bfloat16). Mixed-precision training with bfloat16 reduces memory usage and accelerates computation by using half the bits of standard float32, which directly decreases training time and cost on TPUs and modern GPUs. Vertex AI supports bfloat16 natively on TPU v3+ and A100 GPUs, and for many large language models, this precision preserves model quality because bfloat16 retains the same exponent range as float32, avoiding underflow issues common with float16.

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Increase the number of training steps

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Increase the batch size

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Use a higher learning rate

Question 6

A security team wants to prevent prompt injection attacks on their generative AI application hosted on Vertex AI. Which best practice should they implement?

Accepted Answer

Implement input validation and output filtering. Prompt injection attacks exploit the model's inability to distinguish between user instructions and untrusted input. Implementing input validation (e.g., sanitizing special characters or known injection patterns) and output filtering (e.g., using a classifier to detect and block malicious responses) directly mitigates this risk by controlling what the model processes and returns. On Vertex AI, this can be enforced via custom safety attributes or integration with services like Cloud DLP for data loss prevention.

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Use a custom model instead of a foundation model

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Disable all logging

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Use a private endpoint

Question 7

A data scientist is using the Vertex AI PaLM API for text generation. They notice that the model occasionally generates toxic content. Which parameter should they adjust to reduce the likelihood of toxic outputs?

Accepted Answer

safety_settings. Safety settings in the Vertex AI PaLM API allow you to configure thresholds for filtering harmful content categories (e.g., toxicity, harassment, hate speech). By adjusting these settings, you can block or reduce the likelihood of toxic outputs before they are returned, directly addressing the problem without altering the model's creativity or randomness.

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max_output_tokens

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temperature

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top_k

Question 8

A company deploys a Gemini model on Vertex AI for a healthcare application. They need to ensure that the model does not generate medical advice and that responses are grounded in trusted medical sources. Which combination of safety measures should they implement?

Accepted Answer

Enable safety filters and use Vertex AI Grounding with a labeled medical dataset. It combines two essential safety layers: safety filters block harmful content (including medical advice), and Vertex AI Grounding anchors responses to a labeled medical dataset, ensuring factual accuracy and compliance with healthcare regulations. This dual approach prevents the model from generating unverified or dangerous medical information while maintaining relevance to trusted sources.

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Use Vertex AI Grounding with a public dataset and disable safety filters

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Enable safety filters only, without grounding

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Fine-tune the model on a curated medical dataset and disable safety filters for faster responses

Question 9

What is the key advantage of using vector search for retrieval in a RAG system compared to keyword search?

Accepted Answer

Vector search can find conceptually similar documents even without exact keyword matches. Vector search in a RAG system encodes documents and queries into dense vector embeddings using a foundation model, then retrieves documents based on semantic similarity in the embedding space. This allows it to find conceptually related documents even when they share no exact keywords with the query, overcoming the lexical gap that limits keyword search.

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Vector search eliminates the need for a foundation model

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Vector search is faster than keyword search

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Vector search requires no preprocessing of documents

Question 10

A cloud architect is designing a generative AI pipeline that must comply with the EU AI Act for high-risk AI systems. Which of the following is a mandatory requirement under the Act?

Accepted Answer

The system must undergo a conformity assessment before deployment. Under the EU AI Act, high-risk AI systems must undergo a conformity assessment before deployment to ensure compliance with requirements such as risk management, data governance, and transparency. This is a mandatory procedural step, not a performance metric or specific reasoning technique. The assessment may involve self-evaluation or third-party review depending on the system's risk category.

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The system must be explainable using chain-of-thought reasoning

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The system must achieve a minimum accuracy of 90% on validation data

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The system must be trained on data that is representative of the target population

Question 11

A company wants to generate marketing images for a new product launch using GenAI. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Imagen. Imagen on Vertex AI is Google Cloud's image generation model, available through Vertex AI APIs or Model Garden.

Answer

Gemini for Google Workspace

Answer

Vertex AI Agent Builder

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Vertex AI Model Garden

Question 12

A company runs a pilot for a GenAI-powered internal knowledge base assistant. They want to measure adoption. Which metric is BEST for this purpose?

Accepted Answer

Number of unique users per week divided by total employees. The best metric for measuring adoption because it directly captures the breadth of usage across the organization. Adoption is defined as the proportion of the target user base that actively uses the system, and dividing unique weekly users by total employees provides a clear percentage of uptake, which is the standard measure for adoption in enterprise GenAI deployments.

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Number of follow-up questions asked per session

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User satisfaction score from surveys

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Average response time of the assistant

Question 13

A financial services company uses a generative AI model to produce customer-facing investment advice. They need to ensure the model's outputs can be traced back to specific sources. Which explainability technique is BEST suited for this requirement?

Accepted Answer

Grounding (citing sources). Grounding (citing sources) is the best technique because it directly links each generated output to specific, verifiable source documents or data points. This ensures traceability, which is critical for regulated financial advice where every claim must be attributable to a known reference, such as a regulatory filing or market data feed.

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Confidence indicators

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Model Cards

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Chain-of-thought reasoning

Question 14

A data science team wants to run a large-scale transformer training job with custom model architectures. They need the highest compute density for a multi-node job and want to minimize inter-node communication latency. Which Google Cloud infrastructure is BEST suited for this workload?

Accepted Answer

TPU v4 Pod slices with high-speed inter-chip interconnect. TPU v4 Pod slices provide the highest compute density for multi-node transformer training by using a custom 3D torus interconnect with 800 Gbps per chip bandwidth, which minimizes inter-node communication latency far below what standard networking can achieve. This architecture is specifically designed for large-scale model parallelism, making it ideal for custom transformer architectures that require frequent all-reduce and collective communication operations.

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A single TPU v5e VM with multiple accelerators

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A cluster of A100 GPU VMs connected via standard networking

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Cloud Run jobs with GPU acceleration

Question 15

A startup wants to generate product descriptions from a few keywords using a large language model. They have no prior ML experience and need the fastest time-to-market. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Studio. Vertex AI Studio provides a no-code/low-code environment with pre-trained foundation models and prompt templates, enabling rapid generation of product descriptions from keywords without any ML expertise. It offers the fastest time-to-market because it eliminates the need for custom model training, infrastructure setup, or coding, directly leveraging Google's generative AI capabilities through a simple interface.

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Vertex AI Workbench with custom training

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Vertex AI Agent Builder

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Vertex AI Model Garden

Question 16

A company is deploying a generative AI system for resume screening. They want to ensure fairness and avoid bias. Which TWO actions should they take? (Choose 2)

Accepted Answer

Evaluate the model's decisions across gender and ethnicity groups using a diverse test set. Option A is correct because fairness auditing requires measuring model outcomes across demographic subgroups (e.g., gender, ethnicity) using a diverse, representative test set, so disparate impact or bias can be detected and quantified before deployment. Option B is correct because if the training data is not representative of the candidate population, the model will learn skewed patterns and produce biased predictions, so ensuring representative training data is a foundational bias-mitigation step. Option C is incorrect because SynthID is a watermarking technique for identifying AI-generated content, not a fairness or bias-mitigation control for screening decisions. Option D is incorrect because simply removing protected attributes does not eliminate bias, since proxy variables and correlated features can still encode discriminatory patterns. Option E is incorrect because a larger model improves capacity or accuracy but does not inherently ensure fairness or reduce bias.

Answer

Apply SynthID watermarking to all generated decisions

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Remove protected attributes like gender and race from training data

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Use a larger model to improve accuracy

Question 17

A financial services firm is developing a GenAI application for investment advice. They need to ensure regulatory compliance. Which business strategy should they prioritize?

Accepted Answer

Implement strict human-in-the-loop review for all investment recommendations. In regulated industries like financial services, GenAI applications must prioritize compliance over speed. Option B is correct because a human-in-the-loop (HITL) review ensures that every investment recommendation is auditable and meets regulatory standards (e.g., SEC or FINRA rules), mitigating risks of hallucinated or non-compliant outputs. This strategy directly addresses the need for accountability and transparency in high-stakes decision-making.

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Rapidly deploy an MVP and iterate based on user feedback

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Open-source the model to gain community trust

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Partner with a cloud provider that offers indemnification for model outputs

Question 18

Which TWO features are available in Vertex AI Studio for prompt engineering? (Choose two.)

Accepted Answer

Side-by-side comparison of model outputs. Vertex AI Studio provides a side-by-side comparison feature that allows prompt engineers to evaluate outputs from multiple model configurations or parameter settings simultaneously. This enables direct visual comparison of responses, helping to identify the most effective prompt phrasing or parameter combination without manual switching.

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One-click deployment to a Vertex AI endpoint

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Fine-tuning models directly in the interface

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Building conversational agents with drag-and-drop

Question 19

Which TWO features are available in Vertex AI Agent Builder to enhance the conversational abilities of an agent? (Choose TWO.)

Accepted Answer

Slot filling. Slot filling is correct because it allows the agent to collect required parameters (slots) from the user during a conversation, enabling multi-turn interactions to fulfill complex requests. In Vertex AI Agent Builder, slot filling is a core feature for conversational agents, as it systematically prompts for missing information (e.g., date, location) until all necessary slots are filled, enhancing the agent's ability to handle dynamic user inputs.

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Sentiment analysis

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Code execution

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Knowledge base integration

Question 20

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.

Answer

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