Generative AI Leader Practice Exam 2 — 20 Questions

Question 1

A data scientist wants to run large-scale distributed training of a custom deep learning model using Google's custom AI accelerators. Which infrastructure should they choose to minimize cost while leveraging Google's proprietary chips?

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

Accepted Answer

Cloud TPU v5e. Cloud TPU v5e is the correct choice because it is Google's proprietary custom AI accelerator designed specifically for large-scale distributed training of deep learning models, offering superior cost-efficiency compared to GPUs for many workloads. TPU v5e provides a balanced price-performance ratio for medium-to-large training tasks, and Google's TPU architecture is optimized for TensorFlow and JAX, enabling efficient scaling across multiple TPU pods. This minimizes cost while leveraging Google's custom chips, as opposed to using NVIDIA GPUs which are not Google's proprietary hardware.

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Compute Engine with NVIDIA A100 GPUs

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Vertex AI Workbench with custom machines

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Google Colab Pro with TPU runtime

Question 2

A sales team wants to use Gemini for Google Workspace to draft emails in Gmail. Which feature should they enable to generate full email drafts from a brief prompt?

Accepted Answer

Help me write. 'Help me write' is the specific Gemini for Google Workspace feature designed to generate full email drafts from a brief prompt. It leverages the underlying large language model to compose complete, context-aware email content, unlike features that only suggest short phrases or replies.

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Translate

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Smart Reply

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Smart Compose

Question 3

A company wants to generate a video from a text description using Google Cloud. Which service is designed for this?

Accepted Answer

Veo. Veo is Google Cloud's generative AI model specifically designed for creating high-quality videos from text or image prompts. It leverages advanced diffusion and transformer architectures to generate coherent video sequences, making it the correct choice for text-to-video generation.

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Codey

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Chirp

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Imagen

Question 4

An e-commerce company uses a generative AI model to generate product descriptions. They observe that descriptions for high-end products use more sophisticated language compared to budget products, potentially reinforcing class stereotypes. What is the most likely cause, and what should they do to mitigate it?

Accepted Answer

The training data reflects real-world associations; fine-tune with a balanced dataset that includes diverse product descriptions across price ranges. The bias stems from training data correlations. Fine-tuning on balanced, diverse data can reduce stereotypical associations. The other options either do not address the root cause or are less effective.

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The safety filters are too aggressive; relax them

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The temperature parameter is set too low; increase it to introduce more randomness

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The model architecture is biased; switch to a different base model

Question 5

A retail company wants to use GenAI to generate product descriptions. They have a small team of data scientists. What is the most efficient approach?

Accepted Answer

Use a foundation model API with prompt engineering and few-shot examples. Using a foundation model API with prompt engineering and few-shot examples is the most efficient approach for a small team. It leverages pre-trained models (e.g., GPT-4, Claude) via API calls, requiring no infrastructure or training data, while prompt engineering and few-shot examples allow the model to adapt to the company's product catalog with minimal effort and cost.

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Collect more data for several months before starting

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Train a model from scratch using their product data

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Buy a proprietary model from a startup

Question 6

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 7

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 8

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 9

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 10

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 11

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 12

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 13

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 14

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 15

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 16

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

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Vertex AI Agent Builder

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Vertex AI Model Garden

Question 17

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 18

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 19

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 20

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