Generative AI Leader Practice Test 31 — 15 Questions

Question 1

A company is considering using gen AI for customer support. Which two business strategies are most important for success?

Accepted Answer

Measure customer satisfaction metrics. Measuring customer satisfaction metrics (A) is critical because it provides quantitative feedback on the generative AI system's performance, enabling iterative improvements to the model's responses and alignment with business goals. Without metrics like CSAT or NPS, the company cannot validate whether the AI is reducing resolution time or improving user experience, which are key ROI indicators for gen AI deployments.

Answer

Ignore data privacy

Answer

Deploy without testing

Answer

Use the cheapest model

Question 2

A company wants to measure the business impact of a GenAI content generation tool. Which metric is most appropriate?

Accepted Answer

Reduction in content production time. The primary business impact of a GenAI content generation tool is operational efficiency, measured by the reduction in content production time. This metric directly correlates to cost savings and faster time-to-market, which are key business outcomes. Unlike technical metrics, it reflects real-world value delivery.

Answer

Number of model parameters

Answer

Model accuracy on a test set

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Training loss

Question 3

A startup wants to leverage Google Cloud's generative AI but has limited ML expertise. Which Google Cloud service allows them to build generative AI applications without deep ML knowledge?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio is a managed service that provides a low-code/no-code interface for building, testing, and deploying generative AI applications using pre-trained foundation models. It abstracts away the complexities of model training, infrastructure management, and ML pipeline orchestration, enabling teams with limited ML expertise to leverage generative AI capabilities through simple prompts and visual workflows.

Answer

Cloud TPU

Answer

TensorFlow

Answer

Apigee

Question 4

A retail company wants to generate personalized marketing content (emails, social posts) at scale using generative AI. They need consistent brand voice and the ability to review outputs before publishing. Which two Google Cloud capabilities should they use? (Choose TWO)

Accepted Answer

Google Workspace Duet AI in Docs for drafting, then manual review. Google Workspace Duet AI in Docs allows marketers to draft personalized content using generative AI while maintaining control over brand voice through iterative editing. The manual review step ensures outputs meet quality and compliance standards before publishing, addressing the need for human oversight in content generation.

Answer

AutoML Tables for predicting customer segments

Answer

Pre-trained Gemini model via Vertex AI API with no customization

Answer

Cloud Vision API for image analysis

Question 5

A multinational corporation is using Vertex AI to generate multilingual customer support responses. They have fine-tuned the Gemini model on support tickets in English and now want to extend to 10 additional languages. The fine-tuning dataset for new languages is small (1000 tickets each). During evaluation, the model performs well for common languages (Spanish, French) but poorly for languages like Finnish and Thai. The team needs to improve performance for low-resource languages. They have budget constraints and cannot collect more data quickly. Which approach should they take?

Accepted Answer

Use a multilingual foundation model and fine-tune with cross-lingual transfer learning techniques.. Using a multilingual foundation model (like Gemini's multilingual variant) with cross-lingual transfer learning leverages the model's pre-trained knowledge across languages, allowing it to generalize from high-resource languages (Spanish, French) to low-resource ones (Finnish, Thai) even with small fine-tuning datasets. This approach is budget-friendly as it avoids separate models or costly data collection, and it directly addresses the performance gap by sharing linguistic patterns across languages.

Answer

Switch to Vertex AI Codey API for generating responses in all languages.

Answer

Deploy separate fine-tuned models for each language.

Answer

Collect more training data for low-resource languages via crowdsourcing.

Question 6

Refer to the exhibit. This JSON describes a Vertex AI endpoint with a deployed model. Which statement about scaling is true?

Accepted Answer

The endpoint uses only dedicated resources, no automatic scaling. The JSON shows that the endpoint is configured with `dedicatedResources` and no `autoscalingMetricSpecs` or `minReplicaCount`/`maxReplicaCount` fields. In Vertex AI, when you specify only `machineSpec` and a fixed `minReplicaCount` (here implicitly 1) without a `maxReplicaCount` or autoscaling metrics, the endpoint uses dedicated resources with no automatic scaling — the model will always run on exactly the number of replicas you define, regardless of load.

Answer

The endpoint will automatically scale based on GPU utilization

Answer

The endpoint will scale from 1 to 3 replicas based on load using automatic scaling

Answer

The endpoint can scale to zero when not in use

Question 7

Which of the following is a key Google AI Principle that directly addresses the need to avoid creating or reinforcing unfair bias?

Accepted Answer

Avoid creating or reinforcing unfair bias. It is the exact wording of one of Google's seven AI Principles, which explicitly states the commitment to 'avoid creating or reinforcing unfair bias.' This principle directly addresses the need to mitigate bias in AI systems, such as ensuring training datasets are representative and algorithms do not perpetuate historical inequities. It is a foundational directive for responsible AI development, distinct from broader accountability, safety, or privacy concerns.

Answer

Be accountable to people

Answer

Be built and tested for safety

Answer

Incorporate privacy design principles

Question 8

A data science team wants to compare semantic similarity between thousands of customer reviews to identify emerging themes. Which Google Cloud service and approach should they use?

Accepted Answer

Use Vertex AI Embeddings API to generate embeddings, index them in Vertex AI Vector Search, and query for nearest neighbors. Vertex AI Embeddings API generates dense vector representations of text, which can be indexed in Vertex AI Vector Search for efficient approximate nearest neighbor (ANN) search. This approach scales to thousands of reviews and allows the team to query for the most semantically similar reviews, enabling theme discovery without pairwise computation.

Answer

Use BigQuery ML to train a custom similarity model on the reviews

Answer

Use the Gemini API to compute semantic similarity directly

Answer

Use Vertex AI Embeddings API to generate embeddings and then compute cosine similarity pairwise

Question 9

A data scientist is evaluating a generative AI model for gender bias in its text outputs. They have a test set of 1,000 gender-neutral prompts. Which approach is MOST appropriate for measuring output bias?

Accepted Answer

Analyze the gender of characters, roles, and pronouns in the model's completions. To measure bias, the test set should include prompts that are neutral in gender but may elicit biased responses. The correct approach is to analyze the gender of pronouns, roles, or descriptors in the model's completions.

Answer

Check the gender distribution of the training data

Answer

Use a toxicity classifier to flag any biased outputs

Answer

Ask the model to self-report its confidence in avoiding bias

Question 10

A research team is training a large multimodal model and needs to minimize training time for a fixed budget. Which Google Cloud infrastructure is specifically designed for large-scale training workloads?

Accepted Answer

TPU Pods. TPU pods are purpose-built for large-scale ML training, offering high-bandwidth interconnect and optimized performance for TensorFlow/JAX.

Answer

Compute Engine with A100 GPUs

Answer

Kubernetes Engine with GPU nodes

Answer

Cloud TPU v5e single chip

Question 11

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. The requirement is twofold: generate reports from mixed data and guarantee that outputs never contain sensitive customer information. Vertex AI Gemini API provides the generative capability, Cloud DLP integration (via the DLP API or Vertex AI's built-in DLP integration) inspects and de-identifies sensitive data in prompts and responses, IAM roles enforce least-privilege access to the model and data, and VPC Service Controls create a security perimeter that prevents data exfiltration. Together these address generation, data protection, access control, and network isolation — the complete combination.

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 12

A marketing team wants to generate multiple versions of ad copy for A/B testing. They need consistent brand tone across all outputs. Which prompt engineering technique is most effective?

Accepted Answer

Include few-shot examples of previous ad copy that match the desired tone. Providing few-shot examples (sample outputs with the desired tone) is the most direct way to enforce consistency. Zero-shot gives no guidance; system instructions help but are less effective than examples for tone consistency. Temperature settings control randomness.

Answer

Use a zero-shot prompt with a detailed description of the brand voice

Answer

Use a system instruction that says 'Be consistent'

Answer

Set the temperature to 0.0 to eliminate randomness

Question 13

Which TWO of the following are capabilities of Vertex AI Model Garden? (Choose 2)

Accepted Answer

Access to a curated set of foundation models like PaLM and Gemini.. Option D is correct because Vertex AI Model Garden provides access to a curated catalog of foundation models, including Google's PaLM and Gemini models, which users can discover and use directly. Option E is correct because Model Garden allows users to fine-tune and deploy foundation models, supporting customization and serving within Vertex AI. Option A is incorrect because generating code snippets is a generative AI application capability, not a core capability of Model Garden itself. Option B is incorrect because text-to-image generation is a specific model use case, not a defining capability of Model Garden. Option C is incorrect because deploying custom container images is a Vertex AI Prediction feature, not a Model Garden capability.

Answer

Generate code snippets for common programming tasks.

Answer

Ability to generate images from text descriptions.

Answer

Deploy custom container images for model serving.

Question 14

A healthcare startup needs to deploy an AI model that processes protected health information (PHI). They require a HIPAA BAA with the cloud provider. Which Google Cloud AI service can meet this compliance requirement?

Accepted Answer

Vertex AI. Vertex AI is Google Cloud's enterprise ML platform covered under the Google Cloud HIPAA Business Associate Agreement (BAA), making it suitable for workloads that process protected health information. Customers can request a BAA through their Google Cloud account, and Vertex AI's training, prediction, and pipeline services fall under that coverage. This makes it the compliant choice for deploying an AI model handling PHI.

Answer

Colab

Answer

Gemini API via Google AI Studio

Answer

Cloud Vision API

Question 15

A hospital deploys a generative AI model to assist in diagnosing rare diseases. The model sometimes suggests treatments that are not widely accepted. To align with Google's AI Principle of being accountable to people, what should the hospital implement?

Accepted Answer

Implement a human override mechanism that allows clinicians to reject AI suggestions. Google's AI Principle of being accountable to people requires that AI systems remain under human control, especially in high-stakes domains like healthcare. A human override mechanism ensures that clinicians can reject AI suggestions that are not widely accepted, directly addressing the principle's emphasis on human oversight and responsibility for outcomes.

Answer

Provide confidence scores for each suggestion to help clinicians assess reliability

Answer

Limit the model to only suggest treatments that appear in a predefined list

Answer

Conduct a retrospective audit of all AI suggestions monthly