Generative AI Leader Timed Practice Test 3 — 10 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?

10 minute time limit — choose an answer to begin.

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