Generative AI Leader Practice Test 41 — 15 Questions

Question 1

A company is adopting generative AI for customer support. Which TWO strategies should they implement to manage risks related to brand reputation?

Accepted Answer

Establish a human-in-the-loop escalation process for sensitive interactions.. Option A is correct because a human-in-the-loop escalation process ensures that sensitive or high-stakes customer interactions are reviewed by a person before or during resolution, which directly protects brand reputation by preventing an AI from delivering inappropriate, harmful, or incorrect responses in delicate situations. Option C is correct because automated monitoring for toxic or off-brand language allows the company to detect and remediate reputation-damaging outputs in real time or near real time, maintaining consistent brand voice and catching harmful content that could erode customer trust. Option B is not a sufficient risk-management strategy because a disclaimer only shifts liability and does not prevent reputational harm from bad AI outputs. Option D is incorrect because deploying a model without content filters increases the risk of toxic, biased, or off-brand responses, which is the opposite of reputation risk management. Option E is incorrect because disabling the AI entirely avoids risk only by eliminating the business benefit, which is not a viable strategy for a company adopting generative AI for customer support.

Answer

Publish a disclaimer that the AI may make mistakes.

Answer

Deploy the model without any content filters to maximize helpfulness.

Answer

Disable customer support AI entirely to avoid any risk.

Question 2

A company wants to integrate generative AI into their existing CRM workflow to draft personalized email responses. They have limited engineering resources. Which two approaches should they consider? (Choose TWO)

Accepted Answer

Use Vertex AI API with a low-code integration platform (e.g., Apigee). Option A is correct because using the Vertex AI API through a low-code integration platform like Apigee lets the team call a managed generative model without building or hosting infrastructure, and Apigee handles API management, security, and CRM connectivity with minimal engineering effort. Option C is correct because the Gemini API can be invoked from Google Apps Script, which is a lightweight, serverless scripting environment that can add a custom menu directly into Google Workspace/CRM interfaces, requiring little to no dedicated backend engineering. Option B is not appropriate here because fine-tuning on historical email data demands significant data preparation, training pipelines, and MLOps effort, which conflicts with the limited engineering resources constraint. Option D is not appropriate because deploying a dedicated GPU cluster for inference introduces heavy infrastructure and operational overhead that the company cannot support with limited engineering staff. Option E is not appropriate because building a custom web UI from scratch is a large front-end development effort and unnecessary when low-code or script-based integrations already satisfy the CRM workflow requirement.

Answer

Fine-tune a model on historical email data to ensure brand voice

Answer

Deploy a dedicated GPU cluster for inference

Answer

Build a custom web UI for the assistant from scratch

Question 3

A financial analyst uses generative AI to summarize earnings reports. The summaries vary in style. Which THREE methods can improve consistency? (Choose three.)

Accepted Answer

Set temperature to 0.2. Setting temperature to 0.2 reduces randomness in token sampling, making the model more deterministic and less likely to produce stylistic variations. Lower temperatures (e.g., 0.1–0.3) narrow the probability distribution, forcing the model to select the most likely next token, which directly improves consistency across multiple summaries.

Answer

Increase max output tokens

Answer

Enable citation mode

Question 4

A team is deploying a large language model for legal document summarization. They find the model occasionally omits critical legal clauses. Which improvement technique would be most effective?

Accepted Answer

Design a prompt that explicitly lists required sections. The most effective technique is to design a prompt that explicitly lists the required sections. This is a form of prompt engineering that directly addresses the omission issue by instructing the model to include all specified clauses, leveraging the model's instruction-following capability. It is immediate, low-cost, and does not require retraining or altering sampling parameters. By enumerating the sections, the model is guided to cover each one, reducing the chance of missing critical content.

Answer

Increase the top_p value to 1.0

Answer

Fine-tune the model on legal summaries

Answer

Lower the temperature to 0.1

Question 5

A marketing team wants to use Vertex AI to generate ad copy. They need the model to follow a specific tone and style. What is the best approach?

Accepted Answer

Provide few-shot examples in the prompt and adjust temperature. Few-shot prompting with adjusted temperature is the best approach because it directly controls the model's output style and tone without requiring additional infrastructure or training. By providing a few examples of desired ad copy in the prompt, the model learns the specific tone and style through in-context learning, while temperature tuning (e.g., 0.2 for deterministic output) ensures consistency. This is the most efficient and cost-effective method for immediate, controllable generation.

Answer

Use Vertex AI Grounding to retrieve style guides

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Fine-tune the model on a dataset of past ad copy

Answer

Enable safety filters to enforce brand guidelines

Question 6

A healthcare startup is using Vertex AI Imagen to generate synthetic medical images for training a diagnostic model. The images must comply with HIPAA regulations and cannot contain any real patient data. The team fine-tuned Imagen on a dataset of de-identified medical scans. However, during testing, they notice that some generated images closely resemble specific patients from the original dataset, even though the dataset was de-identified. They suspect that the model memorized some training examples. The team needs to address this issue without losing image quality. They have access to the original training data and Vertex AI tools. What action should they take?

Accepted Answer

Re-tune the model using differential privacy (DP-SGD) to prevent memorization of individual examples.. Differential privacy (DP-SGD) during fine-tuning adds calibrated noise to the gradient updates, which mathematically bounds the model's ability to memorize any single training example. This directly addresses the memorization issue while preserving the utility of the generated images, as the noise is carefully controlled to maintain overall image quality. Vertex AI supports DP-SGD through its custom training infrastructure, making it a practical choice for HIPAA-compliant medical imaging.

Answer

Use a post-processing step to blur or distort generated images.

Answer

Increase the size of the training dataset by adding more synthetic images.

Answer

Apply stricter output safety filters to block images that look like any known patient.

Question 7

A company is developing a generative AI application that will be used by customers in multiple countries, including those with strict data residency laws. How should they approach data governance?

Accepted Answer

Use data residency controls to keep data in specified regions. Data residency controls, such as those provided by Google Cloud Organization Policies, Assured Workloads, or Cloud Storage location constraints, allow the company to enforce that data is stored and processed only within specified geographic regions. This directly addresses strict data residency laws by preventing data from leaving the jurisdiction, which is a fundamental requirement for compliance with regulations like GDPR or Brazil's LGPD. Unlike workarounds, this approach provides native, auditable enforcement at the infrastructure level.

Answer

Store all data in a single central data center to simplify management

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Use a VPN to route data through compliant regions

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Anonymize all data before processing to avoid residency issues

Question 8

A developer wants to generate high-quality images from text descriptions using Google Cloud. Which service should they use?

Accepted Answer

Imagen. Imagen is Google Cloud's text-to-image diffusion model, specifically designed to generate high-quality images from natural language descriptions. It leverages deep learning to produce photorealistic outputs, making it the correct choice for this use case.

Answer

Chirp

Answer

Codey

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Gemini

Question 9

A company is required by the EU AI Act to ensure high-risk AI systems are transparent and auditable. They are using a proprietary model from a vendor. Which step is CRITICAL?

Accepted Answer

Ask the vendor to provide a Model Card and datasheets for the training data. Under the EU AI Act, high-risk AI systems must be transparent and auditable, which requires documentation of the model's intended purpose, training data characteristics, performance metrics, and limitations. A vendor-provided Model Card and datasheets for the training data are the standard artifacts that satisfy this transparency and auditability obligation for a proprietary model. Without them, the deploying company cannot demonstrate compliance or perform meaningful risk assessment.

Answer

Implement custom safety filters on the model outputs

Answer

Use a larger context window to capture all interactions

Answer

Train an internal model from scratch to replace the vendor model

Question 10

A team wants to improve the factual accuracy of their chatbot responses regarding internal company policies. What is the most effective approach?

Accepted Answer

Use RAG with Vertex AI Search indexing the policies. RAG with Vertex AI Search is the most effective approach because it retrieves relevant, up-to-date policy documents from a curated index and injects them into the prompt context at inference time, grounding the chatbot's responses in authoritative sources without modifying the underlying model. This ensures factual accuracy for dynamic or evolving policies, as the model can reference the exact text rather than relying on static training data.

Answer

Use few-shot prompting with example Q&A pairs

Answer

Increase the model's maximum tokens

Answer

Fine-tune the model on policy documents

Question 11

Which Google AI milestone introduced the Transformer architecture that underpins modern LLMs?

Accepted Answer

Transformer paper. The Transformer architecture, which is the foundational technology behind modern large language models (LLMs) like GPT and BERT, was introduced in the 2017 paper 'Attention Is All You Need' by Vaswani et al. This paper proposed the self-attention mechanism and the encoder-decoder structure that replaced recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, enabling parallelized training and superior handling of long-range dependencies in sequence data.

Answer

AlphaGo

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AlphaFold

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BERT

Question 12

A developer is using the Gemini API to generate text summaries. They want to control the creativity and diversity of the output. Which THREE parameters can they adjust?

Accepted Answer

Top-p. Temperature (E) is correct because it scales the logits before sampling, so lower values make the output more deterministic and higher values increase randomness and creativity. Top-p (B), or nucleus sampling, is correct because it restricts sampling to the smallest set of tokens whose cumulative probability exceeds p, directly controlling diversity. Top-k (D) is correct because it limits sampling to the k most likely tokens, which also tunes creativity and variety. Context window (A) only defines the maximum number of tokens the model can consider, and embedding dimension (C) is a fixed property of the embedding model, so neither controls generation creativity.

Answer

Context window

Answer

Embedding dimension

Question 13

Which THREE benefits does Vertex AI Agent Builder provide over building a custom conversational agent from scratch?

Accepted Answer

Automatic scaling and load balancing. Option A is correct because Vertex AI Agent Builder is a managed service that automatically handles scaling and load balancing of the agent infrastructure, removing the need to provision or tune servers yourself. Option B is correct because it provides pre-built connectors and integrations for grounding responses on enterprise data sources such as Vertex AI Search, BigQuery, and other Google Cloud data stores, which would otherwise require custom retrieval pipelines. Option D is correct because it includes built-in safety filters and guardrails (e.g., responsible AI controls, content moderation, and policy enforcement) that a from-scratch agent would need to implement manually. Option C is not correct because Agent Builder abstracts the underlying model and does not give full control over the ML model architecture, which is actually a limitation rather than a benefit. Option E is not correct because Agent Builder does not guarantee lower inference latency; latency depends on the selected model, region, and workload, and no such guarantee is offered.

Answer

Full control over the underlying ML model architecture

Answer

Guaranteed lower inference latency

Question 14

Which TWO strategies are effective for reducing latency in a generative AI chat application deployed on Vertex AI? (Select 2)

Accepted Answer

Use streaming responses. Streaming responses reduce perceived latency by sending tokens to the client as they are generated, rather than waiting for the full response. This leverages server-sent events (SSE) or chunked transfer encoding to deliver partial results immediately, improving user experience in chat applications.

Answer

Deploy on TPU instead of GPU

Answer

Increase the max output tokens

Answer

Use larger batch sizes

Question 15

Which statement best describes the difference between the Gemini Flash and Gemini Pro models on Vertex AI?

Accepted Answer

Gemini Flash is optimized for speed and cost, while Gemini Pro provides higher quality for complex tasks. Gemini Flash is specifically designed for low-latency, high-throughput, and cost-efficient inference, making it ideal for high-volume, simpler tasks. In contrast, Gemini Pro is a larger, more capable model that delivers superior quality and reasoning for complex, multi-step tasks, though at higher latency and cost. This distinction is fundamental to the Gemini model family on Vertex AI, where Flash serves as the lightweight, fast option and Pro as the premium, high-quality option.

Answer

Gemini Flash is a distilled version of Gemini Pro that requires fine‑tuning before use

Answer

Gemini Pro is deployed on Google’s TPU v5p chips, while Flash uses TPU v4

Answer

Gemini Flash is only available for image inputs, while Gemini Pro handles text